Risk Classification of Patients With Advanced Urothelial Carcinoma Treated With Enfortumab Vedotin.

Background/Aim: Enfortumab Vedotin (EV) is a widely used antibody-drug conjugate for patients with advanced urothelial carcinoma (UC) who have previously been treated with platinum-based chemotherapy and immune checkpoint inhibitors. However, limited information is currently available on prognostic factors and risk classification. Therefore, the present study attempted to identify clinical factors that predict outcomes in patients with advanced UC treated with EV and to develop a novel risk classification model. Patients and Methods: We conducted a multicenter retrospective study including patients with advanced UC treated with EV. Oncological outcomes were assessed using progression-free survival (PFS) and overall survival (OS), and prognostic factors for PFS and OS were investigated. We then examined the usefulness of risk classification based on the prognostic factors identified. Results: Median PFS and OS were 7.1 and 16.3 months, respectively. High C-reactive protein levels (CRP level ≥0.5 mg/dl) and hypercalcemia (corrected calcium level >10.2 mg/dl) were identified as prognostic factors for PFS (p=0.012 and p=0.003, respectively) and OS (p=0.035 and p<0.001, respectively). We then divided patients into three risk groups: no prognostic factors group, one prognostic factor group, and two prognostic factors group. Significant differences were observed in PFS and OS among the three groups (p<0.001 and p<0.001, respectively) and c-indices were 0.766 for PFS and 0.800 for OS. Conclusion: The risk classification using CRP and hypercalcemia is useful for predicting the outcomes of patients with advanced UC treated with EV.

Clinical outcomes analysis of image-guided brachytherapy as definitive treatment for inoperable endometrial cancer.

OBJECTIVES: This study evaluates the efficacy and toxicity of image-guided brachytherapy combined with or without external beam radiotherapy (IGBT ± EBRT) as definitive treatment for patients with inoperable endometrial cancer (IOEC), in addition to establishing a risk classification to predict prognosis. METHODS: Fifty-one IOEC patients who underwent IGBT ± EBRT at Peking Union Medical College Hospital from January 2012 to December 2021 were retrospectively analyzed, of which 42 patients (82.4%) were treated with IGBT + EBRT and 9 patients (17.6%) with IGBT alone. Establishing risk classification based on FIGO 2009 staging and biopsy pathology, stage III/IV, non-endometrioid, or Grade 3 endometrioid cancer were included in the high-risk group (n = 25), and stage I/II with Grade 1-2 endometrioid cancer was included in the low-risk group (n = 26). RESULTS: The median follow-up time was 58.0 months (IQR, 37.0-69.0). Clinical complete remission (CR) was achieved in 92.2% of patients after radiotherapy (n = 47). The cumulative incidences of locoregional and distant failure were 19.6% (n = 10) and 7.8% (n = 4), respectively. A total of 20 patients died (39.2%), including 10 cancer-related deaths (19.6%) and 10 comorbidity-related deaths (19.6%). The 5-year locoregional control (LRC), time to progression (TTP), overall survival (OS), and cancer-specific survival (CSS) were 76.9%, 71.2%, 59.4%, and 77.0%, respectively. No Grade 3 or above acute or late toxicities were reported. In univariate analysis, LRC, TTP, and CSS were significantly higher in the low-risk group than in the high-risk group (P < 0.05). After adjusting for age, number of comorbidities, radiotherapy modality, and chemotherapy, the low-risk group was still significantly better than the high-risk group in terms of LRC (HR = 6.10, 95% CI: 1.18-31.45, P = 0.031), TTP (HR = 8.07, 95% CI: 1.64-39.68, P = 0.010) and CSS (HR = 6.29, 95% CI: 1.19-33.10, P = 0.030). CONCLUSIONS: IGBT ± EBRT is safe and effective as definitive treatment for IOEC patients, achieving satisfactory locoregional control, favorable survival outcomes, and low toxicity. Risk classification based on FIGO 2009 staging and biopsy pathology is an independent prognostic factor for LRC, TTP, and CSS.

Differential diagnosis between low-risk and high-risk thymoma: Comparison of diagnostic performance of radiologists with and without deep learning model.

