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In patients with peripheral pulmonary lesions (PPLs), nondiagnostic bronchoscopy results are not uncommon. The conventional approach to estimate the probability of cancer (pCA) after bronchoscopy relies on dichotomous test assumptions, using prevalence, sensitivity, and specificity to determine negative predictive value. However, bronchoscopy is a multidisease test, raising concerns about the accuracy of dichotomous methods. By how much does calculating pCA using a dichotomous approach (pCA dichotomous) underestimate the true pCA when applied to multidisease tests like bronchoscopy for the diagnosis of PPL? In this meta-analysis of cohort studies involving radial endobronchial ultrasound for PPL, Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed, constructing 2 × 2 contingency tables for calculating pCA dichotomous. For the multidisease test approach, 3 × 3 contingency tables for calculating probability of malignancy for a test that can have different categories of results and can diagnose multiple diseases (pCA multidisease) using the likelihood ratio (LR) method for nondiagnostic results (LR(T 0)) was used. Observed malignancy rates in patients with nondiagnostic results were compared with pCA dichotomous and pCA multidisease. In 46 studies (7,506 patients), malignancy was the underlying diagnosis in 76% of cases, another specific disease in 13% of cases, and nonspecific fibrosis or scar in 10% of cases. The percentage of patients with nondiagnostic results who had malignancy matched pCA multidisease across all studies. In contrast, pCA dichotomous consistently underestimated cancer risk (median difference, 0.12; interquartile range, 0.06-0.23), particularly in studies with a higher prevalence of nonmalignant disease. The pooled LR(T 0) was 0.46 (95% CI, 0.40-0.52; I 2 = 76%; P <.001) and correlated with the prevalence of nonmalignant diseases (P =.001). Conventional dichotomous methods for estimating pCA after nondiagnostic bronchoscopies underestimate the likelihood of malignancy. Physicians should opt for the multidisease test approach when interpreting bronchoscopy results. [ABSTRACT FROM AUTHOR]

Otosclerosis is a common cause of hearing loss, yet diagnosing it remains a challenge. Wideband Tympanometry Absorbance (WTA) has been proposed as a potential tool for improving diagnostic accuracy. The aim of study was to investigate the diagnostic value of WTA in diagnosing otosclerosis by comparing its results in patients with clinically diagnosed otosclerosis and a normal control group. This prospective study was conducted in Mashhad, Iran, from 2022 to 2023, involving 64 participants including otosclerosis and control. Inclusion criteria for the otosclerosis group included a clinical diagnosis of otosclerosis confirmed by audiometric tests and conventional tympanometry, and eligibility for surgical intervention. The control group consisted of individuals with normal clinical audiometric and tympanometric results. Both groups underwent conventional and WTA tympanometric assessments. The evaluation of WTA involved 64 participants, divided evenly between otosclerosis a normal ears group. Conventional tympanometry at 226 Hz showed significant differences in compliance between otosclerosis and normal ears (p = 0.02). In contrast, at 1000 Hz did not demonstrate significant differences in compliance (p = 0.2). Also, WTA did not demonstrate significant differences in compliance (p > 0.9). Measurements of gradient and resonance across both 226 and 1000Hz frequencies showed no statistically significant differences. The sensitivity and specificity of WTA, evaluated through compliance, were 72% and 40%, respectively, with an area under the curve of 0.50. The study illustrates that while conventional tympanometry is effective in distinguishing otosclerosis from normal tympanic conditions, WTA shows limited diagnostic efficacy. The moderate sensitivity and specificity of WTA suggest that it should not be relied upon as the sole diagnostic tool. [ABSTRACT FROM AUTHOR]

Background: Ancillary immunohistochemistry testing for p16 loss has been proposed as a diagnostic tool for melanoma, but its accuracy remains uncertain. Methods: A systematic review and meta‐analysis were conducted on 26 studies involving 979 melanomas and 974 nevi. Results: Through bivariate analysis of data across all cut‐off values, the sensitivity and specificity were calculated to be 0.55 (95% confidence interval [CI]: 0.38, 0.70) and 0.85 (95% CI: 0.70, 0.94), respectively. Summary estimates of diagnostic accuracy fell below recommended thresholds for effective tests, but subgroup analysis suggested that p16 loss could aid in diagnosing ambiguous lesions as melanoma in certain scenarios. However, the presence of p16 expression in these contexts does not definitively rule out melanoma. The findings were limited by underpowered exploratory study designs at risk for bias in patient selection and test interpretation. Conclusions: While the use of p16 immunohistochemistry for detecting melanoma is not universally reliable, it may serve as a confirmatory test in differential diagnoses involving common, congenital, acral, Spitz, and deep penetrating nevi. Nevertheless, further studies are needed to validate its utility. Until then, the application of p16 immunohistochemistry in melanoma diagnosis should be regarded as experimental. [ABSTRACT FROM AUTHOR]

Key summary points: Aim: To compare the performance of different case-finding tools (SARC-F, SARC-CalF, calf circumference (CC), and BMI-adjusted CC) for sarcopenia in community-dwelling older adults. Findings: CC was the best tool for sarcopenia case-finding. The best performance was found for CC cut-off point of ≤ 34 cm in women and ≤ 36 cm in men. Message: In clinical practice, measurement of CC is a simple, inexpensive, and reliable tool to identify individuals at risk for sarcopenia for further assessment. Purpose: We compared the performance of SARC-F, SARC-CalF, calf circumference (CC), and body mass index (BMI)-adjusted CC for sarcopenia case-finding in community-dwelling older adults. Methods: Data of Finnish participants (women/men n = 192/36, mean age (SD) of 76.9 (4.8) years) recruited in the SPRINTT trial (NCT02582138) were used. Sarcopenia was determined as a combination of low muscle function (chair-stand-test) and low appendicular lean mass (ALM) detected by whole-body dual-energy X-ray absorptiometry. Associations of case-finding tools with sarcopenia were analysed using ROC curves and logistic regression. Results: The rates of probable and confirmed sarcopenia were 95% and 18% in women and 94% and 36% in men, respectively. Performance of CC for sarcopenia (women AUC 0.85 [95% CI 0.78–0.92]/ men 0.85 [95% CI 0.71–1.0]) was superior to that of other tools; (AUC in women/men for SARC-F was 0.57/0.50, for SARC-CalF 0.76/0.79, and for BMI-adjusted CC 0.68/0.66). The best performance was found for a CC cut-off point of ≤ 34 cm in women with sensitivity/specificity 82.4/75.3% and ≤ 36 cm in men with sensitivity/specificity 76.9/87.0%. For each cm decrease in CC, adjusted for age and BMI, there was a 30% increase in the odds of sarcopenia in women (OR 1.30, 95% CI 1.09─1.56). Although there was a similar pattern in men, the results did not reach statistical significance (OR 1.34, 95% CI 0.84- 2.14). Conclusions: CC was superior to other tools for sarcopenia case-finding. The best performance was found for a CC cut-off point of ≤ 34 cm in women and ≤ 36 cm in men. [ABSTRACT FROM AUTHOR]

An alarming increase in hypertension is a hazard to global health that poses severe implications for the body’s vital organs. To prevent serious repercussions, hypertension should be monitored continuously for early detection. It is well known that physiological signals, such as the photoplethysmogram (PPG) and electrocardiogram (ECG), carry essential information about the vitals of the human body. Considering this, numerous machine learning-based models based on ECG-PPG have been proposed for monitoring hypertension; however, such models are “non-explainable” and lack clinical interpretation. This work proposes a formal method-based runtime verification approach for hypertension monitoring using ECG and PPG sensing, which is explainable. The pulse arrival time (PAT) feature extracted using both signals is employed to implement a decision tree to infer hypertension patterns/policies defined in PAT, based on which a runtime monitor is synthesized to classify hypertension. Using the MIMIC II dataset, the proposed scheme’s performance is assessed, and the accuracy, sensitivity, and specificity are determined to be 95.7%, 93.9%, and 97.6%, respectively. [ABSTRACT FROM AUTHOR]

Background: Multiple differentials exist for pediatric liver tumors under 2 years. Accurate imaging diagnosis may obviate the need for tissue sampling in most cases. Objective: To evaluate the imaging features and diagnostic accuracy of computed tomography (CT) in liver tumors in children under 2 years. Methods: Eighty-eight children under 2 years with treatment naive liver neoplasms and baseline contrast-enhanced CT were included in this institutional review board approved retrospective study. Two blinded onco-radiologists assessed these tumors in consensus. Findings assessed included enhancement pattern, lobulated appearance, cystic change, calcifications, central scar-like appearance, and metastases. The radiologists classified the lesion as hepatoblastoma, infantile hemangioma, mesenchymal hamartoma, rhabdoid tumor, or indeterminate, first based purely on imaging and then after alpha-fetoprotein (AFP) correlation. Multivariate analysis and methods of comparing means and frequencies were used for statistical analysis wherever applicable. Diagnostic accuracy, sensitivity, and positive predictive values were analyzed. Results: The mean age of the sample was 11.4 months (95% CI, 10.9–11.8) with 50/88 (57%) boys. The study included 72 hepatoblastomas, 6 hemangiomas, 4 mesenchymal hamartomas, and 6 rhabdoid tumors. Presence of calcifications, multilobular pattern of arterial enhancement, lobulated morphology, and central scar-like appearance was significantly associated with hepatoblastomas (P-value < 0.05). Fourteen out of eighty-eight lesions were called indeterminate based on imaging alone; six lesions remained indeterminate after AFP correlation. Pure radiology-based diagnostic accuracy was 81.8% (95% CI, 72.2–89.2%), which increased to 92.1% (95% CI, 84.3–96.7%) (P-value > 0.05) after AFP correlation, with one hepatoblastoma misdiagnosed as a rhabdoid tumor. If indeterminate lesions were excluded for biopsy, the accuracy would be 98.8% (95% CI, 93.4–99.9%). Conclusion: CT had high accuracy for diagnosing liver neoplasms in the under 2-year age population after AFP correlation. Certain imaging features were significantly associated with the diagnosis of hepatoblastoma. A policy of biopsying only indeterminate lesions after CT and AFP correlation would avoid sampling in the majority of patients. [ABSTRACT FROM AUTHOR]

