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Aims: The 2023 FIGO staging criteria for endometrial cancer (EC) introduced marked changes from the 2009 version. The full implication of these changes for patient diagnosis and treatment is unknown. We evaluate the differences in staging and prognostication between the two systems, with and without inclusion of molecular classification. Methods and results: We assigned (1) FIGO 2009, (2) 2023 molecular‐agnostic and (3) 2023 molecular‐informed stages to 404 fully staged and molecularly classified patients with EC. Disease‐specific and progression/relapse‐free survival were analysed via the Kaplan–Meier method and compared with log‐rank testing; 118 of 252 (47%) FIGO 2009 stage I patients were upstaged based on histopathological findings alone. Stage I/II subgroup survival distribution analysis showed a worse prognosis in FIGO 2023 IIB and IIC patients. In the molecular‐informed FIGO 2023 system, three of 15 (20%) POLE‐mutated stage I/II cases were downstaged from FIGO 2009 and eight (53%) were downstaged from molecular‐agnostic FIGO 2023. Fifty‐one of 60 (85%) p53‐abnormal tumours were upstaged from the FIGO 2009, whereas 13 of 60 (22%) were upstaged from the 2023 molecular‐agnostic stage. Molecular classification improved prognostic stratification for both 2009 and 2023 FIGO systems. Conclusions: Downstaging based on POLE mutation more accurately represents patient outcomes. However, in the absence of known POLE status, applying molecular‐agnostic FIGO 2023 criteria for stage I/II disease should be conducted with caution. For aggressive histotypes, additionally reporting FIGO 2009 stage should be considered. Upstaging based on substantial lymphovascular space invasion, aggressive histotype with any myometrial invasion and abnormal p53 improves prognostic discernment. Further subdivisions within stage I/II provide minimal additional prognostic information. [ABSTRACT FROM AUTHOR]

Objective: To examine changes in late‐ versus early‐stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania. Data Sources and Study Setting: We analyzed data from the Pennsylvania cancer registry (2010–2018) for adult Medicaid beneficiaries aged 21–64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital‐based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council). Study Design: We leveraged a natural experiment arising from county‐level variation in mandatory MMC in Pennsylvania. Using a stacked difference‐in‐differences design, we compared changes in the probability of late‐stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs. Data Collection/Extraction Methods: N/A. Principal Findings: Mandatory MMC was associated with a reduced probability of late‐stage cancer diagnosis (−3.9 percentage points; 95% CI: −7.2, −0.5; p = 0.02), particularly for screening‐amenable cancers (−5.5 percentage points; 95% CI: −10.4, −0.6; p = 0.03). We found no significant changes in late‐stage diagnosis among non‐screening amenable cancers. Conclusions: In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening‐amenable cancers. Considering that over half of the sample was diagnosed with late‐stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline‐concordant screening or diagnostic procedures. [ABSTRACT FROM AUTHOR]

Introduction: During the Epithelial-mesenchymal Transition (EMT) process, cells undergo a transformation in which they lose their epithelial characteristics and acquire mesenchymal traits. This phenomenon was observed during the metastatic stage of neoplastic disorders, such as cervical cancer. Certain indicators are present during EMT. Aim: To evaluate the role of EMT in utero-cervical carcinoma by analysing the expressions of E-cadherin and vimentin in histopathologically confirmed cases. Materials and Methods: This retrospective cross-sectional study was conducted over a period of two years, from January 1, 2013, to December 31, 2014, at the Department of Pathology in a tertiary care centre and referral hospital. A total of 50 histopathologically confirmed cases of utero-cervical Squamous Cell Carcinoma (SCC) were included in this study. These case were analysed focusing on variables such as age, age at marriage, parity, family history, and hormonal intake. Tumour stages and grades were assessed alongside E-cadherin and vimentin expressions using Immunohistochemistry (IHC). Results were statistically analysed, and the database was created in MS Excel (Version 2007) and analysed using IBM Statistical Package for the Social Sciences (SPSS) Statistics software version 20.0, with significance set at p<0.05. Results: Most cases occurred in the seventh decade of life (42%, 21/50). Out of the 50 tumours studied, 7 (14%) had a positive history of cervical carcinoma, and 6 (12%) had a positive history of hormonal intake. The cervical tumour stages and grades showed statistical significance in relation to family history (p-value=0.001, p-value=0.007) and hormonal intake (p-value<0.001), strongly indicating evidence against the null hypothesis. A total of 4 (8%) cases showed positive lymph node involvement, all of which were in stage 3; among these, 3 (75%) were in grade 3 and 1 (25%) was in grade 2. E-cadherin expression was higher in lower grade and lower stage tumours, while vimentin expression was higher in higher grade and higher stage tumours, as well as in all four lymph node-positive cases. Therefore, the downregulation of E-cadherin and the upregulation of vimentin expression signify EMT. Conclusion: The study concludes that E-cadherin and vimentin play crucial roles in EMT in cervical tumours. E-cadherin was more prevalent in lower grade and stage tumours, while vimentin was more common in higher grade and stage tumours, as well as in all lymph node-positive cases. Cervical tumour stages and grades were significantly associated with family history and hormonal intake. Despite contradictory findings in clinical trials, EMT markers like E-cadherin and vimentin will remain important in future research on cervical carcinoma. [ABSTRACT FROM AUTHOR]

Objective: The International Federation of Gynecology and Obstetrics committee modified the endometrial cancer (EC) staging system based on the histopathological feature and molecular profile. The aim is to evaluate the clinical implications of the new 2023 system compared with the previous 2009 system. Methods: We retrospectively identified 161 patients with EC who underwent primary surgical treatment between 2014 and 2018 at Seoul National University Hospital. The droplet-digital polymerase chain reaction for POLE mutations and immunohistochemistry for MLH1, PMS2, MS2, MSH6, and p53 were performed using tissues from formalin-fixed, paraffin-embedded blocks. All patients were categorized according to the 2009 and 2023 staging systems. Results: The median follow-up period was 62.9 months (range, 0.3-110.9), and the median age was 57.2 years old (range, 28.0-85.9). The 5-year progression-free survival (PFS) for the 2023 system with molecular classification was 80.3% for stage I, 75.2% for stage II, 61.2% for stage III, and 22.2% for stage IV (p<0.001). Patients with the 2009 stage I and II disease were restaged using the 2023 system. In contrast, patients with stage III and IV disease were fixed in the 2009 and 2023 systems. Molecular classification downstaged 10 patients (71.4%) to IAmPOLEmut and upstaged 6 patients (37.5%) to IICmp53abn. The 2023 system with molecular classification was associated with PFS and overall survival (p<0.001 and p=0.038). Conclusion: The 2023 staging system for EC subdivided stages I and II compared to the 2009 system. The 2023 system with molecular classification is a good predictor of survival. [ABSTRACT FROM AUTHOR]