Background: There are few CT-based deep learning (DL) studies on thymoma according to the World Health Organization classification. Purpose: To develop a CT-based DL model to distinguish between low-risk and high-risk thymoma and to compare the diagnostic performance of radiologists with and without the DL model. Material and Methods: 159 patients with 160 thymomas were included. A fine-tuning VGG16 network model with Adam optimizer was used, followed by k-fold cross validation. The dataset consisted of three axial slices, including the maximum tumor size from the CT volume data. The data were augmented 50 times by rotation, zoom, shear, and horizontal/vertical flip. Three independent networks for the CT dataset were considered, and the result was determined by voting. Three radiologists independently diagnosed thymomas with and without the model. The area under the curve (AUC) of the diagnostic performance was compared using receiver operating characteristic analysis. Results: Accuracy of the DL model was 71.3%. Diagnostic performance of the radiologists was as follows: AUC and accuracy without the DL model, 0.61-0.68 and 61.9%-69.3%; and with the DL model, 0.66-0.69 and 68.1%-70.0%, respectively. AUC of the diagnostic performance showed no significant differences between radiologists with and without the DL model. The DL model tended to increase the diagnostic accuracy, but AUC was not significantly improved. Conclusion: Diagnostic performance of the DL was comparable to that of radiologists. The DL model assistance tended to increase diagnostic accuracy.

True Mitotic Count Prediction in Gastrointestinal Stromal Tumors: Bayesian Network Model and PROMETheus (Preoperative Mitosis Estimator Tool) Application Development.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy. OBJECTIVE: The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs. METHODS: Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed. RESULTS: Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model's accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model. CONCLUSIONS: The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

Optimizing Fall Risk Diagnosis in Older Adults Using a Bayesian Classifier and Simulated Annealing.

The aim of this study was to improve the diagnostic ability of fall risk classifiers using a Bayesian approach and the Simulated Annealing (SA) algorithm. A total of 47 features from 181 records (40 Center of Pressure (CoP) indices and 7 patient descriptive variables) were analyzed. The wrapper method of feature selection using the SA algorithm was applied to optimize the cost function based on the difference of the mean minus the standard deviation of the Area Under the Curve (AUC) of the fall risk classifiers across multiple dimensions. A stratified 60-20-20% hold-out method was used for train, test, and validation sets, respectively. The results showed that although the highest performance was observed with 31 features (0.815 ± 0.110), lower variability and higher explainability were achieved with only 15 features (0.780 ± 0.055). These findings suggest that the SA algorithm is a valuable tool for feature selection for acceptable fall risk diagnosis. This method offers an alternative or complementary resource in situations where clinical tools are difficult to apply.

The Relationship between D'Amico and ISUP Risk Classifications and 68Ga-PSMA PET/CT SUVmax Values in Newly Diagnosed Prostate Cancers.

INTRODUCTION: This study aimed to evaluate the relationship between pathological and clinical risk classifications in newly diagnosed prostate cancer patients, and 68Ga-PSMA PET/CT data and serum Prostate Specific Antigen (PSA) values. METHOD: A total of 203 patients who were diagnosed with prostate cancer between 2019 and 2023, who had not yet received treatment and who underwent 68Ga-PSMA PET/CT for staging purposes were included in this study. RESULTS: There was a substantial correlation between D'Amico risk classification, Gleason score, ISUP classification, and the presence or absence of metastasis (p < 0.0001). The median SUVmax value of the prostate gland and the D'Amico risk classification were statistically significantly correlated. (p < 0.0001). There was a statistically significant correlation between the ISUP classification and the PSA value and prostate gland SUVmax value (p < 0.0001). There was a significant correlation between the median SUVmax values of the prostate gland at the time of diagnosis and the patients with and without metastases (p < 0.0001). According to the data obtained from ROC analysis, patients with prostate gland SUVmax values of 8.75 and above were found to have a high probability of metastasis with a sensitivity of 78.9% and a specificity of 59.05%. CONCLUSION: Our study showed that 68Ga-PSMA PET/CT is a highly effective method for staging newly diagnosed high-risk prostate cancer. The probability of metastasis was found to be dramatically increased in Gleason 8 and above. According to D'Amico risk classification, metastasis was detected in at least half of high-risk patients. Since the sensitivity of metastasis was 78.9% in patients with prostate gland SUVmax value above 8.75, we think that these patients should be carefully reported in terms of metastasis.