Background/Aim. Serum tumor markers (TMs) are commonly combined with imaging examinations to differentiate benign and malignant breast nodules (BNs), but there are still limitations. The aim of the study was to determine the value of real-time tissue elastography (RTE) diffusion quantitative analysis combined with serum TMs for the differential diagnosis of benign and malignant BNs. Methods. A total of 149 patients with BNs were included in this study. They were assigned to the benign BN group (n = 87) and malignant BN group (n = 62). All patients were examined using RTE diffusion quantitative analysis. Venous blood was collected to detect the levels of TMs carcinoembryonic antigen-CAE, cancer antigen (CA) 153, and CA 199. The value of RTE diffusion quantitative analysis parameters, TMs, and their combination for the differentiation of benign and malignant BNs was analyzed using the receiver operating characteristic-ROC curve. Results. Among all the above indicators, the area ratio of the blue region (AREA%) had the highest differential value, with an area under the curve (AUC) of 0.916 [95% confidence interval (CI): 0.812-0.967], while sensitivity and specificity were 88.90% and 86.79%, respectively (p < 0.05). Compared to RTE diffusion quantitative analysis parameters or TMs alone, the combination of the two showed the highest value for the differentiation of benign and malignant BNs, with an AUC of 0.957 (95% CI: 0.834-0.982), while sensitivity and specificity were 95.50% and 94.33%, respectively (p < 0.05). Conclusion. RTE diffusion quantitative analysis combined with TMs has a high value for the differentiation of benign and malignant BNs. [ABSTRACT FROM AUTHOR]

Background: Identifying language disorders earlier can help children receive the support needed to improve developmental outcomes and quality of life. Despite the prevalence and impacts of persistent language disorder, there are surprisingly no robust predictor tools available. This makes it difficult for researchers to recruit young children into early intervention trials, which in turn impedes advances in providing effective early interventions to children who need it. Aims: To validate externally a predictor set of six variables previously identified to be predictive of language at 11 years of age, using data from the Longitudinal Study of Australian Children (LSAC) birth cohort. Also, to examine whether additional LSAC variables arose as predictive of language outcome. Methods & Procedures: A total of 5107 children were recruited to LSAC with developmental measures collected from 0 to 3 years. At 11–12 years, children completed the Clinical Evaluation of Language Fundamentals, 4th Edition, Recalling Sentences subtest. We used SuperLearner to estimate the accuracy of six previously identified parent‐reported variables from ages 2–3 years in predicting low language (sentence recall score ≥ 1.5 SD below the mean) at 11–12 years. Random forests were used to identify any additional variables predictive of language outcome. Outcomes & Results: Complete data were available for 523 participants (52.20% girls), 27 (5.16%) of whom had a low language score. The six predictors yielded fair accuracy: 78% sensitivity (95% confidence interval (CI) = [58, 91]) and 71% specificity (95% CI = [67, 75]). These predictors relate to sentence complexity, vocabulary and behaviour. The random forests analysis identified similar predictors. Conclusions & Implications: We identified an ultra‐short set of variables that predicts 11–12‐year language outcome with 'fair' accuracy. In one of few replication studies of this scale in the field, these methods have now been conducted across two population‐based cohorts, with consistent results. An imminent practical implication of these findings is using these predictors to aid recruitment into early language intervention studies. Future research can continue to refine the accuracy of early predictors to work towards earlier identification in a clinical context. WHAT THIS PAPER ADDS: What is already known on the subject: There are no robust predictor sets of child language disorder despite its prevalence and far‐reaching impacts. A previous study identified six variables collected at age 2–3 years that predicted 11–12‐year language with 75% sensitivity and 81% specificity, which warranted replication in a separate cohort. What this study adds to the existing knowledge: We used machine learning methods to identify a set of six questions asked at age 2–3 years with ≥ 71% sensitivity and specificity for predicting low language outcome at 11–12 years, now showing consistent results across two large‐scale population‐based cohort studies. What are the potential or clinical implications of this work?: This predictor set is more accurate than existing feasible methods and can be translated into a low‐resource and time‐efficient recruitment tool for early language intervention studies, leading to improved clinical service provision for young children likely to have persisting language difficulties. [ABSTRACT FROM AUTHOR]

Introduction: Objective of this study was to assess the diagnostic efficacy of the most commonly used radiological evaluation in form of contrast enhanced computed tomography of abdomen with adrenal protocol, basic functional evaluation and surgical outcomes of primary adrenal masses. Material and methods: We have retrospectively analysed the institutional records of 108, patients admitted from August 2017 to September 2023, who had underwent surgical intervention for their adrenal mass after thorough evaluation and stabilization. Results: Flank pain was the most common symptoms in 44 (40.74%) patients. Non-functional adrenal adenoma was found in 36 (33.33%) patients. Pheochromocytoma was the most the common functional adrenal mass found in 24 (22.22%) patients. CECT abdomen had suggested features of malignancy in 16(14.81%) patients. In final histopathological report 26(24.07%) patients had pheochromocytoma and 12 (11.11%) patients had adrenocortical carcinoma. CECT abdomen had sensitivity of 75%, specificity of 95.6%. Plasma free metanephrines and normetanephrine analysis had shown sensitivity of 90%, specificity of 92.86%, aldosterone to renin ratio had sensitivity of 92%, specificity of 100%. Sensitivity and specificity of the low-dose dexamethasone suppression test and plasma dehydroepiandrosterone was 100% in our study. Eighty patients (74.07%) were operated with laparoscopic adrenalectomy, 20 (18.52%) patients with open adrenalectomy. Eight patients (7.41%) were converted from laparoscopic to open. Laparoscopic approach had significantly lesser mean operating time, lesser blood loss, lesser hospitalization and lesser post-operative complications. Conclusion: Radiological analysis and functional analysis has shown good sensitivity and high specificity. Laparoscopic approach has advantage of lesser operative time, lesser hospitalization and lesser post-operative complications. [ABSTRACT FROM AUTHOR]

Background and Purpose: The definitive diagnosis of gastroesophageal reflux disease (GERD) often requires invasive investigations like upper gastrointestinal endoscopy or reflux monitoring. We aimed to explore the relationship between salivary pepsin and GERD and its value as a non‐invasive diagnostic tool. Methods: Databases (PubMed, Web of Science, Cochran Library, and EMBASE) were searched from their inception to January 22, 2024 to explore the correlation of salivary pepsin with GERD. The meta‐analysis data retrieved were summarized, including the salivary pepsin concentration, sensitivity of diagnosis (SEN), specificity of diagnosis (SPE), negative likelihood ratio, positive likelihood ratio, diagnostic odds ratio, and receiver operating characteristic (ROC) curve. Results: The meta‐analysis comparing salivary pepsin concentration in two groups (proven GERD and non‐GERD) with 18 studies revealed that the proven GERD group had higher salivary pepsin concentration than the non‐GERD group (SMD = 1.74 [95% CI 1.14–2.34]). The meta‐analysis of salivary pepsin diagnostic value for proven GERD incorporated 23 studies. The results showed pooled SEN (0.73 [95% CI 0.66–0.80]), SPE (0.72 [95% CI 0.65–0.78]), positive likelihood ratio (2.61 [95% CI 2.02–3.39]), negative likelihood ratio (0.37 [95% CI 0.28–0.50]), diagnostic odds ratio (7.03 [95% CI 4.24–11.66]) and area under the SROC curve (0.79 [95% CI 0.75–0.82]). Conclusion: GERD patients presented a higher salivary pepsin concentration. Salivary pepsin is both sensitive and specific in identifying GERD, making it a promising non‐invasive marker for diagnosis. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to determine and compare the capability of several B-mode ultrasound (US) and shear wave elastography (SWE) metrics to differentiate subjects with chronic non-specific neck pain from asymptomatic subjects. Methods: A diagnostic accuracy study recruiting a sample of patients with chronic neck pain and asymptomatic controls was conducted. Data collection included sociodemographic information (i.e., gender, age, height, weight and body mass index), clinical information (pain intensity assessed using the Visual Analogue Scale and pain-related disability using the Neck Disability Index) and B-mode ultrasound and shear wave elastography features of the cervical multifidus muscle (cross-sectional area, perimeter, mean echo intensity, fat infiltration, shear wave speed and Young's modulus). After analyzing between-group differences for left/right sides, cases and controls, and males and females, the area under the receiver operating characteristic (ROC) curve, the optimal cut-off point, the sensitivity, the specificity, the positive likelihood ratio (LR) and negative LR for each metric were calculated. A total of 316 individuals were recruited in this study (n = 174 cases with neck pain and n = 142 asymptomatic controls). Results: No significant differences (p > 0.05) were found between cases and controls for most variables, except for fatty infiltration, which was significantly higher in chronic neck pain cases (p < 0.001). Gender differences were significant across all US and SWE metrics (all, p < 0.001 except p = 0.015 for fatty infiltrates). A slight asymmetry was observed between the left and right sides for area (p = 0.038). No significant interactions between group, gender and side (all metrics, p > 0.008) were identified. Fatty infiltration was the most effective discriminator, with a ROC value of 0.723, indicating acceptable discrimination. The optimal cut-off point for fatty infiltration was 25.77, with a moderate balance between sensitivity (59.8%) and specificity (20.5%). However, its positive likelihood ratio (LR) of 0.75 suggests limited usefulness in confirming the condition. Conclusions: Fatty infiltration was significantly higher in individuals with chronic idiopathic neck pain compared to those without symptoms, while other muscle metrics were similar between both groups. However, since fat infiltration had moderate diagnostic accuracy and the other metrics showed poor discriminatory power, US cannot be used solely to discriminate patients with idiopathic neck pain. [ABSTRACT FROM AUTHOR]