Background and Objectives: The new molecular classification of endometrial cancer continuously changes the management of the disease in everyday clinical practice. Recently, FIGO released a new staging system for endometrial cancer, which incorporates molecular substages and subdivides further early-stage disease. The aim of this study was to investigate the differences between the two FIGO staging systems and evaluate the prognostic precision of the new one. Materials and Methods: We retrospectively analyzed the records of patients with endometrial cancer that were fully treated in the 1st Department of Obstetrics & Gynecology, in 2012–2023. Patient characteristics, oncological outcome, and follow-up information were collected. The primary outcomes were the stage shifts and the survival data. Results: Sixty-seven (15.5%) patients had a stage shift and the majority of them concerned early-stage disease and specifically an upshift from 2009 stages IA and IB to 2023 stage IIC. Concerning survival, a better median and 5-year PFS was present in stage II disease, and when comparing the prognostic precision of the two FIGO staging systems no significant difference was present. Conclusions: The new 2023 FIGO staging system better distinguishes early-stage endometrial cancer into its prognostic groups and seems to be as precise as the old 2009 FIGO staging system. [ABSTRACT FROM AUTHOR]

Background: Using a previously unreported Peruvian registry of patients treated for early- stage non-small cell lung cancer (NSCLC), this study explored whether wedge resection and lobectomy were equivalent regarding survival and impact on radiologic-pathologic variables. Methods: This observational, analytical, longitudinal study used propensity scorematched (PSM) analysis of a single-center retrospective registry of 2,570 patients with pathologic stage I–II NSCLC who were treated with wedge resection (n=1,845) or lobectomy (n=725) during 2000–2020. After PSM, 650 cases were analyzed (resection, n=325; lobectomy, n=325) through preoperative and clinical variables, including patients with ≥1 lymph node removed. Kaplan-Meier curves and multivariable Cox proportional hazard models were created for 5-year overall survival (OS), disease-free survival (DFS), and locoregional- recurrence-free survival (LRFS). Results: The principal complication was operative pain persisting >7 days for lobectomy versus wedge resection (58% vs. 23%, p=0.034) and shorter hospital stays for resection than for lobectomy (5.3 days vs. 12.8 days, p=0.009). The 5-year OS (84.3% vs. 81.2%, p=0.09) and DFS (79.1% vs. 74.1%, p=0.07) were similar and statistically insignificant between resections and lobectomies, respectively. LRFS was worse overall following wedge resection than lobectomy (79.8% vs. 91.1%, p10 mm (90.9% vs. 87.3%, p

Introduction: During the Epithelial-mesenchymal Transition (EMT) process, cells undergo a transformation in which they lose their epithelial characteristics and acquire mesenchymal traits. This phenomenon was observed during the metastatic stage of neoplastic disorders, such as cervical cancer. Certain indicators are present during EMT. Aim: To evaluate the role of EMT in utero-cervical carcinoma by analysing the expressions of E-cadherin and vimentin in histopathologically confirmed cases. Materials and Methods: This retrospective cross-sectional study was conducted over a period of two years, from January 1, 2013, to December 31, 2014, at the Department of Pathology in a tertiary care centre and referral hospital. A total of 50 histopathologically confirmed cases of utero-cervical Squamous Cell Carcinoma (SCC) were included in this study. These case were analysed focusing on variables such as age, age at marriage, parity, family history, and hormonal intake. Tumour stages and grades were assessed alongside E-cadherin and vimentin expressions using Immunohistochemistry (IHC). Results were statistically analysed, and the database was created in MS Excel (Version 2007) and analysed using IBM Statistical Package for the Social Sciences (SPSS) Statistics software version 20.0, with significance set at p

Abstract Background The COVID-19 pandemic has had negative drawbacks on the healthcare system worldwide and on individuals other than those directly affected by the virus. Delays in cancer therapy and diagnosis have been reported in the literature. We hypothesized similar effects on patients with lung cancer at our center. Methods We retrospectively analyzed data of patients referred to our center with newly diagnosed lung cancer from 2018 to 2022. We considered distribution of UICC Stages and time from case presentation in our multidisciplinary tumor board or from therapeutic indication from treating physician to therapy initiation (surgery, systemic therapies and radiation) to define delays in diagnosis and treatment. Results 1020 patients with newly diagnosed lung cancer were referred to our center from 2018 to 2022, with a median of 206 cases yearly (range: 200–208). Cases with Stage IV in 2020–2022 were significantly higher than in 2018–2019 (57% vs. 46%, p = 0,001). 228 operative resections took place between 2018 and 2022, 100 from January 2018 to February 2020 and 128 from March 2020 to December 2022. Median time from presentation in our tumor board to resection was also significantly longer after the beginning of the pandemic than before (22 days vs. 15,5 days, p = 0,013). No significant delays were observed for administration of systemic treatment and initiation of radiation. Conclusions During the pandemic higher disease stages were reported for patients with lung cancer, yet there were no clinically relevant delays in treatment. In the context of the post-covid era new diagnostic strategies are necessary to facilitate early diagnosis of lung cancer. Despite the pandemic, for patients with suspicious symptoms prompt access to healthcare facilities is essential for early diagnosis.

Breast cancer (BC) is one of the most frequent cancers in females across the globe. Treatment recommendations for BC patients are primarily driven by patient age, staging and tumor molecular subtype. Thus, we updated the general overview of BC staging, molecular surrogates, and treatment choices for women >70 years based on a systematic study encompassing the years 2013-2023. A PRISMA guidelines and PICO framework were followed, and relevant research articles were searched using different data bases (Web of Sciences, PubMed, MEDLINE, and Scopus). Mixed Methods Appraisal Tool was used for studies quality assessment. The research articles that made it into the systematic review were compiled using qualitative criteria. In the meanwhile, heterogeneity was determined using meta-analysis with RevMan 5.4. We applied a random effects model with a 0.05 significance level. Overall, there were 4151 research articles, after screening only 17 articles with 39,906 patients were included. Conclusion: Elderly patients with breast cancer should be treated differently in an adapted way. The treatment should not be the same worldwide due to different health systems. Molecular surrogates are different in geriatric patients. Surgery is the best option for treatment in this subset of patients. We need to have therapeutic decision appointments for elderly patients with breast cancer. The guidelines and medical authority should be used in the best decision. [ABSTRACT FROM AUTHOR]

Introduction: Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. Materials and Methods: We conducted a literature search of PubMed and Google Scholar databases using the keywords: "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR" with variations of each. No language or time limits were imposed. All results were collected in a standardized manner, including country of origin, sample size, algorithm, objective of outcome, and model performance. The precision, recall, and the F1 score of studies were collected as a metric of model performance. Results: Of the 14 studies included in the review, 2 articles focused on documenting digital rectal examinations, 1 on identifying and quantifying pain secondary to PCa, 8 on extracting staging/grading information from clinical reports, with an emphasis on TNM-classification, risk stratification, and identifying metastasis, 2 articles focused on patient-centered post-treatment outcomes such as incontinence, erectile, and bowel dysfunction, and 1 on loneliness/social isolation following PCa diagnosis. All models showed moderate to high data annotation/extraction accuracy compared with the gold standard method of manual data extraction by chart review. Despite their potential, NLPs face challenges in handling ambiguous, institution-specific language and context nuances, leading to occasional inaccuracies in clinical data interpretation. Conclusion: NLP-based data extraction has effectively extracted various outcomes from PCa patients' EHRs. It holds the potential for automating outcome monitoring and data collection, resulting in time and labor savings. [ABSTRACT FROM AUTHOR]

