2026-10-19

Background: Systemic inflammation in psoriasis accelerates atherosclerosis and confers excess cardiovascular risk that is not fully captured by traditional clinical predictors. Metabolomic profiling may identify disease-specific metabolic signatures associated with cardiovascular events and enhance risk prediction beyond conventional factors. Objectives: To determine whether plasma metabolomic profiling improves cardiovascular risk prediction in patients with psoriasis and to develop and externally validate an artificial intelligence-driven metabolomic risk score (MetaPsoAI) for incident cardiovascular events. Methods: We analyzed 1086 individuals with psoriasis and no prior coronary artery disease from the Mass General Brigham (MGB) Biobank who underwent nuclear magnetic resonance-based quantification of 168 circulating metabolites. Associations between metabolites and incident all-cause death or myocardial infarction were assessed. An extreme gradient boosting algorithm was used to develop MetaPsoAI for prediction of myocardial infarction or stroke. External validation was performed in the UK Biobank (n = 274,000), stratified by psoriasis status. Results: Multiple metabolites, particularly HDL subfractions and amino acid metabolites, were associated with cardiovascular outcomes. MetaPsoAI stratified risk across quartiles, with individuals in the highest quartile demonstrating a 3.6-fold higher risk of myocardial infarction or stroke in the MGB cohort. In the UK Biobank, the highest quartile was associated with a 1.9-fold increased risk among psoriasis patients, with stronger effect sizes compared with non-psoriasis controls. Conclusions: Our findings highlight the potential of AI-driven metabolomics to refine cardiovascular risk prediction in immune-mediated diseases and enable precision prevention strategies for patients with psoriasis.

Background: In colorectal cancer, the AJCC T category is defined by the depth of bowel wall invasion rather than by the absolute size of the primary tumor. The clinical value of tumor size in assessing tumor aggressiveness, metastatic risk, and prognosis remains insufficiently investigated. Aim: To evaluate the clinical value of tumor size in assessing potential metastatic risk and prognostic stratification in colorectal cancer. Methods: We retrospectively included 10,513 patients with colorectal cancer who underwent surgical treatment at FUSCC between 2008 and 2020. We integrated tumor size with clinicopathological characteristics to assess metastatic risk using machine-learning models and SHAP-based interpretation. By modeling tumor size as both a categorical and continuous variable, we further investigated its prognostic relevance using Kaplan-Meier survival analysis and Cox proportional hazards regression incorporating restricted cubic splines. Additionally, an independent cohort of 6,739 patients from Changhai Hospital was used for external validation. Results: Machine-learning identified lymph node metastasis as the primary predictive endpoint, with the CatBoost model achieving the highest AUROC of 0.800. SHAP analysis showed that smaller tumor diameter contributed positively to predicted metastatic risk. Multivariable restricted cubic spline analysis further demonstrated a significant nonlinear association between tumor diameter and OS, with mortality risk increasing as tumor diameter decreased within the approximately 0-3 cm range. Among younger male patients with locally advanced disease, tumors ≤3 cm were associated with significantly worse OS and DFS than tumors >5 cm (both P < 0.001). External validation yielded similar findings. Conclusion: Smaller tumor size may paradoxically indicate higher lymph node metastatic risk and poorer prognosis in a subset of patients with colorectal cancer, particularly younger male patients with locally advanced disease. Tumor size may therefore serve as a complementary factor for postoperative risk stratification beyond conventional TNM staging.