Objective: Diagnosis of spondylodiscitis is usually possible radiographically with magnetic resonance imaging (MRI). However, the first imaging method evaluated in daily practice is conventional radiography. The aim of the study was to determine the sensitivity and specificity of detecting infectious or rheumatological spondylodiscitis in the lumbar region with conventional radiography by rheumatologists. Methods: Among 102 patients with spondylodiscitis on lumbosacral (LS) MRI, 23 patients who also underwent simultaneous conventional LS radiography were included. TThe control group consisted of 52 outpatients with no evidence of spondylodiscitis on LS MRI. Eleven rheumatologists blindly evaluated conventional LS radiographs. Sensitivity, specificity, positive, and negative predictive values of LS conventional radiography were calculated. Results: While the cause was infection in 8/23 (34.7%) of spondylodiscitis patients, it was spondyloarthritis in 15/23 (65.2%). According to LS MRI findings, 23 patients had spondylodiscitis in a total of 31 vertebral units. When we evaluated the detection of spondylodiscitis according to the vertebral unit level, it was mostly at one level [14 (60.8%)], primarily at the L4-5 vertebral unit [13 (56.5%)]. The sensitivity of detecting LS spondylodiscitis on conventional radiography was found to be 52% (30-65), and the specificity was 86% (59-94). While the median (minimum-maximum) sensitivity was 75.0 (50.0-87.5) in patients with infectious spondylodiscitis, it was 46.6 (13.3-76.9) in patients with spondylodiscitis due to spondyloarthritis. Conclusion: Clinicians can miss spondylodiscitis. Although it is evaluated with conventional radiography in the first stage in the presence of appropriate clinical findings, the clinician should be careful and consider more advanced approaches.

Objective(s): Although the psychometric properties of the Persian version of the short form of the Female Sexual Function Scale (FSFI-6) was studied, the sensitivity and specificity of the Persian version of this instrument have not been investigated. The present study aimed to assess the sensitivity, specificity and clinical cut-off point of the FSFI-6 in Iranian women. Methods:This descriptive cross-sectional study was conducted on 165 women with and 131 women without sexual dysfunction. In addition to a demographic questionnaire, the Female Sexual Function Index (FSFI-19) questionnaire was administered. Using the Receiver-Operating Characteristic curve (ROC) analysis and indicating cut-off point, sensitivity and specificity were determined Results: Based on the short form of the original instrument, which included questions 4, 2, 7, 11, 16, and 17 from the longer version, the cut-off point for the FSFI-6 was 18.5 and the sensitivity and specificity were 61% and 52%, respectively. Considering that the calculated sensitivity and specificity of the index was relatively low and had a significant difference compared to the reported sensitivity and specificity for this instrument in the literature. Therefore, questions 5, 8, 12, and 18 were replaced with questions 4, 7, 11, and 17 and the analysis was repeated. Based on the replaced questions, the cut-off point was 20.5 and the sensitivity and specificity of the tool was 90%. Conclusion: The findings suggest that the Persian version of FSFI-6 with questions 2, 5, 8, 12, 16, and 18 has a better performance for diagnosing sexual dysfunction in Iranian women.

Objective To investigate the value of real-time tissue elastography (RTE) in evaluating donor liver quality and postoperative early allograft dysfunction (EAD) in liver transplantation patients. Methods A total of 58 patients who received liver transplantation in Organ Transplantation Center of our hospital from October 2020 to January 2022 were enrolled, and according to the recovery of liver function in the early stage after surgery, the patients were divided into EAD group and non-EAD group. The two groups were compared in terms of the indicators such as the routine ultrasound examination and RTE measurement of donor liver before liver transplantation, liver function and coagulation function of recipients before and after liver transplantation, and preoperative Model for End-Stage Liver Disease (MELD) score. The logistic regression analysis was performed for the indicators with significant differences, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the ROC curve (AUC) and assess the performance of related factors. Results Compared with the non-EAD group, the EAD group had significantly higher liver fibrosis (LF) index of donor liver and MELD score of recipients (t=4.822, 2.902,P1.645 was a risk factor for EAD after liver transplantation, and the ROC curve analysis showed that donor liver LF index had an AUC of 0.840 in predicting EAD after surgery, with a sensitivity of 82.4%, a specificity of 73.2%, and a cut-off value of 1.645. Conclusion High LF index of donor liver before liver transplantation is a risk factor for EAD in recipients after liver transplantation, and RTE measurement for LF index of donor liver before li-ver transplantation has a certain value in assessing the quality of donor liver and predicting the onset of EAD.

Objectives: [18F]Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is a non-invasive imaging modality used in the differential diagnosis of splenic lesions, although ideal parameters and thresholds remain unclear. The present study evaluated the ability of [18F]FDG PET/CT, including its visual and quantitative parameters, to differentiate between benign and malignant splenic lesions. Methods: Patients who underwent [18F]FDG PET/CT following the detection of splenic lesions on contrast-enhanced CT were retrospectively analysed. Visual parameters assessed on [18F]FDG PET/CT included whole spleen uptake intensity, lesion multiplicity, and lesion uptake, and quantitative parameters included maximum standardised uptake value (SUVmax), lesion-to-background ratio (LBR), metabolic tumour volume (MTV), total lesion glycolysis (TLG), and lesion size. Parameters differentiating between benign and malignant lesions were evaluated by Pearson's chi-square test, Mann–Whitney U-test, and receiver operating characteristics (ROC) curve analysis. Results: Splenic lesion uptake (p = 0.001) was the only visual parameter significantly distinguishing between benign and malignant lesions. ROC curve analysis demonstrated that SUVmax had the largest area under the ROC, 0.91 (p < 0.001), with an optimal cut-off > 5.3 having a sensitivity of 90.3% and a specificity of 80.6%. Subgroup analysis of malignant lesions showed that SUVmax (p = 0.013), LBR (p = 0.012), and TLG (p = 0.034) were significantly higher in splenic lymphomas than in splenic metastases. Conclusion: Of the [18F]FDG PET/CT parameters investigated, SUVmax had the highest accuracy in diagnosing malignant splenic lesions and was significantly higher in splenic lymphomas than in splenic metastases. Visual determination of [18F]FDG uptake by splenic lesions may be an easily evaluated parameter. Clinical relevance statement: SUVmax and visual grade of [18F]FDG PET/CT help to differentiate spleen lesions. [18F]FDG PET/CT is useful for discriminating between benign and malignant spleen lesions. Key Points: Many splenic lesions are difficult to diagnose on anatomical imaging, with histopathologic analyses are required. SUVmax of PET/CT provided the diagnostic ability to differentiate between benign and malignant splenic lesions. More than normal spleen uptake can be a convenient parameter to diagnose malignant spleen lesions. [ABSTRACT FROM AUTHOR]

Abstract Background Although clinical guidelines exist for diagnosing abnormal uterine bleeding, there is a significant lack of agreement on the best management strategies for women presenting with symptom, particularly in diagnosing endometrial cancer. This study aimed to develop a preoperative risk model that utilizes demographic factors and transvaginal ultrasonography of the endometrium to assess and predict the risk of malignancy in females with endometrial cancer. Methods In this retrospective study, a logistic regression model was developed to predict endometrial carcinoma using data from 356 postmenopausal women with endometrial lesions and an endometrial thickness (ET) of 5 mm or more. These patients had undergone transvaginal ultrasonography prior to surgery, with findings including 247 benign and 109 malignant cases. The model’s predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis and compared with post-surgical pathological diagnoses. Results Our model incorporates several predictors for endometrial carcinoma, including age, history of hypertension, history of diabetes, body mass index (BMI), duration of vaginal bleeding, endometrial thickness, completeness of the endometrial line, and endometrial vascularization. It demonstrated a strong prediction with an area under the curve (AUC) of 0.905 (95% CI, 0.865–0.945). At the optimal risk threshold of 0.33, the model achieved a sensitivity of 82.18% and a specificity of 92.80%. Conclusions The established model, which integrates ultrasound evaluations with demographic data, provides a specific and sensitive method for assessing and predicting endometrial carcinoma.

