2026-10-29

Objective: To develop and validate an interpretable machine learning (ML) model that integrates extracellular volume fraction (ECV) derived from contrast-enhanced CT with clinical and imaging features for preoperative prediction of World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade in clear cell renal cell carcinoma (ccRCC). Materials And Methods: A retrospective cohort of 263 patients with pathologically confirmed ccRCC from two institutions was analyzed. Patients from Hospital A (n=184) were randomly divided into training (n=128) and internal validation (n=56) cohorts, while patients from Hospital B (n=79) served as an independent external validation cohort. CT-derived ECV, arterial enhancement fraction (AEF), and conventional imaging features were extracted. Six machine learning models (Logistic Regression [LR], eXtreme Gradient Boosting [XGBoost], Gaussian Naive Bayes [GNB], Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Light Gradient Boosting Machine [LightGBM]) were constructed, and the best-performing model was identified based on bootstrap resampling. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Tumor size (low-grade: 4.99 ± 1.98 cm; high-grade: 6.20 ± 2.72 cm, P = 0.002), ECV (low-grade: 0.46 ± 0.09; high-grade: 0.57 ± 0.12, P<0.001), and AEF (low-grade: 1.37 ± 0.24; high-grade: 1.56 ± 0.25, P<0.001) were significantly higher in high-grade ccRCC, while hematocrit (HCT, low-grade: 0.41 ± 0.05; high-grade: 0.38 ± 0.06, P<0.001) was lower. The optimal LR model achieved robust performance: training cohort AUC = 0.995 (95% CI: 0.990-0.999), internal validation AUC = 0.903 (95% CI: 0.818-0.988), external test AUC = 0.857 (95% CI: 0.756-0.958). SHAP analysis showed ECV, tumor diameter, and AEF were the top 3 predictors. Conclusion: An interpretable ML model incorporating CT-derived ECV and imaging features effectively predicts preoperative WHO/ISUP grade of ccRCC, providing a noninvasive tool for individualized risk stratification and clinical decision-making.

Background: Mild cognitive impairment (MCI) is clinically relevant in obstructive sleep apnea (OSA). We developed and externally validated an interpretable machine-learning model for concurrent clinically defined MCI to support prioritization for formal cognitive assessment rather than diagnosis. Methods: This multicenter cross-sectional diagnostic-prediction study included 400 eligible patients with OSA from Changchun, divided into a training set (n = 280) and an internal algorithm-selection hold-out (n = 120), plus an independent Shenzhen external-validation sample (n = 192). MCI classification integrated reported cognitive decline, objective cognitive evidence, largely preserved everyday independence, dementia exclusion, and assessment-team consensus with physician participation. Of 27 candidate predictors, redundancy review left 24 and LASSO-Boruta consensus retained nine. Feature selection was performed once on the full training set before repeated cross-validation and was not nested within resampling folds. Eight algorithms were tuned using repeated 10-fold cross-validation with training-fold-only SMOTE and PR-AUC as the primary metric. The RF threshold (0.3228) was derived from training out-of-fold probabilities and locked before validation. Results: MCI was classified in 93/280 (33.2%), 38/120 (31.7%), and 57/192 (29.7%) participants. The nine RF predictors were coffee consumption, physical activity, AHI, MSaO2, PHQ-9, GAD-7, ESS, CPSS-14, and PSQI total scores. In the internal hold-out used for algorithm selection, RF achieved ROC-AUC 0.953, PR-AUC 0.936, and Brier score 0.060; these selection-conditioned estimates are not unbiased generalization estimates. In independent external validation, ROC-AUC was 0.957, PR-AUC 0.809, and Brier score 0.051. Calibration intercept - 0.616 and slope 0.701 indicated systematic probability overestimation despite strong discrimination. Conclusions: The RF model discriminated strongly in Shenzhen, but external miscalibration and systematic probability overestimation mean that high AUC does not make raw probabilities transportable; local calibration assessment and, if needed, recalibration are required before clinical use. The MCI outcome cannot be reproduced from questionnaire/scale scores alone, and Supplementary Table S10 is a retrospective operational specification rather than the original CRF. Performance evidence comes almost entirely from male OSA patients; validity in women is unestablished, and the model should not be used for clinical assessment in females. It is a research screening-prioritization tool, not a diagnostic, causal, prognostic, or clinical-impact instrument.