Background: This study aimed to develop and validate an interpretable machine learning framework that combines semi-automated segmentation, intratumoral habitats derived from multiparametric MRI (mpMRI), and multiscale peritumoral radiomics to predict high-grade prostate cancer (HGPCa) before surgery. Methods: This retrospective dual-center study involved 274 patients, comprising 208 from Center 1 for development and 66 from Center 2 for external test. The clinical and imaging variables included patient age, serum PSA, fPSA, PSAD, maximum tumor diameter, PI-RADS scores, and anatomical zone distribution (PZ vs. TZ). The development cohort was randomly divided into a training cohort (n = 145) and an internal validation cohort (n = 63). Intratumoral habitats were delineated using semi-automated segmentation and unsupervised K-means clustering, while peritumoral radiomic features were extracted from concentric 1-, 3-, and 5-mm shells. Predictive classifiers were optimized through machine learning algorithms, particularly a multilayer perceptron (MLP) for the habitat signature. A combined clinical-radiomics nomogram was constructed, with SHapley Additive exPlanations (SHAP) employed to enhance model interpretability. Subgroup analyses were performed to assess the discriminative performance of the combined nomogram across the peripheral zone (PZ) and transition zone (TZ). Results: The combined nomogram exhibited superior diagnostic performance, achieving AUCs of 0.914, 0.903, and 0.797 in the training cohort, internal validation cohort, and external test cohort, respectively. The MLP-based habitat model showed good discriminative performance, with AUCs of 0.896, 0.897, and 0.782 across the training cohort, internal validation cohort, and external test cohort, respectively. Among the peritumoral regions, the 5-mm shell demonstrated the best discriminative performance. SHAP analysis identified Habitat 1 Shape Sphericity as the most influential feature. Subgroup analysis demonstrated the discriminatory ability of the combined nomogram in both the PZ (AUC: 0.796) and TZ (AUC: 0.753) subgroups. Conclusion: The integration of semi-automated segmentation-based habitat analysis and peritumoral radiomics provides a promising approach for preoperative risk stratification of prostate cancer.

Background: Accurate noninvasive estimation of pathological complete response (pCR) after neoadjuvant therapy (NAT) in triple-negative breast cancer (TNBC) may support multidisciplinary discussions, but multimodal MRI-ultrasound evidence remains limited. We developed and externally validated a clinicopathological deep learning radiomics nomogram and explored its potential auxiliary value in a strictly hypothetical post-hoc surgical-planning scenario. Methods: With institutional review-board approval and waived consent, this retrospective multicenter diagnostic accuracy study included 436 biopsy-proven TNBC patients who underwent NAT and surgery: Center 1 training cohort (n = 268) and internal validation cohort (n = 86), plus historical cases from cooperating Centers 2-3 for external validation (n = 82). pCR was defined as ypT0/is ypN0. Pretreatment dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) maps, T2-weighted imaging (T2WI), and B-mode ultrasound were analyzed. Intratumoral and 5-mm peritumoral ring radiomics features were extracted and selected by LASSO. DenseNet-121 extracted modality-specific deep features, fused using multi-head self-attention. Age, tumor size, Ki-67, tumor-infiltrating lymphocytes (TILs), cT/N stage, and histological grade were integrated with imaging scores in a logistic-regression nomogram. Parameters and thresholds were locked after Center 1 development and applied once to external data. No model output was used clinically. Feature standardization and ComBat harmonization parameters were strictly locked to the training cohort before application to validation data. Results: pCR occurred in 182/436 patients (41.7%). The nomogram achieved areas under the receiver operating characteristic curve (AUCs) of 0.917 (95% CI, 0.881-0.947) in training, 0.883 (0.814-0.937) in internal validation, and 0.856 (0.773-0.921) in external validation, exceeding clinical (external AUC, 0.721), radiomics (0.786), and deep learning (0.819) models. External sensitivity, specificity, and accuracy at the locked threshold were 0.765, 0.813, and 0.793, respectively. Calibration was acceptable (slope, 0.93; intercept, -0.06; Brier score, 0.16). Decision curve analysis showed net benefit for thresholds of 0.20-0.70. Multi-threshold analysis revealed that a high-specificity threshold (0.65) yielded a positive predictive value of 0.85, potentially identifying a subset of patients with very high confidence of achieving pCR. Compared with the clinical model, external reclassification improved (continuous net reclassification improvement [NRI], 0.34; integrated discrimination improvement [IDI], 0.11; P = 0.003 and P = 0.008, respectively). Conclusion: Among the few multicenter TNBC studies combining MRI, ultrasound, intratumoral/peritumoral radiomics, and attention-based deep learning, this nomogram showed good retrospective accuracy for pCR prediction after NAT. The model is presented as an aid for pCR prediction and preoperative stratification rather than as direct support for surgical decision-making. Prospective validation and clinical-impact studies are required before implementation.