Abstract Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen’s κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen’s κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and β errors. For the most frequent cases, we provide example of software freely available on the Internet. Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology. Key points • Sample size is a concept related to precision and statistical power. • It has ethical implications, especially when patients are exposed to risks. • Sample size should always be calculated before starting a study. • This review offers simple, go-to methods for sample size calculations. Graphical Abstract

Background: Although clinical guidelines exist for diagnosing abnormal uterine bleeding, there is a significant lack of agreement on the best management strategies for women presenting with symptom, particularly in diagnosing endometrial cancer. This study aimed to develop a preoperative risk model that utilizes demographic factors and transvaginal ultrasonography of the endometrium to assess and predict the risk of malignancy in females with endometrial cancer. Methods: In this retrospective study, a logistic regression model was developed to predict endometrial carcinoma using data from 356 postmenopausal women with endometrial lesions and an endometrial thickness (ET) of 5 mm or more. These patients had undergone transvaginal ultrasonography prior to surgery, with findings including 247 benign and 109 malignant cases. The model's predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis and compared with post-surgical pathological diagnoses. Results: Our model incorporates several predictors for endometrial carcinoma, including age, history of hypertension, history of diabetes, body mass index (BMI), duration of vaginal bleeding, endometrial thickness, completeness of the endometrial line, and endometrial vascularization. It demonstrated a strong prediction with an area under the curve (AUC) of 0.905 (95% CI, 0.865–0.945). At the optimal risk threshold of 0.33, the model achieved a sensitivity of 82.18% and a specificity of 92.80%. Conclusions: The established model, which integrates ultrasound evaluations with demographic data, provides a specific and sensitive method for assessing and predicting endometrial carcinoma. [ABSTRACT FROM AUTHOR]

Background: Precancerous lesions, conditions associated with exposure to human papillomavirus (HPV), threaten women's health at different stages of life, from adolescence to old age. It has been observed that a low-cost strategy, such as visual inspection with acetic acid (VIA), reveals cervical lesions, while establishing interventions that allow modifying the course of the disease, thus improving women's quality of life. Objective: To determine the validity of VIA in comparison with cervical cytology for the detection of precancerous lesions. Material and methods: Descriptive, observational, crosssectional, prospective, analytical study. The type of sampling was non-probabilistic. The sample consisted of 638 participants. Data were collected with a data collection form and analyzed quantitatively with the SPSS software. The study complied with the provisions of the General Health Law on Research in Mexico and was approved by the local research committee of the Mexican Institute for Social Security (IMSS, according to its initials in Spanish). Results: VIA compared with cervical cytology showed a sensitivity of 75%, a specificity of 98%, a positive predictive value of 47%, a negative predictive value of 99%, a positive post-test probability of 97%, and a concordance of 61%. Conclusion: VIA showed an acceptable sensitivity and can be used as a screening test in conjunction with cytology. [ABSTRACT FROM AUTHOR]

The Australian Scleroderma Interest Group (ASIG) algorithm for screening pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) requires only respiratory function tests and serum N‐terminal pro‐brain natriuretic peptide as first‐tier tests, and is recommended in international guidelines. In this communication, we present the findings of the application of the ASIG screening algorithm to a Singaporean cohort undergoing prospective annual screening for PAH, which shows a high negative predictive value. The ASIG algorithm may offer an alternative to more complex and costly SSc‐PAH screening algorithms. [ABSTRACT FROM AUTHOR]

Background: Self‐reported measures of periodontitis developed for use in population surveillance are increasingly used in causal research. Numerous studies evaluate the validity of these measures against clinical parameters of periodontitis, yet few include validation parameters outside of multivariable models. Individual item validity is necessary to adequately inform use of these measures in causal research. Methods: We used data from the National Health and Nutrition Examination Survey 2011‐2014 in which dentate participants completed full‐mouth periodontal examinations (N = 6966). We evaluated six self‐report questionnaire items related to periodontal disease status against periodontitis case definitions developed by the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC‐AAP). We estimated the sensitivity and specificity of individual items using severe and moderate‐to‐severe periodontitis classifications. We additionally combined items to evaluate the validity of joint measures. Results: Sensitivity was highest when measures were evaluated against severe periodontitis. Self‐rated oral health of fair/poor demonstrated the highest sensitivity for severe (0.60) and moderate‐to‐severe periodontitis (0.48). Specificity was highest when evaluated against moderate‐to‐severe periodontitis, with self‐reported history of tooth mobility as the most specific measure (0.87 for severe disease; 0.92 for moderate‐to‐severe) followed by a history of bone loss (0.88 for severe; 0.91 for moderate‐to‐severe). Combining questions generally improved specificity at the expense of sensitivity. Conclusions: Our findings related to item‐specific validity and the associated clinical profiles facilitate needed considerations for the use of self‐reported measures of periodontitis in causal research. Additionally, item‐specific validity can be used to inform assessments of misclassification bias within such investigations. [ABSTRACT FROM AUTHOR]

OBJECTIVE: A silent health crisis in the United States is the underdiagnosed and undertreated mental health of women related to childbirth. This discussion paper describes the Postpartum Depression Screening Scale (PDSS) with its scoring and interpretation along with its psychometric testing and translations. METHOD: In addition to the studies conducted by the instrument developers, databases were searched for studies where other researchers used the PDSS to assess postpartum depressive symptoms. Studies were included that measured the psychometrics of the scale and studies that reported the translation of the PDSS into other languages. RESULTS: Evidence is presented that the PDSS is a reliable and valid screening scale for use by psychiatric mental health nurses and other health care providers. The scale also has been translated into 14 languages, so it is available to screen non-English speaking mothers. CONCLUSION: Childbirth is one of the most powerful triggers of psychiatric illness in a woman's life. Postpartum depression is a treatable mental health condition, but first, women need to be screened so they can be identified and referrals made. Screening for this devastating mood disorder in new mothers is an essential role of psychiatric mental health nurses and other clinicians so that treatment can be started as early as possible to avoid harmful consequences. [ABSTRACT FROM AUTHOR]

Objective(s): Although the psychometric properties of the Persian version of the short form of the Female Sexual Function Scale (FSFI-6) was studied, the sensitivity and specificity of the Persian version of this instrument have not been investigated. The present study aimed to assess the sensitivity, specificity and clinical cut-off point of the FSFI-6 in Iranian women. Methods: This descriptive cross-sectional study was conducted on 165 women with and 131 women without sexual dysfunction. In addition to a demographic questionnaire, the Female Sexual Function Index (FSFI-19) questionnaire was administered. Using the Receiver-Operating Characteristic curve (ROC) analysis and indicating cut-off point, sensitivity and specificity were determined Results: Based on the short form of the original instrument, which included questions 4, 2, 7, 11, 16, and 17 from the longer version, the cut-off point for the FSFI-6 was 18.5 and the sensitivity and specificity were 61% and 52%, respectively. Considering that the calculated sensitivity and specificity of the index was relatively low and had a significant difference compared to the reported sensitivity and specificity for this instrument in the literature. Therefore, questions 5, 8, 12, and 18 were replaced with questions 4, 7, 11, and 17 and the analysis was repeated. Based on the replaced questions, the cut-off point was 20.5 and the sensitivity and specificity of the tool was 90%. Conclusion: The findings suggest that the Persian version of FSFI-6 with questions 2, 5, 8, 12, 16, and 18 has a better performance for diagnosing sexual dysfunction in Iranian women. [ABSTRACT FROM AUTHOR]

AbstractObjective: The National Institute of Neurological Disorders and Stroke (NINDS) recently revised criteria for Traumatic Encephalopathy Syndrome (TES) (Katz et al.), aiming to improve the specificity of former TES criteria (Montenigro et al.) and adding methods to gauge certainty of underlying Chronic Traumatic Encephalopathy (CTE). This study examined base rates of Montenigro et al. and Katz et al. TES criteria in healthy community-dwelling adults. Method: Participants consisted of healthy adults (n = 835; M = 48.1 ± 18.2 years-old, range = 18–85; 37.1% male; 64.1% White) without known history of neurotrauma or psychiatric or neurological conditions. The former and current TES criteria were operationalized using the NIH Toolbox Cognition, Motor, and Emotion batteries and PROMIS-29. Results: Per Katz et al. criteria, 36.9% had symptoms Suggestive of CTE (i.e. either cognitive impairment or neurobehavioral dysregulation), 4.1% had Possible CTE (i.e. requiring cognitive impairment and two additional criteria), and 0.8% had Probable CTE (i.e. requiring cognitive impairment and three additional criteria). The requirement of cognitive impairment for Possible CTE certainty decreased the base rate of Possible CTE tenfold from Montenigro et al. criteria (40.1%). Conclusion: The Katz et al. criteria were met less frequently by healthy adults than the Montenigro et al. criteria. Requiring cognitive impairment and more supportive TES features when gauging CTE certainty may reduce false-positive diagnoses. This finding supports the role of neuropsychologists in the diagnosis and monitoring of patients in TES research studies. To assess specificity, future research should examine base rates of Katz et al. criteria in other psychiatric and neurological conditions. [ABSTRACT FROM AUTHOR]

AbstractBiomarker enrichment clinical trial designs are versatile tools to assess the treatment effect and increase the efficiency of clinical trials. In this article, we propose a two-stage enrichment clinical trial design with survival outcomes, and consider the situation where the biomarker assay and classification are possibly subject to errors. Specifically, the first stage is a randomized design, stratified by the biomarker appeared status. Depending on the result of the interim analysis and a pre-specified futility criterion, the second stage can be either enriched with only the biomarker appeared positive patients, or remain as the stratified design with both biomarker appeared positive and biomarker appeared negative patients. Compared to continuous and binary outcomes, test statistics for survival outcomes to account for biomarker misclassification are much more complicated and require special care. We develop log-rank statistics for the interim and final analyses, with an adjustment for the sensitivity and specificity of the biomarker assay. Control of Type I error rate is achieved by considering correlations between adjusted log-rank statistics from the same and/or different stages. R code is developed to calculate critical values, global/marginal power, and sample size. Our method is illustrated with examples of a recently successful development of immunotherapy in non-small-cell lung cancer. [ABSTRACT FROM AUTHOR]