Background: To assess the influence of diagnosis and referral provided by specialists in oral diagnosis on disease‐free survival and overall survival of patients with oral cancer. Methods: A cohort of 282 patients with oral cancer treated at a regional cancer hospital from 1998 to 2016 was analyzed retrospectively. The referral register of the patients was analyzed and assigned to two groups: (1) those referred by oral diagnosis specialists (n = 129), or (2) those referred by nonspecialized professionals (n = 153). The cancer treatment evolution was assessed from the patients' records, and the outcome was registered concerning cancer recurrence and death. Sociodemographic and clinicopathological variables were explored as predictors of disease‐free survival and overall survival. Results: Group 1 exhibited lower T stages and a reduced incidence of regional and distant metastases. Surgery was performed in 75.2% of cases in Group 1, while in Group 2, the rate was 60.8%. Advanced T stages and regional metastases reduced the feasibility of surgery. Higher TNM stages and tumor recurrence were associated with decreased disease‐free survival, while surgical intervention was a protective factor. Higher TNM stage had a negative impact on the overall survival. Conclusion: Specialized oral diagnosis did not directly impact disease‐free survival and overall survival and did not influence the indication of surgery in oral cancer; however, it was associated with the diagnosis of early tumors and better prognosis. [ABSTRACT FROM AUTHOR]

Background Patients with head and neck cancer present particularly considerable levels of emotional distress. However, the actual rates of clinically relevant mental health symptoms and disorders among this population remain unknown. Methods A Preferred Reporting Items for Systematic Review and Meta-Analyses and Meta-analyses of Observational Studies in Epidemiology–compliant systematic review and quantitative random-effects meta-analysis was performed to determine suicide incidence and the prevalence of depression, anxiety, distress, posttraumatic stress, and insomnia in this population. MEDLINE, Web of Science, Cochrane Central Register, KCI Korean Journal database, SciELO, Russian Science Citation Index, and Ovid-PsycINFO databases were searched from database inception to August 1, 2023 (PROSPERO: CRD42023441432). Subgroup analyses and meta-regressions were performed to investigate the effect of clinical, therapeutical, and methodological factors. Results A total of 208 studies (n = 654 413; median age = 60.7 years; 25.5% women) were identified. Among the patients, 19.5% reported depressive symptoms (95% confidence interval [CI] = 17% to 21%), 17.8% anxiety symptoms (95% CI = 14% to 21%), 34.3% distress (95% CI = 29% to 39%), 17.7% posttraumatic symptoms (95% CI = 6% to 41%), and 43.8% insomnia symptoms (95% CI = 35% to 52%). Diagnostic criteria assessments revealed lower prevalence of disorders: 10.3% depression (95% CI = 7% to 13%), 5.6% anxiety (95% CI = 2% to 10%), 9.6% insomnia (95% CI = 1% to 40%), and 1% posttraumatic stress (95% CI = 0% to 84.5%). Suicide pooled incidence was 161.16 per 100 000 individuals per year (95% CI = 82 to 239). Meta-regressions found a statistically significant higher prevalence of anxiety in patients undergoing primary chemoradiation compared with surgery and increased distress in smokers and advanced tumor staging. European samples exhibited lower prevalence of distress. Conclusions Patients with head and neck cancer presented notable prevalence of mental health concerns in all domains. Suicide remains a highly relevant concern. The prevalence of criteria-meeting disorders is significantly lower than clinically relevant symptoms. Investigating the effectiveness of targeted assessments for disorders in highly symptomatic patients is essential. [ABSTRACT FROM AUTHOR]

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.

Purpose: [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) has limitations in prostate cancer (PCa) detection owing to low glycolysis in the primary tumour. Recently, prostate-specific membrane antigen (PSMA) PET/CT has been useful for biochemical failure detection and radioligand therapy (RLT) guidance. However, few studies have evaluated its use in primary prostate tumours using PSMA and [18F]FDG PET/CT. This study aimed to evaluate [18F]PSMA-1007 and [18F]FDG PET/CT for primary tumour detection and understand the association of metabolic heterogeneity with clinicopathological characteristics at staging and postoperatively. Method: This prospective study included 42 index tumours (27 acinar and 15 ductal-dominant) in 42 patients who underwent [18F]PSMA-1007 and [18F]FDG PET/CT and subsequent radical prostatectomy. All patients were followed for a median of 26 mo, and serum prostate-specific antigen levels were measured every 3 mo to evaluate biochemical failure. One-way analysis of variance, Tukey's multiple comparison test, and Fisher's exact test were performed. Results: All 42 index tumours were detected on [18F]PSMA-1007 PET/CT, whereas only 15 were detected on [18F]FDG PET/CT (62.3% vs. 37.7%, p < 0.0001). A high SUVmax for [18F]PSMA-1007 was observed in tumours with high Gleason scores (GS 6–7 vs. GS 8–10; 12.1 vs. 20.1, p < 0.05). Tumours with [18F]FDG uptake were mostly ductal dominant (acinar-dominant 4/27; ductal-dominant; 11/15, p < 0.001), with lower [18F]PSMA-1007 uptake than tumours without [18F]FDG uptake (SUVmax 16.58 vs. 11.19, p < 0.001). There were 16.6% (7/42) of patients with pStage IV in whom the primary tumours were [18F]FDG positive. Biochemical failure was observed in 14.8% (4/27) of patients with [18F]FDG negative tumours but in 53.3% (8/15) of patients with [18F]FDG positive tumours (p = 0.013). Conclusions: [18F]PSMA-1007 PET/CT was superior to [18F]FDG PET/CT in detecting primary PCa. In contrast, tumours with [18F]FDG uptake are associated with larger size, a ductal-dominant type, and likely to undergo metastasis at staging and biochemical failure postoperatively. [ABSTRACT FROM AUTHOR]