Background: Urosepsis remains a leading cause of ICU mortality. Albumin-derived composite indices, which integrate nutritional, inflammatory, and metabolic parameters, hold prognostic promise; however, their comparative performance has not been directly assessed. Methods: We conducted a dual-cohort retrospective study using MIMIC-IV (n = 3,060) for derivation and the Affiliated Hospital of Guizhou Medical University (n = 353) for validation. The primary outcome was 28-day ICU mortality in a competing-risks framework with discharge alive as competing event. Six indices-lactate dehydrogenase-to-albumin ratio (LDAR), anion gap-to-albumin ratio (AGAR), glucose-to-albumin ratio (GAR), red cell distribution width-to-albumin ratio (RAR), total bilirubin-to-albumin ratio (TAR), and urea nitrogen-to-albumin ratio (UAR)-were compared via cause-specific Cox and Fine-Gray subdistribution hazard models, cumulative incidence functions, restricted cubic splines (RCS), Kaplan-Meier curves, and ROC/Youden/cNRI analyses. LASSO-selected features trained a multilayer perceptron (MLP), benchmarked against six severity scores; a parsimonious external model (events per variable ≥ 10) was also examined. Results: In the derivation cohort, logLDAR, RAR, TAR, AGAR, and UAR were independently associated with 28-day ICU mortality in fully adjusted models (HRs = 1.292, 1.147, 1.136, 1.074, and 1.013, respectively; all P < 0.01), whereas logGAR was not. Fine-Gray models corroborated these findings (logLDAR sHR = 1.351, 95% CI: 1.238-1.473). RCS revealed a non-linear logLDAR-mortality relationship (P non-linear = 0.040), with risk accelerating beyond logLDAR > 7. logLDAR yielded the highest AUC (0.638, 95% CI: 0.609-0.667) and cNRI (0.119). External validation confirmed the independent prognostic value of logLDAR (HR = 1.524, 95% CI: 1.225-1.895; Fine-Gray sHR = 1.545, 95% CI: 1.295-1.844), with an external AUC of 0.735 (95% CI: 0.648-0.808), although between-cohort differences in case definitions may partly contribute. The MLP with eight LASSO-selected features achieved an external AUC of 0.762 (95% CI: 0.687-0.840), comparable to SOFA plus logLDAR (AUC = 0.782; DeLong P = 0.512). SHAP analysis ranked logLDAR among the top predictors. Conclusion: Among the six albumin-derived indices, logLDAR demonstrated the most consistent and generalizable prognostic performance for 28-day ICU mortality in urosepsis, reflecting both tissue hypoxia and nutritional-inflammatory depletion, and remained robust under competing-risk adjustment. logLDAR may serve as a simple bedside biomarker for early risk stratification; however, prospective studies are warranted to determine whether interventions targeting its underlying pathways can improve outcomes.

Background: Accurate preoperative prediction of lymph node metastasis (LNM) is essential for treatment selection in early gastric cancer (EGC), particularly when considering endoscopic submucosal dissection. Existing approaches show limited sensitivity for metastatic disease and frequently lack external or prospective validation. We aimed to develop and validate an interpretable machine-learning model for individualized preoperative prediction of LNM in EGC. Methods: In this multicenter study, four cohorts from three tertiary centers were included. Patients with pathologically confirmed EGC who underwent gastrectomy with lymph node dissection were retrospectively enrolled for training and external validation. A temporally independent cohort (September 2023-December 2024) was prospectively enrolled for validation. A preoperative pathology validation cohort (January 2025-January 2026) was also analyzed. Clinicopathological variables were selected using LASSO regression. SMOTE was applied to address class imbalance. Six machine-learning algorithms were compared. Model performance was assessed using AUC and PR-AUC, and interpretability was evaluated using SHapley Additive exPlanations. A web-based calculator was developed. Results: Among 3412 patients in the training cohort, 467 (13.7%) had LNM. XGBoost achieved the best discrimination (training AUC = 0.9289) and maintained robust performance in the external (AUC = 0.8583; PR-AUC = 0.6079) and prospective validation cohorts (AUC = 0.8766; PR-AUC = 0.5137). In the preoperative pathology validation cohort, the model achieved an AUC of 0.8653 and a PR-AUC of 0.6050. Key predictors included lymphovascular/neural invasion, invasion depth, tumor location, differentiation, and tumor size. Conclusions: This externally and prospectively validated, interpretable machine-learning model provides transparent individualized risk estimates and may support personalized decision-making between endoscopic and surgical management in EGC.