Background: Negative CCTA can effectively exclude significant CAD, eliminating the need for further noninvasive or invasive testing. However, in the presence of severe CAD, the accuracy declines, thus necessitating additional testing. The aim of our study was to evaluate the diagnostic performance of noninvasive cFFR derived from CCTA, compared to ICA in detecting hemodynamically significant stenoses in participants with high CAC scores (>400). Methods: This study included 37 participants suspected of having CAD who underwent CCTA and ICA. CAC was calculated and cFFR analyses were performed using an on-site machine learning-based algorithm. Diagnostic accuracy parameters of CCTA and cFFR were calculated on a per-vessel level. Results: The median total CAC score was 870, with an IQR of 642–1370. Regarding CCTA, sensitivity and specificity for RCA were 60% and 67% with an AUC of 0.639; a LAD of 87% and 50% with an AUC of 0.688; an LCX of 33% and 90% with an AUC of 0.617, respectively. Regarding cFFR, sensitivity and specificity for RCA were 60% and 61% with an AUC of 0.606; a LAD of 75% and 54% with an AUC of 0.647; an LCX of 50% and 77% with an AUC of 0.647. No significant differences between AUCs of coronary CTA and cFFR for each vessel were found. Conclusions: Our results showed poor diagnostic accuracy of CCTA and cFFR in determining significant ischemia-related lesions in participants with high CAC scores when compared to ICA. Based on our results and study limitations we cannot exclude cFFR as a method for determining significant stenoses in people with high CAC. A key issue is accurate and detailed lumen segmentation based on good-quality CCTA images. [ABSTRACT FROM AUTHOR]

Background: Conventional normative samples include individuals with undetected Alzheimer's disease neuropathology, lowering test sensitivity for cognitive impairment. Objective: We developed Mayo Normative Studies (MNS) norms limited to individuals without elevated amyloid or neurodegeneration (A–N–) for Rey's Auditory Verbal Learning Test (AVLT). We compared these MNS A–N– norms in female, male, and total samples to conventional MNS norms with varying levels of demographic adjustments. Methods: The A–N– sample included 1,059 Mayo Clinic Study of Aging cognitively unimpaired (CU) participants living in Olmsted County, MN, who are predominantly non-Hispanic White. Using a regression-based approach correcting for age, sex, and education, we derived fully-adjusted T-score formulas for AVLT variables. We validated these A–N– norms in two independent samples of CU (n = 261) and mild cognitive impairment (MCI)/dementia participants (n = 392) > 55 years of age. Results: Variability associated with age decreased by almost half in the A–N– norm sample relative to the conventional norm sample. Fully-adjusted MNS A–N– norms showed approximately 7– 9% higher sensitivity to MCI/dementia compared to fully-adjusted MNS conventional norms for trials 1– 5 total and sum of trials. Among women, sensitivity to MCI/dementia increased with each normative data refinement. In contrast, age-adjusted conventional MNS norms showed greatest sensitivity to MCI/dementia in men. Conclusions: A–N– norms show some benefits over conventional normative approaches to MCI/dementia sensitivity, especially for women. We recommend using these MNS A–N– norms alongside MNS conventional norms. Future work is needed to determine if normative samples that are not well characterized clinically show greater benefit from biomarker-refined approaches. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to analyze the validity of the PRE-DELIRIC model and E-PRE-DELIRIC model. Methods: Patients who underwent cardiac surgery at a tertiary hospital between January 2019 and December 2022 were included. The presence or absence of delirium was determined based on risk groups, and the sensitivity, specificity, positive predictive power, and negative predictive power were verified using the Youden index. Receiver operating characteristic curves were derived for the PRE-DELIRIC model and E-PRE-DELIRIC model, the area under the curve was calculated, and the 95% confidence interval (CI) was presented. Results: The incidence of delirium was 28.8%. The average scores of the PRE-DELIRIC model and E-PRE-DELIRIC model were 49.72±29.91 and 18.14±0.41, respectively. In the PRE-DELIRIC model, for which a cut-off point of 14.05 was established using the Youden index, the sensitivity, specificity, positive predictive power, and negative predictive power were 64.8%, 83.2%, 61.0%, and 85.4%, respectively. In the E-PRE-DELIRIC model, with a cut-off point of 64.01, the corresponding indicators were 73.1%, 51.5%, 37.8%, and 82.6%, respectively. The areas under the curve for the PRE-DELIRIC model and the E-PRE-DELIRIC model were 0.77 (95% CI, 0.74-0.82) and 0.65 (95% CI, 0.61-0.69), respectively. Conclusion: It may be helpful to use both the E-PRE-DELIRIC and PRE-DELIRIC models. The two prediction models are expected to help improve the quality of nursing for the early detection and prevention of delirium, enabling intensive observations and interventions for patients at high risk of delirium. [ABSTRACT FROM AUTHOR]

Background: Early detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations – the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity. Methods: We retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of 'mild neurocognitive disorder' (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the 'Brain-IT' project. Using Youden's Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach's α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain. Results: The optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach's α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments. Conclusion: Our findings corroborate the existing evidence of the Qmci's good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted. [ABSTRACT FROM AUTHOR]

Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen's κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen's κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and β errors. For the most frequent cases, we provide example of software freely available on the Internet. Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology. Key points • Sample size is a concept related to precision and statistical power. • It has ethical implications, especially when patients are exposed to risks. • Sample size should always be calculated before starting a study. • This review offers simple, go-to methods for sample size calculations. [ABSTRACT FROM AUTHOR]

Diagnostic tests have intrinsic characteristics such as sensitivity, specificity, overall accuracy and likelihood ratios which define their operational performance. It is not uncommon to find in the literature that test value and clinical utility are defined based exclusively on those characteristics. This paper introduces several arguments aimed at prompting a reflection regarding the characteristics that define the true value of diagnostic tests in clinical practice. It concludes with the view that the value of each diagnostic test needs to be established in accordance with the circumstances in which it is used, taking into account extrinsic characteristics such as in whom it is used, when, where and by who. [ABSTRACT FROM AUTHOR]

This study aimed to determine the performance of Leishmania braziliensis and Leishmania peruviana antigens in the detection of ATL by using serum samples obtained between 2013 - 2016. The obtained soluble and excretion/secretion antigens were transferred to membrane nitrocellulose by immunoblot assay. The evaluation was carried out against sera confirmed for ATL, at a confidence level of 95%, determining that the soluble antigen of Leishmania braziliensis had a sensitivity of 87.7%, specificity of 100% and area under the curve of 0.95; on the other hand, Leishmania peruviana showed values of 92.3%, 95.7% and 0.94, respectively. According to the results, we recommend that the reported immunogenic regions should be characterized and analyzed in order to continue with the development of recombinant and synthetic proteins, aimed at improving the efficiency of the serological diagnosis of the disease. [ABSTRACT FROM AUTHOR]

Background/Aim. Cardiovascular diseases are the most common cause of mortality and morbidity worldwide, with the highest incidence in low-middle-income countries. Dobutamine stress echocardiography (DSE) plays a significant role in diagnosing coronary artery disease. The aim of t he study was t o examine the influence of gender on the accuracy of DSE and on the prognostic significance of the left ventricular wall motion score index (WMSI) as a parameter of DSE for adverse cardiovascular events (ACEs). Methods. The prospective, observational study conducted at the Clinic for Cardiovascular Diseases of the University Clinical Center of the Republic of Srpska, Bosnia and Herzegovina, included 143 patients who, due to suspicion of coronary disease, underwent a DSE from January 1, 2021, until February 1, 2022. ACEs that we observed one year after DSE were: unstable angina, acute myocardial infarction, percutaneous coronary intervention, in-stent restenosis, aortocoronary bypass, heart failure, and death. Results. A positive DSE was more common in men (45%) compared to women (25%), which was statistically significant (p < 0.05). The sensitivity of the test in men was 96.0%, and the specificity was 93.9%. In women, the sensitivity was 86.7%, and specificity was also 86.7%. ACEs were more frequent in men (43%) than in women (19%), which was statistically significant (p < 0.01). In men, WMSI had a sensitivity of 96.0% and a specificity of 82.4%. In women, WMSI had a sensitivity of 93.3% and a specificity of 90.6% in predicting ACEs. Conclusion. Our results confirmed the influence of gender on the accuracy o f D SE i n diagnosing coronary artery disease. WMSI as a parameter of DSE was identified as a significant prognostic factor for ACEs in both sexes, with the sensitivity being higher in men and specificity in women. [ABSTRACT FROM AUTHOR]