Lung cancer, a predominant global health challenge, necessitates advanced diagnostic strategies to improve patient outcomes. This research explored the integration of machine learning algorithms, specifically Faster R-CNN, YOLO, and SSD, with lung imaging to enhance cancer detection and staging. Using a comprehensive dataset encompassing radiographs and CT scans of Atypical Carci- noids, Adenosquamous carcinoma, and Mucoepidermoid carcinoma, the models were trained, validated, and tested. The results spotlighted the potential of this approach, with the algorithms achieving an average accuracy of approximately 88% for cancer presence determination and 83% for stage identification. In comparison to Traditional CNN's results of 82% and 76% respectively, our methodologies showcased a marked improvement. These findings underscore the significance of machine learning in medical imaging, offering avenues for more precise, timely, and informed clinical decisions, ultimately setting a trajectory for improved patient care in the realm of lung cancer. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The American Joint Committee on Cancer (AJCC) staging system undergoes periodic revisions to maintain contemporary survival outcomes related to stage. Recently, the AJCC has developed a novel, systematic approach incorporating survival data to refine stage groupings. The objective of this study was to demonstrate data‐driven optimization of the version 9 AJCC staging system for anal cancer assessed through a defined validation approach. METHODS: The National Cancer Database was queried for patients diagnosed with anal cancer in 2012 through 2017. Kaplan–Meier methods analyzed 5‐year survival by individual clinical T category, N category, M category, and overall stage. Cox proportional hazards models validated overall survival of the revised TNM stage groupings. RESULTS: Overall, 24,328 cases of anal cancer were included. Evaluation of the 8th edition AJCC stage groups demonstrated a lack of hierarchical prognostic order. Survival at 5 years for stage I was 84.4%, 77.4% for stage IIA, and 63.7% for stage IIB; however, stage IIIA disease demonstrated a 73.0% survival, followed by 58.4% for stage IIIB, 59.9% for stage IIIC, and 22.5% for stage IV (p <.001). Thus, stage IIB was redefined as T1‐2N1M0, whereas Stage IIIA was redefined as T3N0‐1M0. Reevaluation of 5‐year survival based on data‐informed stage groupings now demonstrates hierarchical prognostic order and validated via Cox proportional hazards models. CONCLUSION: The 8th edition AJCC survival data demonstrated a lack of hierarchical prognostic order and informed revised stage groupings in the version 9 AJCC staging system for anal cancer. Thus, a validated data‐driven optimization approach can be implemented for staging revisions across all disease sites moving forward. Evaluation of the 8th edition American Joint Committee on Cancer staging system demonstrated a lack of hierarchical prognostic order for anal cancer. Thus, the version 9 American Joint Committee on Cancer staging system for anal cancer was revised through a validated and data‐driven approach and should similarly be applied across all disease sites moving forward. [ABSTRACT FROM AUTHOR]

Objective: To evaluate trends in the incidence and survival outcomes of endometrial cancer (EC) based on the year of diagnosis, stage, age, and histologic types. Methods: Women with primary EC diagnosed between 1999 and 2018, and who were followed up with until 2019, were identified from the Korea Central Cancer Registry using the International Classification of Diseases, 10th revision. The age-standardized rates (ASRs) of incidence, annual percent changes (APCs), and survival were estimated according to age, stage, histology, and year of diagnosis. Results: The ASR for EC increased from 2.38 per 100,000 in 1999 to 7.29 per 100,000 in 2018 across all histologic types (APCs of 9.82, 15.97, and 7.73 for endometrioid, serous, and clear cell, respectively, p<0.001). There were significant differences in the 5-year survival rates based on histology (90.9%, 55.0%, and 68.5% for endometrioid, serous, and clear cell, respectively, p<0.001), stage (93.4%, 77.0%, and 31.0% for localized, regional, and distant, respectively, p<0.001), and age (93.0% for <50 years and 80.6% for =50 years, p<0.001). The 5-year survival was significantly better in the group diagnosed between 2000 and 2018 (85.9%) than that in the 1999-2008 group (83.3%) (p<0.001). This trend was only observed for endometrioid cancer (p<0.001). Conclusion: The incidence of EC increased across the all 3 subtypes. Survival of patients with endometrioid histology improved over the past two decades, but remained static for serous or clear cell histology. Healthcare strategies to prevent EC incidence in at-risk populations and apply effective treatments for high-risk histology are needed. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the differences in baseline staging of anal squamous cell carcinoma based on CT, MRI, and PET/CT, and the resultant impact on the radiation plan. Methods: This retrospective study included consecutive patients with anal squamous cell carcinoma who underwent baseline pelvic MRI, CT, and PET/CT (all examinations within 3 weeks of each other) from January 2010 to April 2020. CTs, MRIs, and PET/CTs were re-interpreted by three separate radiologists. Several imaging features were assessed; tumor stage was determined based on the eight edition of the American Joint Committee on Cancer (AJCC) staging manual; and T (tumor), N (node), and M (metastasis) categories were determined based on National Comprehensive Cancer Network (NCCN) guidelines. Radiologist assessments were then randomly presented to a radiation oncologist who formulated the radiation plan in a blinded fashion. Results: Across 28 patients (median age, 62 years [range, 31–78], T-category classification was significantly different on PET/CT compared to MRI and CT (p = 0.037 and 0.031, respectively). PET/CT staged a higher proportion of patients with T1/T2 disease (16/28, 57%) compared to MRI (11/28, 39%) and CT (10/28, 36%). MRI staged a higher proportion of patients with T3/T4 disease (14/28, 50%) compared to CT (12/28, 43%) and PET/CT (11/28, 39%). However, there was no significant difference between the three imaging modalities in terms of either N-category, AJCC staging, or NCCN TNM group classification, or in treatment planning. Conclusion: Our exploratory study showed that MRI demonstrated a higher proportion of T3/T4 tumors, while PET/CT demonstrated more T1/T2 tumors; however, MRI, CT, and PET/CT did not show any significant differences in AJCC and TNM group categories, nor was there any significant difference in treatment doses between them when assessed independently by an experienced radiation oncologist. [ABSTRACT FROM AUTHOR]

Background: Incidental findings (IFs) are commonly seen in staging rectal magnetic resonance imaging (MRI) scans. Their prevalence and clinical significance have not been previously documented. Purpose: To assess the prevalence, clinical significance, and outcomes of incidental findings in MRI scans performed for the staging of rectal cancer. Material and Methods: A retrospective study was performed at a tertiary colorectal imaging institution. Consecutive MRI rectal staging scans with correlative pathology confirmed primary rectal cancer between March 2014 and March 2021 were identified. The respective imaging reports were reviewed for IFs, which were classified as high, moderate, and low, according to their clinical significance. Medical records were reviewed to assess the outcomes of the highly significant IFs. Results: There were 266 eligible patients (97 women; mean age = 64.2 years) during the study period. A total of 120 (45%) patients did not have any IFs. A total of 238 IFs in 146 (55%) patients were found. There were 21 (9%) IFs of high clinical significance, 122 (51%) of moderate clinical significance, and 95 (40%) of low clinical significance. The prostate and uterus had the most IFs of high clinical significance, two of which were subsequently pathology confirmed as prostate adenocarcinomas. Conclusion: IFs were seen in more than half of the staging MRI scans in rectal cancer but less than 10% of these were of high clinical significance. The results of this study highlight the range of potential IFs and can guide future research assessing the potential impact of these IFs on patients and the healthcare system. [ABSTRACT FROM AUTHOR]

BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information. [ABSTRACT FROM AUTHOR]