Rationale And Objectives: To develop and validate an artificial intelligence-driven hybrid model integrating ultrasound radiomics, deep learning (DL) signatures, and clinical predictors for differentiating benign from borderline/malignant Ovarian-Adnexal Reporting and Data System (O-RADS) 4 adnexal masses, and to evaluate its utility as a radiologist decision-support tool. Materials And Methods: This multicenter study retrospectively enrolled 653 patients from four centers (521 from Center A [training, n = 405; internal validation, n = 116]; 132 from Centers B-D [external validation]), and prospectively enrolled 126 patients from Center A. Pathological results served as the gold standard. Radiomics features were extracted to generate a Rad-score using machine learning classifiers. A transfer learning-based DL model using convolutional neural networks produced a DL-score. A hybrid model integrating Rad-score, DL-score, and clinical predictors was constructed. Diagnostic performance of models was assessed using the area under the receiver operating characteristic curve (AUC). In the prospective validation set, six radiologists interpreted cases with and without hybrid model assistance. Diagnostic differences were assessed using McNemar's χ² test, and inter-reader agreement by Cohen's kappa. Results: The hybrid model demonstrated good performance with AUCs of 0.88 (95% confidence interval [CI]: 0.82-0.93) and 0.86 (95% CI: 0.78-0.92) in the prospective and external validation sets. Model assistance significantly improved accuracy and specificity across all readers (p < 0.05), with inter-reader agreement improving from kappa 0.49 to 0.73. The model stratified lesions into low-risk (4a) and high-risk (4b) subcategories with consistently distinct malignancy rates across all datasets (4a: 4.5%-16.1%; 4b: 75.4%-97.1%). Conclusion: The hybrid model demonstrated good performance for risk stratification of O-RADS 4 adnexal masses and improved radiologist performance across experience levels.

Background: Drug-induced QTc-prolongation is a major concern for developing ventricular arrhythmias, which may eventually lead to sudden cardiac death. Identifying high-risk patients is essential to support healthcare providers in handling automatically generated alerts in clinical practice. The aim of study was to develop clinical data-driven machine learning methods to predict drug-induced QTc-prolongation. Methods: Inpatients with paired electrocardiograms were enrolled from Southwest Hospital (2015-2022). Six machine learning (ML) algorithms were developed to predict drug-induced QTc prolongation (ΔQTc > 60 ms). Model performance was evaluated using an internal test set and an independent external validation cohort. Model interpretations were recognized by feature importance and SHapley Additive exPlanations (SHAP) methods. Finally, we benchmarked the ML models against the traditional Tisdale risk score. Results: A total of 2451 participants were recruited, of whom 292 developed QTc-prolongation, while 150 patients from a separate center served as the external validation cohort. The eXtreme Gradient Boosting (XGBoost) model achieved the best performance with recall of 0.884 and AUC of 0.814 in internal test set. During external validation, the AUC remained robust at 0.864, although recall declined to 0.650. SHAP analysis identified baseline QTc and QT intervals, inflammation, high-risk drugs, and hypertension are the primary drivers of model predictions. Conclusions: The XGBoost algorithm provides a highly sensitive framework for predicting drug-induced QTc prolongation. Integrating this ML tool with identified clinical drivers into hospital decision support systems would facilitate the optimization of treatment strategies and early ECG monitoring strategies.

