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Objective To investigate the diagnostic performance of radiomics based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) of MRI for preoperative prediction of extramural vascular invasion (EMVI) in rectal cancer. Methods A total of 168 patients with pathology-confirmed rectal adenocarcinoma were enrolled during January 2010 to June 2023. The patients underwent preoperative rectal MRI scans, and they were randomly divided into training set and validation set at a 7∶3 ratio. Radiomic features from T2WI and DWI were extracted and selected by dimensionality reduction using the maximum relevance minimum redundancy (mRMR) method and the least absolute shrinkage and selection operator (LASSO) regression analysis with ten-fold cross-validation. The radiomic total score (Radscore) for each patient was calculated to make radiomics model. The training set enrolled three clinical features [gender, age and preoperative level of carcinoembryonic antigen (CEA)] and six magnetic resonance imaging features [ADC value, depth of infiltration, tumor length, tumor location, T staging and magnetic resonance imaging-defined extramural vascular invasion (mrEMVI)].The clinical model was established through univariable and multivariable logistic regression analysis based on above clinical and imaging features， and the clinical-radiomics model (combined model) was established with Radscore and independent risk factors from the clinical model. The diagnostic efficacy of each model was assessed using receiver operating characteristic (ROC) curve. The differences in performance among the models were compared using the DeLong test. The Calibration curves were employed to evaluate the consistence between the preoperative predictive results obtained from the nomogram and the postoperative pathological results. Additionally, decision curve analysis (DCA) was applied to evaluate the clinical utility of the three models. Results The area under the curve (AUC) of the ROC curve for the combined model, clinical model, and radiomics model in the training were 0.926, 0.888, 0.756, and were 0.917, 0.896, 0.782 in validation sets, respectively. The DeLong test showed that the diagnostic efficacy of combined model was higher than that of radiomics model in both training and validation sets (P

Abstract Background Identifying risk factors for adverse pathologic features in low-risk papillary thyroid microcarcinoma (PTMC) can provide valuable insights into the necessity of surgical or non-surgical treatment. This study aims to develop a nomogram for predicting the probability of adverse pathologic features in low-risk PTMC patients. Methods A total of 662 patients with low-risk PTMC who underwent thyroid surgery were retrospectively analyzed in Qilu Hospital of Shandong University from May 2019 to December 2021. Logistic regression analysis was used to determine the risk factors for adverse pathologic features, and a nomogram was constructed based on these factors. Results Most PTMC patients with these adverse pathologic features had tumor diameters greater than 0.6 cm (p 0.05 each). The nomogram was drawn to provide a quantitative and convenient tool for predicting the risk of adverse pathologic features based on age, gender, family history of thyroid cancer, autoimmune thyroiditis, tumor size, and BRAF V600E mutation in low-risk PTMC patients. The areas under curves (AUC) were 0.645 (95% CI 0.580–0.702). Additionally, decision curve analysis (DCA) and calibration curves were used to evaluate the clinical benefits of this nomogram, presenting a high net benefit. Conclusion Tumor size > 0.60 cm was identified as an independent risk factor for adverse pathologic features in low-risk PTMC patients. The nomogram had a high predictive value and consistency based on these factors.

Background: Currently, there is a lack of prognostic evaluation methods for non-serous epithelial ovarian cancer (EOC). Method: We collected patients with non-serous EOC diagnosed between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database into a training cohort (n = 2078) and an internal validation cohort (n = 891). Meanwhile, patients meeting the criteria were screened from the Fujian Provincial Maternal and Child Health Hospital from 2013 to 2022 as an external validation cohort (n = 56). Univariate and multivariable logistic regression were used to determine the independent prognostic factors of cancer-specific survival (CSS) to construct the nomogram. The nomogram was validated by the concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curves. Result: Age, laterality, preoperative CA125 status, histologic type, tumor grade, AJCC stage, surgery lesion, number of lymph nodes examined, residual lesion size, and bone metastasis were identified as independent prognostic factors to construct the nomogram. The nomogram showed better predictive ability than FIGO stage through internal and external cohorts validation. The C-index of the nomogram in the training cohort, validation cohort, and external validation cohort were 0.831, 0.835 and 0.944 higher than those of the Federation International of Gynecology and Obstetric (FIGO) stage, P

IntroductionGastric schwannoma is a rare benign tumor accounting for only 1–2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2–5 cm).MethodsGastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves.ResultsLogistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105–11.900), absence of necrosis (OR 4.752; 95% CI 1.464–15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499–88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042–1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080–1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900–0.973) and validation (AUC, 0.921; 95% CI, 0.830–1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value.ConclusionThe proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

ObjectivesTo explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN).MethodsClinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People’s Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA).ResultsUnivariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value.ConclusionGender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.

Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool. [ABSTRACT FROM AUTHOR]

Background Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC. Methods The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129–3p on VC in vitro and the association between circulating miR-129–3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort. Results In cell experiments, miR-129–3p was found to attenuate vascular calcification, and in human, serum miR-129–3p exhibited a negative correlation with vascular calcification, suggesting that miR-129–3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129–3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model. Conclusion We build a diagnostic prediction model and present it as an intuitive tool based on miR-129–3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events. [ABSTRACT FROM AUTHOR]

Timely recognition of risk factors for early progression in older adult patients with COVID-19 is of great significance to the following clinical management. This study aims to analyze the risk factors and create a nomogram for early progression in older adult patients with COVID-19 in the Omicron era. Methods: A total of 272 older adults infected with COVID-19 admitted from December 2022 to February 2023 were retrospectively recruited. Risk factor selection was determined using the logistic and the least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then created to predict early progression, followed by the internal validation and assessment of its performance through plotting the receiver operating characteristic (ROC), calibration, and decision curves. Results: A total of 83 (30.5%) older adult patients presented an early progression on chest CT after 3– 5 days of admission under standard initiate therapy. Six independent predictive factors were incorporated into the nomogram to predict the early progression, including CRP > 10 mg/L, IL-6 > 6.6 pg/mL, LDH > 245 U/L, CD4+ T-lymphocyte count < 400/μL, the Activities of Daily Living (ADL) score ≤ 40 points, and the Mini Nutritional Assessment Scale-Short Form (MNA-SF) score ≤ 7 points. The area under the curve (AUC) of the nomogram in discriminating older adult patients who had risk factors in the training and validation cohort was 0.857 (95% CI 0.798, 0.916) and 0.774 (95% CI 0.667, 0.881), respectively. The calibration and decision curves demonstrated a high agreement in the predicted and observed risks, and the acceptable net benefit in predicting the early progression, respectively. Conclusion: We created a nomogram incorporating highly available laboratory data and the Comprehensive Geriatric Assessment (CGA) findings that effectively predict early-stage progression in older adult patients with COVID-19 in the Omicron era. [ABSTRACT FROM AUTHOR]

Background: Identifying risk factors for adverse pathologic features in low-risk papillary thyroid microcarcinoma (PTMC) can provide valuable insights into the necessity of surgical or non-surgical treatment. This study aims to develop a nomogram for predicting the probability of adverse pathologic features in low-risk PTMC patients. Methods: A total of 662 patients with low-risk PTMC who underwent thyroid surgery were retrospectively analyzed in Qilu Hospital of Shandong University from May 2019 to December 2021. Logistic regression analysis was used to determine the risk factors for adverse pathologic features, and a nomogram was constructed based on these factors. Results: Most PTMC patients with these adverse pathologic features had tumor diameters greater than 0.6 cm (p < 0.05). Other factors (age, gender, family history of thyroid cancer, history of autoimmune thyroiditis, and BRAFV600E mutation) had no significant correlation with adverse pathologic features (p > 0.05 each). The nomogram was drawn to provide a quantitative and convenient tool for predicting the risk of adverse pathologic features based on age, gender, family history of thyroid cancer, autoimmune thyroiditis, tumor size, and BRAFV600E mutation in low-risk PTMC patients. The areas under curves (AUC) were 0.645 (95% CI 0.580–0.702). Additionally, decision curve analysis (DCA) and calibration curves were used to evaluate the clinical benefits of this nomogram, presenting a high net benefit. Conclusion: Tumor size > 0.60 cm was identified as an independent risk factor for adverse pathologic features in low-risk PTMC patients. The nomogram had a high predictive value and consistency based on these factors. [ABSTRACT FROM AUTHOR]

Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666–0.748] in the training group and 0.702 (95% CI, 0.639–0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770–0.943), 0.747 (95% CI, 0.689–0.804), and 0.700 (95% CI, 0.643–0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733–0.947), 0.763 (95% CI, 0.677–0.849), and 0.709 (95% CI, 0.623–0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC). Methods: Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model’s performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram. Results: Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95% CI: 0.993–0.999) and validation (AUC = 0.995, 95% CI: 0.974, 1.000) cohorts, and good calibration (Brier scores: 0.019 and 0.016, respectively). DCA demonstrated the clinical usefulness of the US-based nomogram. Conclusion: A noninvasive clinical and US-based nomogram combining conventional US and CEUS features possesses good predictive value for differentiating rUC from c-RCC. [ABSTRACT FROM AUTHOR]