Abstract Background Magnetic resonance imaging (MRI) is recommended in patients with upper tract urothelial carcinoma (UTUC) only when computed tomography (CT) is contraindicated. However, CT does not allow distinguishing ureter wall layers, making impossible to assess muscle invasion, a factor contributing to differentiate high- from low-risk UTUCs, which require different therapeutic approaches. We investigated the feasibility of MRI assessment of UTUC muscle invasion. Methods From June 2022 to March 2023, we prospectively enrolled patients suspected of UTUC, i.e., with positive urinary tract ultrasound and/or ureteroscopy, or positive urinary cytology and/or hematuria but negative cystoscopy and bladder ultrasound at two Italian centers. They underwent CT followed by MRI (≤ 24 h apart), independently reported by two experienced radiologists, blinded from histopathology results. After imaging confirmation, they all underwent nephroureterectomy and histopathology analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. Results Thirty-nine lesions were detected in 30 patients on both CT and MRI. Muscle-invasive UTUC prevalence was 81% (21/26) among patients with MRI suspicion and 8% (1/13) among those without MRI suspicion (p

Abstract Background The predictive correlation of tumor depth of invasion changes after neoadjuvant therapy, and the 8th American Joint Committee on Cancer (AJCC) ypTNM system for gastric cancer may not accurately predict patient prognosis following neoadjuvant therapy. Methods A retrospective analysis was conducted on a total of 258 patients who underwent radical surgery for gastric cancer after neoadjuvant therapy. The Newstage system was established based on tumor regression grade and pathological lymph node status. The 3-year survival rates of patients classified by the Newstage system were compared with those classified by the AJCC ypTNM system. Results In a cohort of 258 patients, the 3-year overall survival rates based on the Newstage system were: (I) 94.6%, (II) 79.3%, (III) 54.5%, and (IV) 30.2%. The Newstage system exhibited a lower Akaike information criterion value (902.57 vs. 912.03). Additionally, the area under the ROC curve (0.756 vs. 0.733) and the C-index (0.731 vs. 0.718) was higher than the AJCC ypTNM system. Furthermore, a multivariate analysis indicated that the Newstage system was an independent prognostic factor (p = 0.001). Conclusion The Newstage system exhibits superior predictive performance in estimating survival rates for neoadjuvant therapy in gastric cancer. It also functions as an independent prognostic factor.

Simple Summary: This research studied how three types of proteins, called interleukins (namely 8, 17A and 33), are present in the blood of healthy people and people with colon cancer. Colon cancer is a disease that affects the large intestine. The paper looked at 82 people, 42 of whom had colon cancer and 40 of whom did not. The researchers divided the cancer patients into four groups based on how severe their cancer was. The study measured the amount of interleukins in the blood of each person using a special test. The paper analyzed the results of the test, considering the age, gender, and cancer stage of each person. The results showed that younger people, and those with less severe cancer had more interleukins in their blood. The paper also found that two of the interleukins (8 and 17A) were higher in the cancer group, while one of them (33) was higher in the healthy group. The study also found that the interleukins were related to each other in both groups. This research concluded that the interleukins might help detect colon cancer and predict how it will progress. This paper could help improve the diagnosis and treatment of colon cancer. This research investigated the serum levels of three interleukins (IL8, IL17A, and IL33) and the possible relationships between them in healthy people and colon cancer patients at different stages. This study involved 82 participants, 42 of whom had colon cancer and 40 were healthy individuals. The cancer patients were classified into four groups according to the TNM staging classification of colon and rectal cancer. Serum levels of the interleukins were measured by the ELISA test. The data were analyzed statistically to compare the demographic characteristics, the interleukin levels across cancer stages, and the correlation between interleukins in both groups. The results showed that women had more early-stage colon cancer diagnoses, while men had more advanced-stage cancer diagnoses. Stage two colon cancer was more common in older people. Younger people, men, and those with early-stage colon cancer had higher levels of interleukins. The levels of IL8 and IL17A were higher in the cancer group, while the level of IL33 was higher in the healthy group. There was a strong correlation between IL8 and IL17A levels in both groups (p = 0.001). IL17A influenced the level of IL33 in the cancer group (p = 0.007). This study suggested that cytokine variation profiles could be useful for detecting colon cancer and predicting its outcome. [ABSTRACT FROM AUTHOR]

Analyzing highly individual-specific genomic data to understand genetic interactions in cancer development is still challenging, with significant implications for the discovery of individual biomarkers as well as personalized medicine. With the rapid development of deep learning, graph neural networks (GNNs) have been employed to analyze a wide range of biomolecular networks. However, many neural networks are limited to black box models, which are only capable of making predictions, and they are often challenged to provide reliable biological and clinical insights. In this research, for sample-specific networks, a novel end-to-end hierarchical graph neural network with interpretable modules is proposed, which learns structural features at multiple scales and incorporates a soft mask layer in extracting subgraphs that contribute to classification. The perturbations caused by the input graphs' deductions are used to evaluate key gene clusters, and the samples are then grouped into classes to produce both sample- and stage-level explanations. Experiments on four gene expression datasets from The Cancer Genome Atlas (TCGA) show that the proposed model not only rivals the advanced GNN methods in cancer staging but also identifies key gene clusters that have a great impact on classification confidence, providing potential targets for personalized medicine. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) is recommended in patients with upper tract urothelial carcinoma (UTUC) only when computed tomography (CT) is contraindicated. However, CT does not allow distinguishing ureter wall layers, making impossible to assess muscle invasion, a factor contributing to differentiate high- from low-risk UTUCs, which require different therapeutic approaches. We investigated the feasibility of MRI assessment of UTUC muscle invasion. Methods: From June 2022 to March 2023, we prospectively enrolled patients suspected of UTUC, i.e., with positive urinary tract ultrasound and/or ureteroscopy, or positive urinary cytology and/or hematuria but negative cystoscopy and bladder ultrasound at two Italian centers. They underwent CT followed by MRI (≤ 24 h apart), independently reported by two experienced radiologists, blinded from histopathology results. After imaging confirmation, they all underwent nephroureterectomy and histopathology analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. Results: Thirty-nine lesions were detected in 30 patients on both CT and MRI. Muscle-invasive UTUC prevalence was 81% (21/26) among patients with MRI suspicion and 8% (1/13) among those without MRI suspicion (p < 0.001). Considering the assessment of muscle-layer invasion, the more experienced reader achieved 95% sensitivity (95% confidence interval 82−100), 71% specificity (47−88), 81% PPV (63−93), 92% NPV (70−100), 85% accuracy (67−96), and 0.84 AUC (0.70−0.98). Inter-reader agreement was substantial (κ = 0.73). Conclusions: MRI showed a promising diagnostic performance for the assessment of UTUC risk of muscle invasion. Relevance statement: Resulting feasible both in technical and clinical terms, MRI could be helpful for upper tract urothelial carcinomas pre-operative risk stratification, to allow a personalized patients' management. These results play in favor of promoting preoperative MRI for UTUC, as already proven for bladder cancer. Key points: • Muscle invasion is a crucial information for tailored treatments of upper tract urothelial carcinomas. • CT does not distinguish ureter wall layers, making muscle invasion risk assessment not feasible. • MRI was shown to reliably diagnose muscle-layer invasion by upper tract urothelial carcinomas (sensitivity 95%, specificity 71%). [ABSTRACT FROM AUTHOR]