Objective This study compared adults with peripheral vestibular hypofunction (VH) to healthy controls and assessed the sensitivity and specificity of the Cervical Torsion Test (CTT) and the Head-Neck Differentiation Test (HNDT). This study aimed to determine whether neck problems affected primary outcomes. Methods This cross-sectional study included adults from a specialist consultation for dizziness. VH had been diagnosed with the video Head Impulse Test. Exclusion criteria were conditions following head or neck trauma and diseases of the central nervous system. The sensitivity and specificity of the index tests were calculated, and regression analyses were performed to test for contributing factors. Results A total of 19 patients with VH and a historical cohort of 19 matched healthy controls were included. Most patients with VH (84.2%) experienced symptoms in at least 1 test component, compared to 5.2% of the control group. Of patients with VH, 78.9% had symptoms during the HNDT "en bloc" (en bloc = head and trunk rotated together), whereas only 26.3% reported symptoms during the CTT en bloc. The best discriminatory validity was found for the HNDT en bloc, with a sensitivity of 0.79 (95% CI = 0.54–0.94), a specificity of 0.86 (95% CI = 0.65–0.97), and a positive likelihood ratio of 5.79 (95% CI = 1.97–17.00). The number of symptoms of CTT "in torsion" (in torsion = trunk rotated actively with fixed head) was increased by a factor of 1.13 (95% CI = 1.01–1.27) for every additional point on the Neck Disability Index. Conclusion The CTT and HNDT can serve as non-laboratory tests for patients with dizziness. The HNDT en bloc has the best discriminatory validity, finding those with and those without VH. Symptom reproduction during torsion may help to identify when neck problems may contribute to dizziness. Impact The HNDT en bloc may be useful for ruling VH in or out in patients with dizziness. Positive CTT and HNDT in torsion components may verify the likelihood of additional neck involvement. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to perform a method comparison between the CellaVision preclassification neutrophil count and the reclassification neutrophil count performed by trained laboratory technicians, and to evaluate the diagnostic performance of the preclassification neutrophil count at clinical decision levels. Methods: We retrospectively identified patient samples through 2019–2022 in which the differential count was performed on Cellavision (n = 4,354). Data on sample characteristics and leukocyte- and differential counts was extracted from the electronic medical journal. For each sample, data containing the pre- and reclassification leukocyte classification, respectively, was extracted from the Cellavision software. Method comparison between the pre-and reclassification neutrophil count was performed using Bland Altman analysis. Diagnostic performance of the preclassification neutrophil count was evaluated according to four pre-specified categories of results with the reclassification as reference method. Results: The median difference between the pre- and reclassification neutrophil count was 0.044 x 109/L. The preclassification neutrophil count categorised 95.6% of all samples correctly according to the four categories. The sensitivity, specificity, positive predictive value and negative predictive value for detecting neutrophilia > 7.00 x 109/L was 98.8%, 97.2%, 95.8%, and 99.2%, respectively. In samples with leukopenia (n = 543), the sensitivity, specificity, positive predictive value and negative predictive value for detecting severe neutropenia (< 0.50 x 109/L) was 97.7%, 99.1%, 98.6%, and 98.5%, respectively. Conclusion: The diagnostic performance of the CellaVision preclassification neutrophil count was satisfactory. The preclassification neutrophil count may be released to the electronic medical journal to improve turnaround time and benefit laboratory management. [ABSTRACT FROM AUTHOR]

BACKGROUND: Atrial fibrillation (AF) is often asymptomatic and undiagnosed. As AF and hypertension often coexist, opportunistic AF detection during routine automated blood pressure (BP) measurement appears to be an attractive screening method. METHODS: A systematic literature search was conducted to identify studies assessing the diagnostic test accuracy of office, home, or 24-hour ambulatory BP measuring devices with AF detection algorithms versus reference electrocardiography. Analyses were performed per participant (AF status based on several BP readings; most office/home devices) or per reading (AF status based on individual readings; all ambulatory devices). A meta-analysis stratified by device type (office/home/ambulatory) was conducted to calculate pooled measures of diagnostic accuracy. Sensitivity/meta-regression analyses were also performed. RESULTS: Among 3096 records initially retrieved, 23 diagnostic test accuracy studies were included. Data derived from 11 093 individuals (weighted age 69 years, males 56%, hypertensives 79%, diabetics 24%, and AF prevalence 17%) indicated a pooled sensitivity 0.97 (95% CI, 0.92–0.99), specificity 0.93 (95% CI, 0.90–0.95), and accuracy 0.93 (95% CI, 0.89–0.95), with generally consistent results using office, home, or ambulatory BP devices (slightly lower specificity with the latter). The positive and negative predictive values were 0.70 (95% CI, 0.60–0.80) and 0.99 (95% CI, 0.98–1.00), respectively. Sensitivity analyses indicated lower specificity in studies implementing reading versus participant analyses. Most studies presented a low risk of bias and minor applicability concerns. CONCLUSIONS: There is considerable and consistent evidence suggesting high diagnostic accuracy of AF detection algorithms implemented in automated BP monitors during routine BP measurements in and out of the office. AF diagnosis requires verification (electrocardiography) before treatment is administered. [ABSTRACT FROM AUTHOR]

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Purpose: The discoid meniscus (DM) is distinguished by its thickened, disc‐shaped formation, which extends over the tibial plateau. The likelihood of developing osteoarthritis escalates if a DM tear remains undiagnosed and untreated. While DM tears can be diagnosed through arthroscopy, the high cost, invasive nature and limited availability of this procedure highlight the need for a better diagnostic modality. This study aims to determine the accuracy of magnetic resonance imaging (MRI) in diagnosing DM tears. Methods: A systematic review was conducted to gather articles with at least 10 cases on the comparison of MRI and arthroscopy as the gold standard for DM tear diagnosis. Stata and MetaDisc were used to conduct the statistical analysis. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies‐2 tool. Results: Five diagnostic performance studies, derived from four original research papers involving 305 patients, were evaluated. Based on the pooled data, the sensitivity, specificity, diagnostic odds ratio, positive limit of detection and negative limit of detection were found to be 0.87 (95% confidence interval [CI], 0.82–0.91) and 0.84 (95% CI, 0.75–0.90), 32.88 (95% CI, 5.81–186.02), 5.22 (95% CI, 1.71–15.92) and 0.18 (95% CI, 0.09–0.38), respectively. A hierarchical summary receiver operating characteristic curve with an area under the curve of 0.92 was generated. Conclusion: This meta‐analysis demonstrates that MRI has excellent sensitivity and specificity for diagnosing DM tears. Despite its lower accuracy compared to arthroscopy, MRI can be used in symptomatic patients as a viable alternative to arthroscopy due to its inherent advantages. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]

BackgroundEarly detection and diagnosis of venous thromboembolism are vital for effective treatment. To what extent methodological shortcomings exist in studies of diagnostic tests and whether this affects published test performance is unknown.ObjectivesWe aimed to assess the methodological quality of studies evaluating diagnostic tests for venous thromboembolic diseases and quantify the direction and impact of design characteristics on diagnostic performance.MethodsWe conducted a literature search using Medline and Embase databases for systematic reviews summarizing diagnostic accuracy studies for five target disorders associated with venous thromboembolism. The following data were extracted for each primary study: methodological characteristics, the risk of bias scored by the QUADAS QUADAS-2 instrument, and numbers of true-positives, true-negatives, false-positives, and false-negatives. In a meta-analysis, we compared diagnostic accuracy measures from studies unlikely to be biased with those likely to be biased.ResultsEighty-five systematic reviews comprising 1’818 primary studies were included. Adequate quality assessment tools were used in 43 systematic reviews only (51%). The risk of bias was estimated to be low for all items in 23% of the primary studies. A high or unclear risk of bias in particular domains of the QUADAS/QUADAS-2 tool was associated with marked differences in the reported sensitivity and specificity.ConclusionsSignificant limitations in the methodological quality of studies assessing diagnostic tests for venous thromboembolic disorders exist, and studies at risk of bias are unlikely to report valid estimates of test performance. Established guidelines for evaluation of diagnostic tests should be more systematically adopted.Systematic Review RegistrationPROSPERO (CRD 42021264912).

Background European guidelines recommend the use of a 0h/1h hs‐cTn (high‐sensitivity cardiac troponin) protocol in patients with acute chest pain. We aimed to determine the performance of this protocol in routine care when supplemented with patient history and ECG and a recommendation to refrain from noninvasive testing in low‐risk patients. Methods and Results This was a pre‐ and postimplementation study with concurrent controls. Patients with chest pain were enrolled at 5 Swedish emergency departments (EDs) during a 10‐month period in both 2017 and 2018. All hospitals used a 0h/3h hs‐cTnT protocol in 2017 and 3 EDs implemented a 0h/1h hs‐cTnT protocol during 10 months in 2018. The 2 coprimary outcomes were the incidence of acute myocardial infarction and all‐cause death within 30 days and ED length of stay. The study included 26 040 consecutive patients. In the intervention hospitals, 21 (0.40%) of the discharged patients had an acute myocardial infarction/death event during the control period (0h/3h testing) and 22 (0.45%) in the intervention period (0h/1h testing), which met the criteria for noninferiority. There was no significant difference in ED length of stay (ratio 0.99, P=0.48) or ED discharge rate between the periods in the intervention versus the control hospitals. A total of 3142 patients met low‐risk 0h/1h hs‐cTnT criteria and were discharged, of whom 2 had an acute myocardial infarction/death event. Conclusions A 0h/1h hs‐cTnT protocol incorporating patient history and ECG was as safe as using a 0h/3h protocol but did not reduce ED length of stay or increase the discharge rate. Refraining from noninvasive testing in patients identified as low risk was safe. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03421873.

Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information. Plain language summary Whole-body magnetic resonance imaging demonstrates the entire body and can detect the spread of tumour, without the burden of ionising radiation. Recently, the STREAMLINE study reported that whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. However, whole-body magnetic resonance imaging is complex to report. Machine learning is a type of artificial intelligence whereby a computer learns from being given previous data to undertake a task, using techniques such as classification forests, convolutional neural networks, and multi-atlas approaches. Our aim was to develop a machine-learning method to automatically detect lesions on whole-body magnetic resonance imaging to support radiologists by potentially improving their ability to correctly detect disease and reduce the reading time of whole-body magnetic resonance imaging scans in patients with cancer. Firstly, whole-body magnetic resonance imaging scans from 51 healthy volunteers were used to develop machine-learning methods to automatically detect normal organs. Secondly, machine-learning methods were trained to detect cancer lesions, using 271 whole-body magnetic resonance imaging scans from a previous study. Finally, the refined machine-learning technique was tested in 188 patient scans from a previous study, to see if the technique could improve radiology reporting by increasing accuracy and speed in detecting disease. We designed a system to test the accuracy of radiologists reading whole-body magnetic resonance imaging with or without machine-learning support in a near-real clinical National Health Service setting. Twenty-five independent radiologists (18 experienced in reading whole-body magnetic resonance imaging and 7 radiologists inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging scans to read with or without machine-learning support. We found that machine-learning support resulted in similar accuracy for detecting disease and was slightly more efficient in the reading time than for radiological reads without machine-learning support. Differences in interpretation between experienced readers were considered moderate in both cases. Overall, the study was an ambitious attempt to undertake a highly complex machine-learning task, to detect cancer on whole-body magnetic resonance imaging. Many important steps have been taken but the current machine-learning algorithm did not result in a significant improvement in the radiologist’s accuracy for disease detection, although it may have slightly reduced the time taken to read the study. Future work is advocated to further develop machine-learning tools to improve the accuracy of tumour detection. Scientific summary Background Whole-body magnetic resonance imaging (WB-MRI) has been developed in the last decade and it has been proposed as an alternative to multimodality cancer staging pathways. The STREAMLINE study has demonstrated that staging of lung and colon cancer via WB-MRI was of similar accuracy to standard care staging pathways but resulted in fewer tests being required, and a reduction in staging time and cost. However, this technique has not widely translated into clinical practice, being limited to a few expert centres. A barrier to translation may be the complexity of interpretation by inexperienced readers. A machine-learning (ML) algorithm for automated detection of cancer lesions, to assist radiologists, may allow clinical translation of WB-MRI for the benefit of patient care. Objective The objective of Phases 1 and 2 was to develop a ML method for automatic detection of cancer lesions on WB-MRI. The primary objective of Phase 3 was to compare the diagnostic test accuracy of WB-MRI in patients being staged for cancer with and without ML support, when read by independent, experienced readers. The reference standard was the consensus reference panel from the STREAMLINE study, which had recorded the sites of disease in the STREAMLINE patients using all available clinical information for a 12-month follow-up from time of enrolment. The planned secondary objectives of this study were: to compare the reading time of WB-MRI scans, with and without ML support; to determine the interobserver variability of WB-MRI diagnosis by different radiologists, with and without ML support; to evaluate the diagnostic accuracy of WB-MRI read by non-experienced readers, with and without ML support; to evaluate different combinations of acquired MRI sequences; all the above with and without ML support (not achieved); to determine any difference in costs related to radiology reading time by means of a simple cost-effectiveness analysis (not achieved). Design This was an observational study (study limited to working with data), using different patient cohorts, and methodologies, being evaluated in series during three consecutive phases. Phase 1: Previously acquired WB-MRI scans in 51 healthy volunteers were stitched into imaging volumes. The normal organs and skeleton were segmented by a trained radiologist. We compared and tested several state-of-the-art medical image segmentation methods to train an algorithm to automatically detect and segment the organs. This included a multi-atlas (MA) approach, classification forests (CFs) and convolutional neural networks (CNNs) methods. For the CFs method, we used 50 trees with a maximum tree depth of 30 in the segmentation. The stopping criterion for growing trees was if either the objective function (information gain) could not be further improved or the number of training samples in a leaf fell below a threshold of four samples. For the CNNs method, we employed a dual pathway (2 resolutions), 11-layer deep CNN, where the last 2 layers correspond to fully connected layers, which combine the features extracted on the 2 resolution pathways. We adopted 50–70 feature maps (different kernels) for each layer. The network architecture was fully convolutional and there were no max-pooling layers, which we found to increase segmentation accuracy. The CNN architecture was a balance between model capacity, training efficiency, and memory demands. The third algorithm was based on a MA label propagation approach. MA segmentation uses a set of atlases (images with corresponding segmentations) that represent the interparticipant variability of the anatomy to be segmented. Each atlas was registered to the new image to be segmented using a deformable image registration. The MA approach accounts for anatomical shape variability and is more robust than single-atlas propagation methods, in that at any errors associated with propagation were averaged out when combining multiple atlases. The approach employed here makes use of efficient 3D–3D intensity-based image registration with free-form deformations as the transformation model and correlation coefficient as the similarity measure. Majority voting is used to derive the final tissue label at each voxel. Phase 2: Available WB-MRI scans from the STREAMLINE study (n = 438) were allocated by the study statistician to Phase 2 (model training) and clinical validation (hold-out set of 193 cases). The visible lesions in cases allocated to Phase 2 were segmented by trained radiologists, using the defined consensus reference standard for the sites of disease. Using 226 evaluable and annotated cases, we trained a model for lesion detection and localisation rather than attempting accurate automated segmentation. We found that lesion detection in WB-MRI was suboptimal with the CNN alone. An optimal ‘two-stage’ ML method was developed and tested. In the first stage of the ‘two-stage’ method, the information from Phase 1 was used to identify the position of organs and bones and in stage two, the lesions were detected. Stage two could be modular with respect to the anatomical location in which the suspected lesion could be found. The architecture and configuration of the used CNN were modified to achieve optimal performance. Phase 3: The final two-stage ML algorithm from Phase 2 was applied to the 193 cases that were held out for Phase 3, generating probability heatmap volumes. WB-MRI scans were reported by 25 independent radiologists (18 radiologists experienced in reading WB-MRI and 7 radiologists inexperienced in WB-MRI). All radiologists took part in the 1st and 2nd round reads which incorporated inter-rater reads, and 8 participated in the 3rd round read for intrarater reads. All reads were undertaken in an NHS radiology reporting room, although using a separate cloud-based picture archiving and communication system (PACS). Based on experience level, 10–16 cases were randomly allocated by the study statistician to the reading lists for inexperienced or experienced radiologists. Each radiologist read both lung and colon cases with and without ML as a paired cohort. There were at least 4 weeks interval between each reading round for individual radiologists. Cases allocated to round 1 would be a random mixture of cases with and without ML support and then in round 2 the cases would be reversed such that each case was read once with and once without ML support. The number of cases with and without ML output was balanced in each reading round. A scribe recorded reading time. Results Phase 1 results We found that CNNs outperformed CFs and the MA algorithm when T2w volumes were used as input to the algorithms and when using pooled overlap-evaluation metrics [Dice similarity coefficient (DSC), recall (RE), precision (PR)] to assess the accuracy of segmentation. When the performance of the algorithms was assessed, with pooled surface distance metrics [average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD)], it was the MA algorithm that performed best. Single misinterpreted voxels in CFs and CNNs can greatly elevate ASD, RMSSD, and HD; these metrics are particularly sensitive to outliers. We then assessed the pooled metrics performance of CFs and CNNs when using all imaging combinations [T2w + T1w + diffusion-weighted imaging (DWI)] as input, arguing that maximisation of training information to the algorithms might improve the performance of segmentation. We found that the performance of CFs was improved, however not significantly, when using all imaging combinations as input for training. The opposite was observed for CNNs. The findings for the pooled metrics analysis, described above, were corroborated by a ‘per-organ’ quantitative analysis of the commonly used DSC, to assess the performance of our segmentation algorithms. This analysis confirmed that for all individual anatomical structures (except for the bladder), the algorithm that returned the greatest DSC was CNNs with T2w images only used as input. As our morphological T2w and T1w scans were acquired using breath-holds and the DWI sequence was acquired with free breathing, we found that there was significant displacement between soft tissues in anatomical areas adjacent to the diaphragm between these types of scans. As the employed affine registration method could not fully compensate for nonlinear motions caused by breathing, we assumed that misregistration could be the reason why the performance of CNNs, despite performing better than the other two algorithms when using T2w volumes as input only, was degraded when using all imaging combinations as input for training. A more robust, nonlinear registration method could improve the accuracy of CNNs and further improve the performance of CFs. The performance of our methods cannot be directly compared to similar methods in the literature because there is no previous work describing automatic, simultaneous segmentation of healthy organs and bones in multiparametric WB-MRI. We believe, however, that our methods may compare favourably to other ML methods for detection and segmentation in medical imaging because our classifiers are inherently multilabel and effective training was achieved when using a relatively small number of data sets, something that is very important in the clinical setting. However, we still need to address the performance limitations of our algorithms when segmenting organs with big variability in appearance (e.g. the gallbladder or the pancreas). Phase 2 results We tested different ML methods for lesion detection. We found that using CNNs was not optimal for lesion detection on WB-MRI. This may have been due to the small fraction of lesion volume occupying the scanned space, when compared to the WB volume. It may also have been due to the complexity of intensities in background tissue and the lesion with weak boundaries causing challenges for the CNNs. We, therefore, adapted our process to become an optimal two-stage process, with an initial stage for detection of organs as per the Phase 1 technique followed by a CNN to detect lesions. Stage two could be modular with respect to the anatomical location where the suspected lesion can be found. The architecture and configuration of the used CNN could be modified to achieve optimal performance for lesion detection. Phase 3 results All radiology reads were completed between November 2019 and March 2020. Among the 193 cases allocated to Phase 3, 188 WB-MRI scans were evaluable (117 colon and 71 lung cancer) and 50/188 cases had metastases. Per-patient specificity for detection of metastases within experienced readers was 86.2% (WB-MRI + ML) and 87.7% [WB-MRI + standard deviation (SD)], [difference −1.5%, 95% confidence interval (CI) −6.4%, 3.5%; p = 0.387]. Per-patient sensitivity produced results of 66.0% (WB-MRI + ML) and 70.0% (WB-MRI + SD) (difference −4.0%, 95% CI −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (WB-MRI + ML) and 60.0% (WB-MRI + SD). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to the low number of lesions in each site, hampering interpretation. Reading time was lowered under ML by 6.2% (95% CI −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% CI 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using ML in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% CI 0.47 to 0.81 (WB-MRI + ML) and Cohen’s κ = 0.66, 95% CI 0.47 to 0.81 (WB-MRI + SD). Conclusion Phase 1 In Phase 1, we developed and evaluated three state-of-the-art algorithms that automatically segment healthy organs and bones in WB-MRI with accuracy comparable to the one achieved manually by clinical experts, using relatively sparse training data. An algorithm based on CNNs and trained using T2w-only images as input performs favourably when compared to CFs or a MA algorithm, trained with either T2w-only images or a combination of imaging inputs (T2w + T1w + DWI). Using multimodal MRI data as input for training did not improve the segmentation performance in this work, but it is anticipated to improve the segmentation performance if more effective WB registration between the various imaging modalities can be performed. This investigation was the first step towards developing robust algorithms for the automatic detection and segmentation of benign and malignant lesions in WB-MRI scans for staging of cancer patients. Phase 2 There were many challenges in training the algorithm for lesion detection, with a very heterogeneous data set, acquired at 16 sites on different machines. The low number of metastatic lesions were scattered through many anatomic sites. Identification of lesions of relatively small volume in relation to the large volume of the WB-MRI required a two-stage approach for lesion detection, with initial organ identification followed by lesion detection. Phase 3 We undertook a robust diagnostic test accuracy study comparing WB-MRI with ML support (index text) with standard WB-MRI, without ML support (comparator test), with paired reads separated by a wash-out period. Although there was no clear statistical difference with or without ML support in terms of diagnostic test accuracy, either in experienced or inexperienced hands, we found that ML reads were likely to be a little shorter in reading time. Implications for health care Machine learning support for image interpretation is a rapidly expanding area of research. With continually increasing demand for diagnostic imaging and a radiology workforce crisis in the NHS, as well as globally, ML techniques may offer support to allow complex imaging modalities, such as WB-MRI, to be ready for translation into clinical care for the benefit of patients. Phase 1 demonstrated that with relatively sparse data, we could develop very successful organ segmentation tools in highly complex data sets and this has many potential future roles. In this study, we used this as a first step prior to lesion detection and this generic method could be applied to a wider range of disease areas, including other tumour types. We found that ML support slightly shorted the reading time, although with no improvement in diagnostic accuracy. However, we have shown that lesion detection using ML is achievable on heterogeneous and complex multiparametric WB-MRI scans. With further model training, we believe that ML support is feasible in the future, with the potential to improve translation of WB-MRI more widely. In addition, many of the techniques that have been developed in the course of the study have the potential to be applied to other areas of diagnostic imaging. Recommendations for future research Future research should investigate: Further improvement in lesion detection accuracy, with evaluation of failure cases for improved model training. Evaluate lesion detection technique on other cancers, notably breast, prostate and myeloma. Further developments in understanding the marrow composition in healthy aging and in metastatic cancer. Developing further understanding and methods to overcome registration challenges between breath-hold and non-breath-hold MRI sequences in order to improve ML tissue characterisation. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Background. Prompt and effective rehabilitation of people with mental retardation through processing and analysis of streaming speech data is important for the timely response of medical decision support systems aimed at improving the condition of a person with disabilities. The purpose of this work is to present an original methodological approach that ensures increased efficiency and efficiency of assessing the rehabilitation process of people with mental retardation through the analysis of informative parameters of speech signals under audiovisual stimulation. Materials and methods. To achieve the stated goals, the work uses mathematical and statistical methods for processing and detecting amplitude, frequency and time parameters of speech signals, programming and mathematical modeling. Results. A scheme has been developed for forming an information subsystem for recording speech signals in a person with mental retardation, which includes various stages of processing and analyzing speech signals in order to form a set of informative significant parameters. Conclusions. A methodological approach to increasing the efficiency of the rehabilitation process through the use of original algorithms and programs for detecting and comparing significant parameters of speech signals before and after the rehabilitation process and adjusting the process through negative feedback is presented and substantiated.