Background: Critically ill patients with chronic obstructive pulmonary disease (COPD) and sepsis are at high risk of in-hospital mortality because of respiratory and organ dysfunction. Early risk assessment may be useful for clinical decision-making. This study aimed to develop an interpretable machine learning model for this population. Methods: Data were extracted from MIMIC-IV and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV cohort was split in a 7:3 ratio into training and internal test sets. Features were selected using least absolute shrinkage and selection operator regression and Boruta. Eight models were trained using 10-fold cross-validation and grid search. The best-performing model was externally validated using eICU-CRD, interpreted using Shapley Additive Explanations (SHAP), and compared with the Sequential Organ Failure Assessment (SOFA) score. Results: Among 4,900 MIMIC-IV patients, in-hospital mortality was 19.2%. In the internal test set, the naive Bayes (NB) model achieved an area under the receiver operating characteristic curve (AUC) of 0.743 (95% CI, 0.711-0.775), outperforming the SOFA score (AUC, 0.696, 95% CI, 0.663-0.730, P < 0.001). In external validation, AUC decreased to 0.681 (95% CI, 0.607-0.755), an absolute reduction of 0.062. SHAP identified Simplified Acute Physiology Score II and SOFA as predictors, followed by the anion gap and urine output. The model was deployed across web, desktop, and mobile platforms. Conclusion: An NB model was successfully developed to predict in-hospital mortality in critically ill patients with COPD and sepsis. Further prospective multicenter validation is warranted before routine clinical use.

Background: Preoperative detection of meningioma bone invasion lacks objective biomarkers and relies on subjective radiological assessment, posing a risk of excessive bone resection or residual tumor. We therefore developed and externally validated a parsimonious six-feature DenseNet121 signature derived from multiparametric MRI across two independent institutions. Methods: In this retrospective diagnostic accuracy study, 438 consecutive patients (mean age, 53 ± 11 years; 77% women) with histologically confirmed meningioma were allocated to training (n = 182), internal validation (n = 77), and external test (n = 179) sets. Following N4 bias-field correction and isotropic resampling, contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted volumes were fed into a DenseNet121 architecture to extract a six-feature signature via nested cross-validation. Discrimination (area under the ROC curve, AUC), calibration (Hosmer-Lemeshow test), and clinical utility (decision-curve analysis) were evaluated. Results: In the external cohort (prevalence 11%), the signature achieved an AUC of 0.82 (95% CI 0.73-0.91), sensitivity 75%, specificity 77% and negative predictive value 96%, with an AUC improvement of 0.155 over conventional radiomics (95% CI, 0.01-0.30; p = 0.035). At a 20% risk threshold, decision-curve analysis confirmed a positive net clinical benefit, primarily driven by the high negative predictive value. SHAP analysis demonstrated the dominant and consistent contribution of low-ADC features. Conclusion: The DenseNet121-derived six-feature signature serves as a reliable rule-out aid for preoperative meningioma bone invasion, with a high negative predictive value that may safely support decisions to avoid extensive bone resection in predicted-negative cases. Positive predictions require confirmatory CT or intraoperative biopsy to mitigate false positives. This tool could assist nurses and surgeons in preoperative planning, facilitating personalized patient care and potentially reducing operative time and complications.

