2026-10-21

Background: To evaluate whether baseline multi-parametric MRI radiomics models, integrating multiple regions of interest (ROIs), can predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC), and to validate performance externally. Methods: In this multicenter, retrospective cohort study, 510 patients with LARC treated with NCRT followed by total mesorectal excision were enrolled (330 for training, 83 for internal validation, and 97 for external validation). Radiomic features were extracted from three ROIs (primary tumor, mesorectal region, and lateral lymph node regions). After redundancy reduction and clustering, the top 30 features were selected using gradient boosting decision trees via cross-validation. Four classifiers, logistic regression, random forest, support vector machine, and extra trees, were evaluated, with SMOTE applied to address class imbalance. Model performance was compared across ROI combinations, MRI sequences, and algorithms using the area under the receiver operating characteristic curve (AUC). Results: Overall, 98 of 510 patients (19.2%) achieved pCR. The multi-ROI, multi-parametric model based on extra trees achieved an AUC of 0.717 in the external validation set. Among single-ROI models, tumor ROI yielded the highest AUC (0.630). Among MRI sequences, the ADC-based model performed best (AUC 0.629). Conclusion: Baseline multi-parametric MRI radiomics models integrating multiple ROIs showed modest predictive performance for pCR in LARC and demonstrated similar predictive utility in an independent external cohort. This approach may provide pre-treatment risk stratification and support future research on individualized treatment strategies and organ preservation.

Context: Hemoglobin A1c (A1c) and fasting plasma glucose (FPG) miss diabetes diagnosis in many individuals, but the alternative, oral glucose tolerance testing (OGTT), is difficult to obtain. Objective: This work aimed to develop and validate a model to identify diabetes missed by A1c and FPG but detected by OGTT and associate its outcomes with mortality. Methods: We trained machine-learning models using National Health and Nutrition Examination Survey (NHANES) data, applied them to corresponding National Center for Health Statistics mortality data, and externally validated the models with Diabetes Prevention Program Outcomes Study (DPPOS) data. We tested models including the A1c, vital signs, complete blood count, and comprehensive metabolic panel with and without the FPG. This retrospective analysis included NHANES study participants from 1999 to 2016 and DPPOS study participants from 1996 to 2020. Main outcome measures included the area under the receiver operating curve, sensitivity, and specificity of models to detect diabetes. Results: The A1c/FPG+ model had better sensitivity for DPPOS individuals with A1c less than 6.5% (48 mmol/mol) and FPG less than 126 mg/dL compared to a logistic regression of A1c and FPG and recalibrated FPG for individuals both on (0.69 vs 0.53 vs 0.57, respectively) and off (0.75 vs 0.65 vs 0.68, respectively) medication. NHANES participants without diabetes but with A1c/FPG+-predicted diabetes had elevated risk of mortality compared to those without A1c/FPG+-predicted diabetes (HR = 1.4; P < 2 × 10-16). Conclusion: Machine-learning models can improve the detection of individuals who would need OGTT assessment to diagnose diabetes using standard nonfasting laboratory values and vital signs with and without FPG.

Purpose: Metastatic castration-resistant prostate cancer (mCRPC) shows marked clinical heterogeneity and poor long-term survival, highlighting the need for simple and reliable risk stratification tools beyond the limited usability of existing complex prognostic models. In this study, we developed and validated PROGRESS (Prostate Cancer Global Risk Evaluation and Stratification Score), a simplified prognostic score derived through machine learning‑guided feature selection distilled into a simple laboratory-based tool. Methods: Baseline data from 2,035 patients with mCRPC (training set) were embedded into synthetic representations, reduced with UMAP, and clustered with DBSCAN to discover outcome-related subpopulations. Feature contribution analysis informed a multivariable model that yielded three independent predictors (prostate specific antigen [PSA], alkaline phosphatase [ALP], and AST) composing PROGRESS. External validation used two independent data sets (n = 1,239) across different therapeutic settings. Overall survival (OS) was analyzed with Kaplan-Meier and Cox regression; discrimination was assessed by ROC-AUC, AUC(t), and Harrel C-index. Results: Unsupervised modeling identified two patient risk subpopulations with significantly different OS rates (median 27.4 v 17.7 months; hazard ratio [HR], 2.20 [95% CI, 1.91 to 2.54]; P < .00001). The three retained predictors (PSA, ALP, and AST) were combined into a composite score (PROGRESS) that showed strong discrimination in the training cohort (ROC-AUC 0.89; AUC(t) 0.60; Harrel C-index 0.69). Using a prespecified cutoff, patients classified by PROGRESS as increased risk had significantly shorter survival than low-risk patients (median 18.3 v 25.6 months; HR, 1.72 [95% CI, 1.50 to 1.97]; P < .0001). PROGRESS prognostic performance was consistent across all validation cohorts (all P < .0001). Conclusion: By combining ML-based pattern discovery with classical modeling, PROGRESS provides a simple, objective, and readily implementable prognostic tool using three inexpensive routine laboratory measures. Its consistent performance across independent data sets supports clinical utility for individual risk stratification, trial enrichment, and early decision making. Prospective real-world validation is warranted.