ObjectiveBased on retrospective longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) and Korean Longitudinal Study of Ageing (KLoSA) cohorts, we developed and externally validated an interpretable machine learning model to predict 4-year incident frailty in middle-aged and older adults with arthritis.MethodsModel development was performed using the CHARLS cohort (n = 2126) and included participants aged ≥45 years who had been diagnosed with arthritis and were nonfrail at their initial survey. New-onset frailty was tracked over a 4-year interval. Thirty-one candidate predictors were identified through a literature review, none of which overlapped with the deficits constituting the frailty index. Ten core features, including age, marital status, left- and right-hand grip strength, number of painful sites, knee pain, leg pain, back pain, number of hospitalizations, and antirheumatic medication use, were selected via combined least absolute shrinkage and selection operator-Boruta feature selection. Six algorithms were compared based on their area under the curves in the internal validation dataset, specificity, sensitivity, and other clinical applicability metrics. Tenfold cross-validation was employed. The selected model was frozen and transported to KLoSA without refitting. A prespecified recalibration-in-the-large was applied to account for the lower baseline frailty incidence in the Korean cohort. Model interpretability was achieved with SHapley Additive exPlanations global and local analyses, and an online calculator was developed.ResultsLogistic regression was selected as the final model because it achieved competitive discrimination with the least shrinkage between training and validation sets. Its discriminative ability, measured as the area under the receiver operating characteristic curve, was 0.764 (95% confidence interval: 0.707-0.816) internally and remained at 0.747 (95% confidence interval: 0.701-0.793) when the model was transported to KLoSA. Performance was stable across 10-fold cross-validation (mean, 0.772 ± 0.046). Discrimination was well preserved, although a prespecified recalibration-in-the-large was required to correct for the lower baseline frailty incidence in the Korean cohort (Brier score 0.205→0.065). In the SHapley Additive exPlanations ranking, the number of painful sites, antirheumatic medication use, leg pain, age, and grip strength contributed the most. Once a user enters the 10 easily collected variables, the calculator reports that person's probability of becoming frail within 4 years. In post hoc analyses, pain burden and low grip strength emerged as two independent risk dimensions. High pain burden remained associated with falls (adjusted odds ratio = 1.60) and hospitalization (1.66) even at a given level of frailty, whereas low grip strength and medication use were associated with an increased frailty risk without a corresponding excess of events.ConclusionsThis explainable machine learning model, developed using data from Chinese middle-aged and older patients with arthritis and externally validated in a KLoSA cohort, combines acceptable predictive accuracy with cross-population robustness. The tool estimates an individualized 4-year frailty risk probability using 10 routinely available indicators. Given the observational nature of both cohorts, this model should be viewed as a candidate risk stratification aid rather than a validated clinical tool. Formal impact evaluation through prospective validation is necessary before the model can be recommended for use in community settings.

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, and its early and accurate diagnosis from chest CT imaging is complicated by overlapping radiological features between benign and malignant lesions, as well as by the limited cross-institution validation and interpretability reported in much of the existing computer-aided diagnosis literature. This study evaluates the effectiveness of U-Net architectures integrated with different pre-trained convolutional neural network (CNN) backbones for automated lung segmentation and lung cancer classification in chest CT images, with an emphasis on rigorous, leakage-free validation and cross-institution generalizability relevant to clinical practice. Methods: A balanced internal dataset of 832 chest CT images (416 cancerous and 416 non-cancerous cases, NIDCH, Bangladesh) was preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and resized to 224 × 224 pixels. U-Net models with three CNN backbones (VGG16, ResNet50, and Xception) were implemented for lung segmentation, with pseudo-labels verified against expert-annotated masks. A standalone CNN and three hybrid classifiers combining CNN feature extraction with machine learning algorithms (Support Vector Machine, Random Forest, and Gradient Boosting) were then evaluated, alongside a Vision Transformer baseline. All models were assessed using subject-level fivefold cross-validation, followed by fully independent external testing on a second, structurally distinct dataset (IQ-OTH/NCCD; 110 subjects) without retraining. Performance was measured using Dice coefficient, IoU, accuracy, precision, recall, F1-score, and AUC, with 95% confidence intervals and statistical significance testing. Results: For segmentation, U-Net with the Xception backbone achieved the best overall performance (Dice: 0.9581 ± 0.0104; IoU: 0.9163 ± 0.0167). For classification, the CNN + SVM hybrid model achieved the highest internal cross-validation accuracy (98.21% ± 0.68%) and AUC (0.9881 ± 0.0041), significantly outperforming the standalone CNN (p = 0.041), and retained the strongest performance under external validation (92.73% accuracy), with the smallest accuracy drop among all evaluated models. Ablation analyses confirmed substantial contributions from CLAHE preprocessing and U-Net segmentation, and Grad-CAM analysis showed that segmentation constrains classifier attention to anatomically plausible lung regions. Conclusion: Integrating U-Net segmentation with hybrid CNN-based classification provides an accurate and reasonably generalizable framework for lung cancer detection, offering a methodological foundation for future clinically oriented computer-aided diagnosis research, pending prospective, multi-institution validation.