Cardiovascular, renal and mortality risk by the KDIGO heatmap in Japan.

Background: This study aimed to assess the prognosis of people with chronic kidney disease (CKD) in Japan using the Kidney Disease: Improving Global Outcomes (KDIGO) heatmap. Methods: The prognoses of individuals with estimated glomerular filtration rates (eGFR) <90 mL/min/1.73 m2 were evaluated based on the KDIGO heatmap using an electronic medical record database in Japan. The primary outcome was major adverse cardiovascular events (MACE), a composite of myocardial infarction (MI), stroke, heart failure (HF) hospitalization and in-hospital death (referred to as MACE1). Additionally, ad hoc MACE2 (MI hospitalization, stroke hospitalization, HF hospitalization and in-hospital death) was examined. The secondary outcome was the renal outcome. Results: Of the 543 606 individuals included, the mean age was 61.6 ± 15.3 years, 50.1% were male and 40.9% lacked urine protein results. The risk of MACEs increased independently with both eGFR decline and increasing proteinuria from the early KDIGO stages: hazard ratios (95% confidence interval) of MACE1 and MACE2, compared with G2A1 were 1.16 (1.12-1.20) and 1.17 (1.11-1.23), respectively, for G3aA1, and 1.17 (1.12-1.21) and 1.35 (1.28-1.43), respectively, for G2A2. This increased up to 2.83 (2.54-3.15) and 3.43 (3.00-3.93), respectively, for G5A3. Risks of renal outcomes also increased with CKD progression. Conclusions: This study is the first to demonstrate the applicability of the KDIGO heatmap in assessing cardiovascular and renal risk in Japan. The risk increased from the early stages of CKD, indicating the importance of early diagnosis and intervention through appropriate testing.

A taxonomy of cyber risk taxonomies.

The field of cyber risks is rapidly expanding, yet significant research remains to be conducted. Numerous taxonomy-based systems have been proposed in both the academic literature and industrial practice to classify cyber risk threats. However, the fragmentation of various approaches has resulted in a plethora of taxonomies, often incongruent with one another. In this study, we undertake a comprehensive review of these alternative taxonomies and offer a common framework for their classification based on their scope. Furthermore, we introduce desirable properties of a taxonomy, which enable comparisons of different taxonomies with the same scope. Finally, we discuss the managerial implications stemming from the utilization of each taxonomy class to support decision-making processes.

Bayesian network analysis of risk classification strategies in the regulation of cellular products.

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products.

Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection.

BACKGROUND: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies. OBJECTIVE: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database. METHODS: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction. RESULTS: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831. CONCLUSIONS: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. TRIAL REGISTRATION: Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action.

Prospective validation of a preoperative scoring system for difficult laparoscopy cholecystectomy.

Difficult laparoscopic cholecystectomy (LC) is defined by its surgical outcomes, including operative time, conversion to open surgery, bile duct and/or vascular injury. Difficult LC can be graded based on intraoperative findings. The main objective of this study is to apply and validate the reliability of their proposed risk score to predict the operative difficulty of an LC, based on their own validated intraoperative scale. Single-center prospective cohort study from 01/2020-12-2023. 367 patients > 18 years who underwent LC were included. The preoperative risk scale and intraoperative grading system were registered. Surgical outcomes were determined. Predictive accuracy was evaluated by the Receiver Operator Characteristic curve, sensitivity, specificity, positive, and negative predictive values, and Youden's Index (J). Patients' mean age was 44.1 ± 15.3 years. According to the risk score, 39.5% LC were "low" risk difficulty, 49.3% were "medium" risk, and 11.2% were "high" risk difficult LC. Based on the intraoperative grading system, 31.9% were difficult LC (Nassar grades 3-4) and 68.1% were easy LC (Nassar grades 1-2). There was a statistically significant correlation (0.428, p < 0.05) between the preoperative risk score and the intraoperative grading system. The AUC for the preoperative risk score scale and intraoperative difficult LC was 0.735 (95% CI 0.687-0.779) (J: 0.34). A preoperative risk score > 1.5 had an 83.7% sensitivity and a 50.8% specificity for intraoperative difficult LC. A predictive preoperative score for difficult LC and a routine collection of the intraoperative difficulty should be implemented to improve surgical outcomes and surgical planning.

Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission.

INTRODUCTION: To develop and validate a support tool for healthcare providers, enabling them to make precise and critical decisions regarding intensive care unit (ICU) admissions for high-risk pregnant women, thus enhancing maternal outcomes. METHODS: This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission. Subsequently, sensitivity analysis was conducted to compare the accuracy, predictive power, sensitivity, and specificity of these models, with differences analyzed using the Wilcoxon test. RESULTS: The XGBoost algorithm demonstrated superior efficiency, achieving an accuracy rate of 85%, sensitivity of 42%, specificity of 97%, and an area under the receiver operating characteristic curve of 86.7%. Notably, the estimated prevalence of ICU utilization by the model (11.6%) differed from the prevalence of ICU use from the study (21.52%). CONCLUSION: The developed risk engine yielded positive results, emphasizing the need to optimize intensive care bed utilization and objectively identify high-risk pregnant women requiring these services. This approach promises to enhance the effective and efficient management of pregnant women, particularly in resource-constrained regions worldwide. By streamlining ICU admissions for high-risk cases, healthcare providers can better allocate critical resources, ultimately contributing to improved maternal health outcomes.

ARAware: Assisting Visually Impaired People with Real-Time Critical Moving Object Identification.

Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed.

Prognostic Nomograms for Elderly Patients with Small Cell Lung Cancer Brain Metastasis: A Surveillance, Epidemiology, and End Results Population-Based Study with Temporal External Validation.

OBJECTIVE: This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer (SCLC) brain metastasis (BM), and to create and validate nomograms for OS and CSS prediction. METHODS: Data from elderly SCLC BM patients were extracted out of the Surveillance, Epidemiology, and End Results database, including 1200 patients identified from 2010 and 2015 who were randomly allocated into a training set and an internal validation set at a proportion of 7:3, and 666 patients diagnosed between 2018 and 2020 as a temporal external validation set. Independent predictors for OS and CSS were determined through univariate Cox analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox analysis sequentially. Nomograms for OS and CSS were constructed, and validated by the internal and temporal external validation sets. RESULTS: Age, N stage, chemotherapy, and liver metastasis were determined as independent predictors of OS and CSS, while radiotherapy and surgery were not. Nomograms were constructed based on these independent predictors. The results of the receiver operator characteristic curves, the areas under the curve and calibration curve demonstrated that the nomograms exhibited commendable discriminative ability and calibration. Moreover, decision curve analysis, net reclassification improvement, and integrated discrimination improvement also suggested that the nomograms possessed superior clinical usefulness and predictive capability relative to the TNM system. CONCLUSIONS: Prognostic nomograms for elderly patients with SCLC BM have been developed, demonstrating good performance in terms of accuracy, reliability, and practicality.

Endoscopic Resection of Malignancies in the Upper GI Tract: A Clinical Algorithm.

Background: Malignancies in the upper gastrointestinal tract are amenable to endoscopic resection at an early stage. Achieving a curative resection is the most stringent quality criterion, but post-resection risk assessment and aftercare are also part of a comprehensive quality program. Summary: Various factors influence the achievement of curative resection. These include endoscopic assessment prior to resection using chromoendoscopy and HD technology. If resectability is possible, it is particularly important to delineate the lateral resection margins as precisely as possible before resection. Furthermore, the correct choice of resection technique depending on the lesion must be taken into account. Endoscopic submucosal dissection is the standard for esophageal squamous cell carcinoma and gastric carcinoma. In Western countries, it is becoming increasingly popular to treat Barrett's neoplasia over 2 cm in size and/or with suspected submucosal infiltration with en bloc resection instead of piece meal resection. After resection, risk assessment based on the histopathological resection determines the patient's individual risk of lymph node metastases, particularly in the case of high-risk lesions. This is categorized according to the current literature. Key Messages: This review presents clinical algorithms for endoscopic resection of esophageal SCC, Barrett's neoplasia, and gastric neoplasia. The algorithms include the pre-resection assessment of the lesion and the resection margins, the adequate resection technique for the respective lesion, as well as the post-resection risk assessment with an evidence-based recommendation for follow-up therapy and surveillance.