Objectives: Early recognition of critical deterioration events (CDEs) in pediatric intensive care units (PICUs) is crucial to improving outcomes, but the real-time performance of machine-learning (ML)-based alert systems is rarely assessed. We prospectively evaluated the performance, operational utility, and deployment feasibility of the PICU Warning Index (P-WIN), a personalized ML alert system predicting CDE in the PICU. Materials And Methods: In this prospective cohort study at a quaternary-care children's hospital, P-WIN estimated deterioration risk for PICU patients aged 0-24 years at 30-minute intervals between January 7 and April 7, 2026 using electronic health record data collected via Fast Healthcare Interoperability Resources and proprietary EPIC API endpoints. CDEs included cardiopulmonary resuscitation, initiation of extracorporeal membrane oxygenation, dilute (10 μg/mL) epinephrine bolus, or unplanned endotracheal intubation. P-WIN was compared to baseline workflows: an automated PICU Warning Tool and a clinician-huddle-based assessment. Primary measures were CDE-level sensitivity, admission-level positive predictive value (PPV), and alerts per patient-day. Results: Among 1005 PICU patients, 99 CDEs were experienced by 62 patients. P-WIN detected 73% of these CDE 1-24 hours in advance, outperforming the PICU Warning Tool (39%, P < .05) and huddle workflow (23%, P < .05). P-WIN generated 0.21 (95% CI, 0.18-0.23) alerts per patient-day. The PICU Warning Tool and huddle workflow had similar PPV to P-WIN. Conclusion: P-WIN detected significantly more CDEs than existing PICU Warning tool and huddle workflows while maintaining a similar PPV. Our findings underscore the feasibility of ML-based personalized warning systems to enhance shared situational awareness of PICU deterioration.

Purpose: Preoperative planning, currently performed by orthopaedic surgeons (OS), is essential for successful osteotomy, but is labour-intensive and error-prone. As such, there is significant potential in the use of automated artificial intelligence (AI) algorithms to augment the abilities of OS in managing lower extremity pathologies in a high-volume task that demands high precision and reliability. The aim was to develop a deep learning (DL) system capable of fully autonomous planning a medial open wedge high tibial osteotomy (mow-HTO) on weight bearing anterior posterior (AP) long leg radiographs (LLR) and to validate its agreement with an expert-derived reference standard, its reliability and processing time of the DL model compared to expert OS on multicentric test data sets (two institutions). Methods: A total of 594 patients (age 41.1 ± 13.2 years, 182 female) who underwent osteotomy between 01/2014 and 01/2021 were retrospectively enrolled. Manual annotations for lower extremity alignment and high tibial landmarks were performed on AP LLR radiographs. The data set was divided into training (60%, n = 399), validation (10%, n = 59) and hold-out testing (30%, n = 136), with an additional external dataset (n = 100) for further testing. A DL system with twelve expert networks was trained to identify relevant landmarks for HTO planning. The DL model was then tested on fully automated planning of a medial open wedge HTO based on (1) the desired postoperative intersection of the mechanical axis with the tibial plateau (%-based) and (2) the desired femorotibial axis (°-based). Agreement with the expert-derived reference, reliability and processing time were compared with three expert OS using FDA-approved preoperative planning software on the test data sets. Results: For preoperative alignment parameter analysis, compared to the ground truth, human preoperative accuracy ranged from 0.13° ± 0.10° to 1.06° ± 1.4°, interrater reliability ranged from intraclass correlation (ICC) 0.84-1.0 and clinically acceptable accuracy ranged from 72% to 100%. Preoperative accuracy of the DL model ranged from 0.22° ± 0.18° to 1.04° ± 1.28°, interrater reliability ranged from ICC 0.93-1.0 and clinically acceptable accuracy ranged from 91% to 100%. For the planning of the osteotomy gap, compared to the ground truth, human accuracy ranged from 0.46 ± 0.65 mm to 0.5 ± 0.88 mm (%-based) and 0.43 ± 0.55 to 0.52 ± 0.75 mm (°-based), reliability ranged from ICC 0.9-0.96 and 0.95-0.97 and clinically acceptable accuracy was 93.6%-96.0% (%-based) and 89.8%-93.9% (°-based). For the DL model, the accuracy was 0.53 ± 0.63 mm (%-based) and 0.44 ± 0.53 mm (°-based), reliability was ICC 0.99 (both methods) and clinically acceptable accuracy was 98.1% (%-based) and 96.2% (°-based). The DL-model significantly and substantially outperformed the OS in intrarater reliability (0.88 vs. 1.0) and processing time for a fully automated alignment analysis (25.3 ± 0.8 s vs. 148.7 ± 10.1 s, p < 0.001). Conclusion: The developed DL model enabled fully automated mow-HTO planning on AP long-leg radiographs with reliability comparable to that of expert OS while significantly reducing processing time and improving measurement consistency. These suggest that DL-based systems may assist orthopaedic providers by accelerating and standardising osteotomy planning. Level Of Evidence: Level III, retrospective comparative study.