Background: The predictive correlation of tumor depth of invasion changes after neoadjuvant therapy, and the 8th American Joint Committee on Cancer (AJCC) ypTNM system for gastric cancer may not accurately predict patient prognosis following neoadjuvant therapy. Methods: A retrospective analysis was conducted on a total of 258 patients who underwent radical surgery for gastric cancer after neoadjuvant therapy. The Newstage system was established based on tumor regression grade and pathological lymph node status. The 3-year survival rates of patients classified by the Newstage system were compared with those classified by the AJCC ypTNM system. Results: In a cohort of 258 patients, the 3-year overall survival rates based on the Newstage system were: (I) 94.6%, (II) 79.3%, (III) 54.5%, and (IV) 30.2%. The Newstage system exhibited a lower Akaike information criterion value (902.57 vs. 912.03). Additionally, the area under the ROC curve (0.756 vs. 0.733) and the C-index (0.731 vs. 0.718) was higher than the AJCC ypTNM system. Furthermore, a multivariate analysis indicated that the Newstage system was an independent prognostic factor (p = 0.001). Conclusion: The Newstage system exhibits superior predictive performance in estimating survival rates for neoadjuvant therapy in gastric cancer. It also functions as an independent prognostic factor. [ABSTRACT FROM AUTHOR]

Purpose: The diagnostic accuracy of computed tomography urography for upper tract urothelial carcinoma is high; however, difficulties are associated with precisely assessing the T stage. Preoperative tumor staging has an impact on treatment options for upper tract urothelial carcinoma. We herein attempted to identify preoperative factors that predict pathological tumor up-staging, which will facilitate the selection of treatment strategies. Materials and methods: We retrospectively identified 148 patients with upper tract urothelial carcinoma who underwent computed tomography urography preoperatively followed by radical nephroureterectomy without preoperative chemotherapy at our institution between 2000 and 2021. Preoperative factors associated with cT2 or lower to pT3 up-staging were examined using a multivariate logistic regression analysis. Results: Ninety out of 148 patients were diagnosed with cT2 or lower, and 22 (24%) were up-staged to pT3. A multivariate analysis identified a positive voided urine cytology (HR 4.69, p = 0.023) and tumor length ≥ 3 cm (HR 6.33, p = 0.003) as independent predictors of pathological tumor up-staging. Conclusions: Patients diagnosed with cT2 or lower, but with preoperative positive voided urine cytology and/or tumor diameter ≥ 3 cm need to be considered for treatment as cT3. [ABSTRACT FROM AUTHOR]

Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) based on the venous-phase CT images and explored the performance of these models in stratifying patients with laryngeal carcinoma into stage I–II and stage III–IV, and also compared these models with radiologists. Three hundreds and nineteen patients with pathologically confirmed laryngeal carcinoma were randomly divided into a training set (n = 223) and a test set (n = 96). In the training set, the radiomics features with inter- and intraclass correlation coefficients (ICCs) > 0.75 were screened by Spearman correlation analysis and recursive feature elimination (RFE); then support vector machine (SVM) classifier was applied to develop the radiomics model. The DL model was built using ResNet 18 by the cropped 2D regions of interest (ROIs) in the maximum tumor ROI slices and the last fully connected layer of this network served as the DL feature extractor. Finally, a combined model was developed by pooling the radiomics features and extracted DL features to predict the staging. The area under the curves (AUCs) for radiomics model, DL model, and combined model in the test set were 0.704 (95% confidence interval [CI]: 0.588–0.820), 0.724 (95% CI: 0.613–0.835), and 0.849 (95% CI: 0.755–0.943), respectively. The combined model outperformed the radiomics model and the DL model in discriminating stage I–II from stage III–IV (p = 0.031 and p = 0.020, respectively). Only the combined model performed significantly better than radiologists (p < 0.050 for both). The combined model can help tailor the therapeutic strategy for laryngeal carcinoma patients by enabling more accurate preoperative staging. [ABSTRACT FROM AUTHOR]

Prompt diagnosis and surgical management of melanoma strongly impact prognosis. Considering the limited resources, emergency closures, and staffing shortages during the COVID-19 pandemic in Canada, our institution implemented a dedicated care pathway to prioritize cancer surgeries. We aim to assess whether this strategy was effective at preventing surgical wait time delays and upstaging of melanoma. We retrospectively collected data of patients aged ≥18 years with biopsy-proven primary melanoma who underwent wide local excision (WLE) ± sentinel lymph node biopsy (SLNB) between 1 March 2018–29 February 2020 (pre-pandemic) and 1 March 2020–22 March 2022 (pandemic). Patients with distant metastasis, recurrence, in situ disease, and unknown primary were excluded. Wait time from consult to surgery, tumour (T) and nodal (N) stage, and overall stage were collected. Results: We included 419 patients [pre-pandemic (n = 204) and pandemic (n = 215)]. Median wait time (days) [interquartile range] to surgery was 36 [22–48] pre-pandemic and 35 [24–49] during the pandemic (p = 0.888). There were no differences found in T stage (p = 0.060), N stage (p = 0.214), or overall melanoma stage (p = 0.192). We highlight the importance of streamlining melanoma surgery during a pandemic. As the need arises to meet surgical backlogs including benign surgery, dedicated cancer surgery should maintain a priority to not negatively affect cancer outcomes.

Background and Objectives: The new molecular classification of endometrial cancer continuously changes the management of the disease in everyday clinical practice. Recently, FIGO released a new staging system for endometrial cancer, which incorporates molecular substages and subdivides further early-stage disease. The aim of this study was to investigate the differences between the two FIGO staging systems and evaluate the prognostic precision of the new one. Materials and Methods: We retrospectively analyzed the records of patients with endometrial cancer that were fully treated in the 1st Department of Obstetrics & Gynecology, in 2012–2023. Patient characteristics, oncological outcome, and follow-up information were collected. The primary outcomes were the stage shifts and the survival data. Results: Sixty-seven (15.5%) patients had a stage shift and the majority of them concerned early-stage disease and specifically an upshift from 2009 stages IA and IB to 2023 stage IIC. Concerning survival, a better median and 5-year PFS was present in stage II disease, and when comparing the prognostic precision of the two FIGO staging systems no significant difference was present. Conclusions: The new 2023 FIGO staging system better distinguishes early-stage endometrial cancer into its prognostic groups and seems to be as precise as the old 2009 FIGO staging system.