Colorectal cancer (CRC) remains a significant global health challenge, with approximately 1.9 million new cases and 930,000 deaths reported in 2020. The highest incidence rates are observed in Australia/New Zealand and Europe, while lower rates are found in Africa and Southern Asia. Projections for 2040 indicate a rise to 3.2 million new cases and 1.6 million deaths, particularly in high development index regions, underscoring the need for improved prevention and detection. Despite advancements in screening methods and polyp removal, CRC mortality remains high in the United States due to non-adherence to recommended tests. Barriers such as cost and lack of insurance contribute to this issue. Cell-free DNA (cfDNA) blood-based testing offers a promising alternative, with studies showing 83.1% sensitivity for CRC and 89.6% specificity for advanced neoplasia, comparable to traditional screening methods but with reduced risk of adverse events. The recent FDA approval of the Shield blood test, which has demonstrated 83% efficacy in detecting late-stage CRC, represents a significant advancement. Incorporating cfDNA testing into screening protocols could improve accessibility and compliance, especially for those unwilling or unable to undergo more invasive procedures. Regular evaluation of cfDNA testing, including Shield, is essential for enhancing CRC screening strategies and patient outcomes. [ABSTRACT FROM AUTHOR]

Abstract Sarcopenia is linked to chronic inflammation and muscle wasting. This research aims to compare the screening accuracy of tools for sarcopenia in axial spondyloarthritis (axSpA). A cross-sectional study involving 104 axSpA patients was conducted at Phramongkutklao Hospital between January 2020 and February 2021. Sarcopenia was diagnosed according to the AWGS 2019 criteria. Appendicular skeletal muscle mass was measured using DXA. SARC-F, SARC-CalF, and SARC-F+EBM, muscle strength, and physical performance were assessed. The screening tests were evaluated using ROC curves. The optimal cutoffs were identified with the Youden index. Most patients were male (74%), with a mean (SD) age and disease duration of 42.6 (12.22) and 8.3 (8.5), respectively. The prevalence of sarcopenia was 22.1%. The AUCs (95% CI) for calf circumference, SARC-F, SARC-CalF, SARC-F+EBM, handgrip strength, chair stand time, gait speed, and time and go test were 0.830 (0.734, 0.925), 0.509 (0.373–0.645), 0.782 (0.670–0.894), 0.856 (0.758–0.954), 0.710 (0.594–0.825), 0.640 (0.508–0.772), 0.689 (0.539–0.839), and 0.711 (0.576–0.846), respectively. The optimal cutoffs for SARC-F, SARC-CalF, and SARC-F+EBM were 1, 10, and 10, with sensitivity/specificity of 81.0%/29.7%, 90.5%/68.9%, and 77.3%/87.2%, respectively. Calf circumference, SARC-CalF, and SARC-F+EBM had the best performance to screen for sarcopenia in axSpA patients. Lowering the thresholds would potentially enhance the performances of SARC-CalF and SARC-F+EBM.

Background/Aims: Recognizing Helicobacter pylori infection during endoscopy is important because it can lead to the performance of confirmatory testing. Linked color imaging (LCI) is an image enhancement technique that can improve the detection of gastrointestinal lesions. The purpose of this study was to compare LCI to conventional white light imaging (WLI) in the endoscopic diagnosis of H. pylori infection.Methods: We conducted a comprehensive literature search using PubMed, Embase, and the Cochrane Library. All studies evaluating the diagnostic performance of LCI or WLI in the endoscopic diagnosis of H. pylori were eligible. Studies on magnifying endoscopy, chromoendoscopy, and artificial intelligence were excluded.Results: Thirty-four studies were included in this meta-analysis, of which 32 reported the performance of WLI and eight reported the performance of LCI in diagnosing H. pylori infection. The pooled sensitivity and specificity of WLI in the diagnosis of H. pylori infection were 0.528 (95% confidence interval [CI], 0.517 to 0.540) and 0.821 (95% CI, 0.811 to 0.830), respectively. The pooled sensitivity and specificity of LCI in the diagnosis of H. pylori were 0.816 (95% CI, 0.790 to 0.841) and 0.868 (95% CI, 0.850 to 0.884), respectively. The pooled diagnostic odds ratios of WLI and LCI were 15.447 (95% CI, 8.225 to 29.013) and 31.838 (95% CI, 15.576 to 65.078), respectively. The areas under the summary receiver operating characteristic curves of WLI and LCI were 0.870 and 0.911, respectively.Conclusions: LCI showed higher sensitivity in the endoscopic diagnosis of H. pylori infection than standard WLI.

Abstract Background Thymic cysts are a rare benign disease that needs to be distinguished from low-risk thymoma. [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is a non-invasive imaging technique used in the differential diagnosis of thymic epithelial tumours, but its usefulness for thymic cysts remains unclear. Our study evaluated the utility of visual findings and quantitative parameters of [18F]FDG PET/CT for differentiating between thymic cysts and low-risk thymomas. Methods Patients who underwent preoperative [18F]FDG PET/CT followed by thymectomy for a thymic mass were retrospectively analyzed. The visual [18F]FDG PET/CT findings evaluated were PET visual grade, PET central metabolic defect, and CT shape. The quantitative [18F]FDG PET/CT parameters evaluated were PET maximum standardized uptake value (SUVmax), CT diameter (cm), and CT attenuation in Hounsfield units (HU). Findings and parameters for differentiating thymic cysts from low-risk thymomas were assessed using Pearson’s chi-square test, the Mann-Whitney U-test, and receiver operating characteristics (ROC) curve analysis. Results Seventy patients (18 thymic cysts and 52 low-risk thymomas) were finally included. Visual findings of PET visual grade (P

Abstract To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)–based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE–based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann–Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = − 4.522, p