Esophageal cancer (EC) is highly lethal due to late diagnosis, and accurate non-invasive tools for early detection and subtyping remain lacking. Circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics offers a promising biomarker source. In this large multi-center study enrolling 1,260 participants spanning healthy control (HC), benign esophageal diseases (BED), precancerous lesions and EC patients, we developed two machine learning models based on plasma ccf-mtDNA fragmentomic profiles: an EC detection (ED) model and a pathological subtyping prediction (PSP) model. The ED model achieved an AUC of 0.9706 (EC vs. non-EC) in the training cohort, with high performance in internal (0.9762), external (0.9748), and Chinese minority (0.9668, Uyghur and Kazakh) validation cohorts. At the optimal cut-off, it yielded 92.16% sensitivity for stage I EC in the combined validation cohort and correctly identified 93.10% of early EC cases missed by preoperative biopsy. It also discriminated high-grade intraepithelial neoplasia from non-EC with an AUC of 0.9597. The PSP model distinguished ESCC from EAC with 89.71% accuracy, improving to 90.74% after integrating key clinical parameters. In summary, ccf-mtDNA fragmentomics provides a robust, non-invasive platform for early EC detection, precancerous risk stratification, and histological subtyping, demonstrating strong potential to transform clinical management.

Background: Cardiovascular disease (CVD) is the leading cause of death among peritoneal dialysis (PD) patients, yet PD-specific biomarkers and prediction models remain limited. This study aimed to identify glycated peptide biomarkers associated with CVD in PD patients and develop a prediction model integrating glycated proteomics and clinical indicators using machine learning. Methods: In Phase I, a matched discovery cohort (48 PD patients with CVD and 48 without CVD) was used to identify glycated peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS), with glmnet used to prioritise candidate biomarkers. In Phase II, candidate biomarkers were quantified in a prospective internal derivation cohort (n = 240) and a multicentre external validation cohort (n = 102). The combined model was developed using F-test feature selection and elastic-net logistic regression. Its predictive performance was compared with three published cardiovascular risk models. Patients were stratified using a training-derived Youden cutoff. Results: In the discovery cohort, 257 glycated peptides were differentially abundant between groups. Four candidate biomarkers were prioritised, and two peptides, GEAFTLK(g)ATVLNYLPK and K(g)QHLFVK, were retained in the final model together with six clinical indicators: phosphate, diabetes, total Kt/V, albumin, cardiac troponin T, and glycated albumin. The combined model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.784 in the training set, 0.781 in the internal testing set, and 0.822 in the external validation cohort, respectively. Using the training-derived cutoff of 0.373, higher 1-year CVD incidence was observed in the high-risk group in both validation cohorts. The combined model also achieved higher ROC-AUCs than the published cardiovascular risk models. Conclusion: We developed and externally validated a PD-specific CVD prediction model by integrating glycated peptide biomarkers with clinical indicators, which may facilitate earlier risk stratification and targeted prevention.

Objective: To construct and validate a standardized three-dimensional convolutional neural network (3D CNN) framework based on cervical spine CT imaging for automated identification of cervical spinal cord injury (CSCI), and to evaluate its stability across multicenter data and potential clinical applicability. Methods: Cervical spine CT images were retrospectively collected from two independent clinical centers, including cases with and without cervical spinal cord injury. All images were converted to NIfTI format through a unified preprocessing pipeline, and three-dimensional spinal cord regions of interest (ROIs) were delineated using ITK-SNAP to assess interobserver consistency. A standardized 3D CNN architecture was constructed, and the dataset was divided into training, internal validation, and external validation sets using a stratified random strategy. Model training was performed using the PyTorch framework. Classification performance across different datasets was systematically evaluated, and ROC curves, precision-recall (PR) curves, prediction probability distributions, and threshold sensitivity analyses were used to explore the model's clinical application characteristics. Results: The model demonstrated stable discriminative performance across the training, internal validation, and external validation sets, with AUC-ROC values of 0.943, 0.953, and 0.948, respectively, and AUC-PR values of 0.936, 0.939, and 0.943, respectively. Performance metrics showed minimal variation among datasets, with no evident overfitting or performance degradation. Prediction probability distributions indicated good separation between injured and non-injured samples. Threshold sensitivity analysis showed that the model could achieve performance trade-offs under different threshold settings, suggesting a degree of flexibility for clinical decision-making. Conclusion: Based on standardized data processing and network design, the 3D CNN effectively extracted latent spatial information associated with cervical spinal cord injury from CT images. The proposed framework demonstrated good stability and reproducibility across multicenter data and may serve as a preliminary research framework for CT-based CSCI risk evaluation, particularly in scenarios where MRI is unavailable or limited. Prospective validation is required before clinical deployment. It also provides a methodological reference for the standardized construction of deep learning models in medical imaging research.