Abstract Analyzing highly individual-specific genomic data to understand genetic interactions in cancer development is still challenging, with significant implications for the discovery of individual biomarkers as well as personalized medicine. With the rapid development of deep learning, graph neural networks (GNNs) have been employed to analyze a wide range of biomolecular networks. However, many neural networks are limited to black box models, which are only capable of making predictions, and they are often challenged to provide reliable biological and clinical insights. In this research, for sample-specific networks, a novel end-to-end hierarchical graph neural network with interpretable modules is proposed, which learns structural features at multiple scales and incorporates a soft mask layer in extracting subgraphs that contribute to classification. The perturbations caused by the input graphs' deductions are used to evaluate key gene clusters, and the samples are then grouped into classes to produce both sample- and stage-level explanations. Experiments on four gene expression datasets from The Cancer Genome Atlas (TCGA) show that the proposed model not only rivals the advanced GNN methods in cancer staging but also identifies key gene clusters that have a great impact on classification confidence, providing potential targets for personalized medicine.

The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival data were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and divided into the training set (131,725 cases) and the validation set (15,683 cases). Five prognostic factors were studied: Ann Arbor stage, type, site, age, and sex. EACCD was applied to the training set to produce a prognostic system, called an EACCD system, for convenience. The EACCD system stratified patients into eight prognostic groups with well-separated survival curves. These eight prognostic groups had significantly higher accuracies in survival prediction than the 24 Ann Arbor substages. A higher-risk group in the EACCD system roughly corresponds to a higher Ann Arbor substage. The proposed system shows a good performance in risk stratification and survival prediction on both the training and the validation sets. The EACCD system expands the traditional Ann Arbor staging system by leveraging additional prognostic information and is expected to advance treatment management for lymphoma patients.

This retrospective study aimed to evaluate the consistency of transrectal contrast-enhanced ultrasonography (TR-CEUS) with post-operative pathology and the value of contrast-enhanced ultrasonography (CEUS) in staging surgically treated cervical cancer when combined with conventional ultrasonography (US). From October 2020 to March 2023, hospitalized patients with stage IB and II cervical cancer confirmed by total hysterectomy were consecutively enrolled. The standard images of US and CEUS by transabdominal (TA-US/CEUS) and transrectal (TR-US/CEUS) approaches and magnetic resonance imaging (MRI) were acquired, on which the size and stage of the tumors were evaluated, and the consistency of results with the pathological specimen was analyzed. Thirty-nine patients with cervical cancer were finally enrolled in this study. The results showed that CEUS significantly improved the reliability of TA-US in evaluating tumor diameter; the intraclass correlation coefficient (ICC) was from 0.672 to 0.735. TR-US indicated good reliability with or without the addition of CEUS (ICC = 0.796 and 0.780). In terms of tumor staging, CEUS improved the consistency of transabdominal (weighted κ values from 0.689 to 0.731) and transrectal staging of tumors (κ from 0.758 to 0.785), and the staging of TR-US combined with TR-CEUS had the highest consistency with post-operative results, similar to MRI (κ , respectively 0.785 and 0.789). CEUS can reflect the heterogeneity of the tumor. Heterogeneous enhancement and perfusion defects were more common in >2 cm cervical cancer (50%, 20/40 and 52.5%, 21/40), respectively, and perfusion defects were more common in moderately to poorly differentiated tumors (66.67%, 20/30). For stage IB and IIA cervical cancer, CEUS can aid in assessing the International Federation for Gynecology and Obstetrics staging of tumors alongside TA-US and TR-US. The combination of TR-US and TR-CEUS has shown good consistency with pathology in the staging of cervical cancer, comparable to that of MRI. [ABSTRACT FROM AUTHOR]

Background: The Toronto Paediatric Cancer Stage Guidelines are a compendium of staging systems developed to facilitate collection of consistent and comparable data on stage at diagnosis for childhood cancers by cancer registries. Material and Methods: This retrospective, observational cohort study investigated changes in stagespecific incidence and survival for children diagnosed between 2000-2008 compared to 2009-2017 using the population-based Australian Childhood Cancer Registry. Information on mortality for each patient was available to 31st December 2020. Shifts in incidence by stage were evaluated using chisquare tests, and differences in stage-specific five-year observed survival for all causes of death over time were assessed using flexible parametric models. Results: Stage was assigned according to the Toronto Guidelines for 96% (n=7944) of the total study cohort (n=8292). Changes in the distribution of incidence by stage between the two diagnosis periods were observed for retinoblastoma, with stage 0 increasing from 26% to 37% of cases (p=0.02), and hepatoblastoma, with metastatic disease increasing from 22% to 39% of cases (p = 0.04). There were large gains in stage-specific survival over time for stage IV rhabdomyosarcoma (five-year adjusted mortality hazard ratio for 2009-2017 compared to 2000-2008 of 0.38, 95% CI 0.19-0.77; p=0.01), stage M3 for medulloblastoma (HR = 0.41, 95% CI 0.21-0.79; p=0.01) and metastatic neuroblastoma excluding stage MS (HR = 0.61, 95% CI 0.44-0.84; p<0.01). Conclusion: These results indicate that improvements in childhood cancer survival in Australia are most likely due to refined management rather than changes in stage at diagnosis, particularly for metastatic solid tumours. Wide international uptake of the Toronto Guidelines will allow comprehensive evaluation of differences in survival between countries. Abbreviations: ACCR: Australian Childhood Cancer Registry; CI: confidence interval; CNS: central nervous system; HR: hazard ratio; ICCC-3: International Classification of Childhood Cancers, 3rd edition; RCC: renal cell carcinoma; TNM: tumour-node-metastasis staging system. [ABSTRACT FROM AUTHOR]

The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival data were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and divided into the training set (131,725 cases) and the validation set (15,683 cases). Five prognostic factors were studied: Ann Arbor stage, type, site, age, and sex. EACCD was applied to the training set to produce a prognostic system, called an EACCD system, for convenience. The EACCD system stratified patients into eight prognostic groups with well-separated survival curves. These eight prognostic groups had significantly higher accuracies in survival prediction than the 24 Ann Arbor substages. A higher-risk group in the EACCD system roughly corresponds to a higher Ann Arbor substage. The proposed system shows a good performance in risk stratification and survival prediction on both the training and the validation sets. The EACCD system expands the traditional Ann Arbor staging system by leveraging additional prognostic information and is expected to advance treatment management for lymphoma patients. [ABSTRACT FROM AUTHOR]