Objectives: Pelvic bones exhibit multiple secondary ossification centers during maturation, enabling automated age estimation. This study investigated whether a deep learning model could estimate chronological age from pelvic radiographs across the full pediatric age range. Materials And Methods: This retrospective study included pelvic radiographs from two hospitals for model development, from patients aged between 0 and 18 years. A DenseNet161 model was trained to classify age into 19 1-year interval categories using chronological age as a reference standard and conditional ordinal regression (CORN) loss. External validation was conducted using two independent cohorts, including 541 radiographs from a different department of the same hospital and 203 from an external hospital. External validation was performed on 744 radiographs from two independent cohorts. Model performance was evaluated using the accuracy of predictions within 1 year of the chronological age (ACC@dist = 1) and the mean absolute error (MAE). Results: A total of 3972 radiographs (median age, 2.5 years; interquartile range, 0-8 years; 2463 males) were included in model development. On the internal test set, the model achieved an MAE of 0.394 years (girls: 0.360; boys: 0.419) and an ACC@dist = 1 of 0.937 (girls: 0.944; boys: 0.931). On the external test sets, the corresponding values were 0.513 years (girls, 0.628; boys, 0.455) and 0.927 (girls, 0.896; boys, 0.943), respectively. Conclusion: Automated age estimation in children is feasible from pelvic radiography using a deep neural network. Key Points: Question Pelvic radiographs are commonly obtained in children, yet their potential for automated chronological age estimation across the full pediatric age range remains unexplored. Findings A deep learning model estimated chronological age from pelvic radiographs with a mean absolute error of 0.394 and 0.513 years on internal and external test sets, respectively. Clinical relevance Deep learning-based chronological age estimation from pelvic radiographs is feasible across the full pediatric age range. This approach allows clinically relevant skeletal maturation data to be extracted from imaging already acquired for other indications.

Background: Radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) poses a major therapeutic challenge, with loss of radioiodine avidity, limited treatment options, and worse outcomes. Current risk stratification systems were not designed to predict RAIR transformation before clinical refractoriness. Methods: This retrospective two-center study included a development cohort of 302 consecutive patients and an external validation cohort of 122. Candidate predictors were restricted to baseline demographic, clinicopathological, molecular, and biochemical variables available at or before first postoperative RAI administration; all post-treatment, response-related, and time-dependent variables were excluded a priori. Six machine-learning algorithms were developed with repeated cross-validation and compared by discrimination, probability accuracy, and clinical utility. Model interpretation used SHapley Additive exPlanations (SHAP). External transportability was assessed by discrimination, calibration, decision-curve analysis, and validation of prespecified risk groups. Results: Forty-six patients (15.2%) developed RAIR-DTC in the development cohort and 18 (14.8%) in the external cohort. Random forest was selected for the most favorable balance of cross-validated discrimination and probability accuracy, despite comparable external discrimination from XGBoost and the stacked ensemble. It achieved an area under the receiver operating characteristic curve (AUC) of 0.896 and Brier score of 0.078 in the internal test cohort, with robust external discrimination (AUC, 0.892; PR-AUC, 0.597; sensitivity, 0.778; specificity, 0.798). Raw external calibration was poor (calibration slope 0.304; observed-to-expected ratio 0.613), indicating that ranking was preserved whereas absolute risk estimates required local recalibration. SHAP analysis identified age ≥55 years, TERT promoter (TERTp) mutation, vascular invasion, initial stimulated thyroglobulin (sTg), and extrathyroidal extension (ETE) as the dominant contributors. Training-derived quantile cut points generated three-tier risk groups, identifying a low-risk subgroup with zero events in the external cohort and a low event rate (3.3%) in the internal test set, while enriching the high-risk subgroup to event rates of 75.0% and 37.2%, respectively, with 95% confidence intervals of 47.6%-92.7% and 23.0%-53.3%, respectively. Conclusions: An explainable, leakage-aware random forest based exclusively on routinely available baseline variables provided robust external discrimination and clinically meaningful early risk stratification for RAIR-DTC. Preserved discrimination did not guarantee transportable probability estimates, underscoring that external calibration and site-specific recalibration are essential prerequisites for responsible clinical deployment.