A Comparison of Globally Applied Prognostic Risk Groups and the Prevalence of Metastatic Disease on Prostate-specific Membrane Antigen Positron Emission Tomography in Patients with Newly Diagnosed Prostate Cancer.

BACKGROUND: Various risk classification systems (RCSs) are used globally to stratify newly diagnosed patients with prostate cancer (PCa) into prognostic groups. OBJECTIVE: To compare the predictive value of different prognostic subgroups (low-, intermediate-, and high-risk disease) within the RCSs for detecting metastatic disease on prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) for primary staging, and to assess whether further subdivision of subgroups would be beneficial. DESIGN, SETTING, AND PARTICIPANTS: Patients with newly diagnosed PCa, in whom PSMA-PET/CT was performed between 2017 and 2022, were studied retrospectively. Patients were stratified into risk groups based on four RCSs: European Association of Urology, National Comprehensive Cancer Network (NCCN), Cambridge Prognostic Group (CPG), and Cancer of the Prostate Risk Assessment. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The prevalence of metastatic disease on PSMA-PET/CT was compared among the subgroups within the four RCSs. RESULTS AND LIMITATIONS: In total, 2630 men with newly diagnosed PCa were studied. Any metastatic disease was observed in 35% (931/2630) of patients. Among patients classified as having intermediate- and high-risk disease, the prevalence of metastases ranged from approximately 12% to 46%. Two RCSs further subdivided these groups. According to the NCCN, metastatic disease was observed in 5.8%, 13%, 22%, and 62% for favorable intermediate-, unfavorable intermediate-, high-, and very-high-risk PCa, respectively. Regarding the CPG, these values were 6.9%, 13%, 21%, and 60% for the corresponding risk groups. CONCLUSIONS: This study underlines the importance of nuanced risk stratification, recommending the further subdivision of intermediate- and high-risk disease given the notable variation in the prevalence of metastatic disease. PSMA-PET/CT for primary staging should be reserved for patients with unfavorable intermediate- or higher-risk disease. PATIENT SUMMARY: The use of various risk classification systems in patients with prostate cancer helps identify those at a higher risk of having metastatic disease on prostate-specific membrane antigen positron emission tomography/computed tomography for primary staging.

Comparison of multi-task ergonomic assessment methods for risk of upper extremity and low back musculoskeletal disorders.

Work-related musculoskeletal disorder of upper extremity multi-task assessment methods (Revised Strain Index [RSI], Distal Upper Extremity Tool [DUET]) and manual handling multi-task assessment methods (Revised NIOSH Lifting Equation [RNLE], Lifting Fatigue Failure Tool [LiFFT]) were compared. RSI and DUET showed a strong correlation (rs = 0.933, p < 0.001) where increasing risk factor exposure resulted in increasing outputs for both methods. RSI and DUET demonstrated fair agreement (κ = 0.299) in how the two methods classified outputs into risk categories (high, moderate or low) when assessing the same tasks. The RNLE and LiFFT showed a strong correlation (rs = 0.903, p = 0.001) where increasing risk factor exposure resulted in increasing outputs, and moderate agreement (κ = 0.574) in classifying the outputs into risk categories (high, moderate or low) when assessing the same tasks. The multi-task assessment methods provide consistent output magnitude rankings in terms of increasing exposure, however some differences exist between how different methods classify the outputs into risk categories.

Reducing delivery insurance costs through risk score model for food delivery company.

In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers' profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America.

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). METHODS: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning-based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the compared models, SegNeXt-ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. CONCLUSION: This two-stage SegNeXt-ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.