Prompt diagnosis and surgical management of melanoma strongly impact prognosis. Considering the limited resources, emergency closures, and staffing shortages during the COVID-19 pandemic in Canada, our institution implemented a dedicated care pathway to prioritize cancer surgeries. We aim to assess whether this strategy was effective at preventing surgical wait time delays and upstaging of melanoma. We retrospectively collected data of patients aged ≥18 years with biopsy-proven primary melanoma who underwent wide local excision (WLE) ± sentinel lymph node biopsy (SLNB) between 1 March 2018–29 February 2020 (pre-pandemic) and 1 March 2020–22 March 2022 (pandemic). Patients with distant metastasis, recurrence, in situ disease, and unknown primary were excluded. Wait time from consult to surgery, tumour (T) and nodal (N) stage, and overall stage were collected. Results: We included 419 patients [pre-pandemic (n = 204) and pandemic (n = 215)]. Median wait time (days) [interquartile range] to surgery was 36 [22–48] pre-pandemic and 35 [24–49] during the pandemic (p = 0.888). There were no differences found in T stage (p = 0.060), N stage (p = 0.214), or overall melanoma stage (p = 0.192). We highlight the importance of streamlining melanoma surgery during a pandemic. As the need arises to meet surgical backlogs including benign surgery, dedicated cancer surgery should maintain a priority to not negatively affect cancer outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Many advances in the understanding of the pathologic and molecular features of endometrial cancer have occurred since the FIGO staging was last updated in 2009. Substantially more outcome and biological behavior data are now available regarding the several histological types. Molecular and genetic findings have accelerated since the publication of The Cancer Genome Atlas (TCGA) data and provide improved clarity on the diverse biological nature of this collection of endometrial cancers and their differing prognostic outcomes. The goals of the new staging system are to better define these prognostic groups and create substages that indicate more appropriate surgical, radiation, and systemic therapies. Methods: The FIGO Women's Cancer Committee appointed a Subcommittee on Endometrial Cancer Staging in October 2021, represented by the authors. Since then, the committee members have met frequently and reviewed new and established evidence on the treatment, prognosis, and survival of endometrial cancer. Based on these data, opportunities for improvements in the categorization and stratification of these factors were identified in each of the four stages. Data and analyses from the molecular and histological classifications performed and published in the recently developed ESGO/ESTRO/ESP guidelines were used as a template for adding the new subclassifications to the proposed molecular and histological staging system. Results: Based on the existing evidence, the substages were defined as follows: S tage I (IA1): non-aggressive histological type of endometrial carcinoma limited to a polyp or confined to the endometrium; (IA2) non-aggressive histological types of endometrium involving less than 50% of the myometrium with no or focal lymphovascular space invasion (LVSI) as defined by WHO criteria; (IA3) low-grade endometrioid carcinomas limited to the uterus with simultaneous low-grade endometrioid ovarian involvement; (IB) non-aggressive histological types involving 50% or more of the myometrium with no LVSI or focal LVSI; (IC) aggressive histological types, i.e. serous, high-grade endometrioid, clear cell, carcinosarcomas, undifferentiated, mixed, and other unusual types without any myometrial invasion. S tage II (IIA): non-aggressive histological types that infiltrate the cervical stroma; (IIB) nonaggressive histological types that have substantial LVSI; or (IIC) aggressive histological types with any myometrial invasion. S tage III (IIIA): differentiating between adnexal versus uterine serosa infiltration; (IIIB) infiltration of vagina/parametria and pelvic peritoneal metastasis; and (IIIC) refinements for lymph node metastasis to pelvic and para-aortic lymph nodes, including micrometastasis and macrometastasis. S tage IV (IVA): locally advanced disease infiltrating the bladder or rectal mucosa; (IVB) extrapelvic peritoneal metastasis; and (IVC) distant metastasis. The performance of complete molecular classification ( POLEmut, MMRd, NSMP, p53abn) is encouraged in all endometrial cancers. If the molecular subtype is known, this is recorded in the FIGO stage by the addition of "m" for molecular classification, and a subscript indicating the specific molecular subtype. When molecular classification reveals p53abn or POLEmut status in Stages I and II, this results in upstaging or downstaging of the disease (IICmp53abn or IAmPOLEmut). Summary: The updated 2023 staging of endometrial cancer includes the various histological types, tumor patterns, and molecular classification to better reflect the improved understanding of the complex nature of the several types of endometrial carcinoma and their underlying biologic behavior. The changes incorporated in the 2023 staging system should provide a more evidence-based context for treatment recommendations and for the more refined future collection of outcome and survival data. [ABSTRACT FROM AUTHOR]

Radiologic imaging, especially MRI, has long been the mainstay for rectal cancer staging and patient selection for neoadjuvant therapy prior to surgical resection. In contrast, colonoscopy and CT have been the standard for colon cancer diagnosis and metastasis staging with T and N staging often performed at the time of surgical resection. With recent clinical trials exploring the expansion of the use of neoadjuvant therapy beyond the anorectum to the remainder of the colon, the current and future state of colon cancer treatment is evolving with a renewed interest in evaluating the role radiology may play in the primary T staging of colon cancer. The performance of CT, CT colonography, MRI, and FDG PET-CT for colon cancer staging will be reviewed. N staging will also be briefly discussed. It is expected that accurate radiologic T staging will significantly impact future clinical decisions regarding the neoadjuvant versus surgical management of colon cancer. [ABSTRACT FROM AUTHOR]

Objectives The aims of this study were to assess the feasibility of single-port laparoscopic surgical staging (SPLS) in early ovarian cancer and to compare the surgical outcomes of SPLS with those of staging laparotomy.MethodsBetween January 2014 and December 2018, 40 patients underwent SPLS and 41 patients underwent staging laparotomy at Yonsei Cancer Center. The patients were diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage I ovarian cancer. Variables such as patient age, body mass index (BMI), tumor size, FIGO stage, and perioperative surgical outcomes and survival outcomes of SPLS and laparotomy were compared.ResultsThe total operation time was similar between the 2 groups (SPLS: 201.4 vs. laparotomy: 203.0 minutes, P=0.806). The median tumor diameters in the SPLS and laparotomy groups were 11.0 (2.5-28 cm) and 15.4 (6-40 cm), respectively (P=0.001). The SPLS group had lower tumor spillage rate (5.0% vs. 19.5%, P=0.047), less intraoperative blood loss (102.0 vs. 371.5 mL, P

Backgrounds: Magnetic resonance imaging is the primary method for local staging in rectal cancer patients. Administration of intravenous (IV) hyoscine butylbromide is thought to improve accuracy, but there are contraindications and potential adverse effects. The aim was to assess the efficacy of IV hyoscine butylbromide on the accuracy of MRI rectal cancer staging of T2 and T3 rectal cancers. Methods: A retrospective cohort study was carried out on patients prospectively recorded on the Cabrini Monash colorectal neoplasia database. A total of 74 patients (53 males, 21 females) MRI pelvis and rectums with antispasmodics were performed at multiple centres in the pre‐operative setting between 2010 and 2016. Each patient underwent total mesorectal excision of rectal cancer. The excision specimens were assessed and given a pathological TNM stage, which was considered the reference standard. Results: There was no statistically significant impact on the overall accuracy of MRI rectal cancer staging between patient groups who received IV hyoscine butylbromide and groups who did not receive IV hyoscine butylbromide. The accuracy of T2 and T3 staged rectal cancers was more likely to be correct (compared with T1 cancers) with the administration of IV hyoscine butylbromide. Still, there was no improvement in the accuracy of N‐staging. Conclusion: Given the potential side effects and adverse outcomes of IV anti‐spasmodic agents, department protocols may need to be re‐assessed regarding the prescription of these medications for MRI rectal cancer staging. [ABSTRACT FROM AUTHOR]