2026-10-28

Objective: Postoperative risk estimation in retroperitoneal liposarcoma (RPLS) remains challenging because conventional clinicopathologic models capture only part of its biological heterogeneity. We aimed to develop and externally evaluate a postoperative prognostic model integrating preoperative CT features with postoperative clinicopathologic information to estimate overall survival (OS) after RPLS resection. Materials And Methods: This bicentric retrospective study included 367 patients who underwent complete RPLS resection between 2009 and 2021. Institution 1 provided the training cohort (n = 128) and internal validation cohort (n = 55); Institution 2 provided the external test cohort (n = 184). A radiomics signature was constructed from three-dimensional tumor features. A deep-learning signature was derived from the largest axial tumor section using an ImageNet-pretrained ResNet-101 trained with Cox partial-likelihood loss. Clinical variables underwent univariable and multivariable Cox regression analysis. The retained clinicopathologic variables and imaging signatures were integrated into a combined model termed RDCS. Results: During follow-up, 71, 30, and 107 patients died in the training, internal validation, and external test cohorts, respectively. The corresponding RDCS C-indices were 0.775, 0.704, and 0.725, each higher than those of the individual radiomics, deep-learning, and clinical models. Calibration curves showed agreement between predicted and observed survival. Decision-curve analysis suggested potential clinical utility within the evaluated thresholds. In a separate exploratory comparison in the external primary-tumor subset (n = 82), C-indices were 0.746 for RDCS and 0.590 for the official Sarculator application. Conclusion: RDCS estimates OS after complete RPLS resection using preoperative CT-derived imaging signatures and postoperative clinicopathologic information. RDCS may complement established postoperative prognostic tools, but its application in surveillance and clinical management requires prospective validation and clinical impact assessment.

Objective: To develop and externally validate a radiomics-based machine learning model for differentiating myxoid liposarcoma (MLS) from myxoma on MRI and compare its diagnostic performance with that of experienced musculoskeletal radiologists. Methods: In this retrospective study, T1-weighted and fluid-sensitive MRI sequences from 109 patients with histologically confirmed MLS or myxoma were used to train support vector machine (SVM), random forest (RF), and XGBoost (XGB) classifiers. Models were evaluated in an independent external cohort of 32 patients from a second institution using different scanner hardware and protocols. Diagnostic performance was compared with that of two fellowship-trained senior musculoskeletal radiologists. Secondary analysis assessed MLS grading. Results: The T1-weighted SVM achieved the strongest external binary performance (accuracy, 0.84; AUC, 0.91), outperforming RF and XGB (p = 0.005 and p < 0.001, respectively). Its performance did not differ significantly from that of radiologists (AUC, 0.88-0.94; p > 0.05), although the study was not powered to establish equivalence. Fluid-sensitive SVM performance was limited (accuracy, 0.56; AUC, 0.60), indicating sequence-dependent generalizability. Models and radiologists demonstrated limited MLS grading accuracy, reflecting tumor heterogeneity. Conclusion: Externally validated radiomics-based machine learning supported noninvasive differentiation of MLS from myxoma primarily on T1-weighted MRI. T1-weighted SVM performance was not significantly different from that of experienced radiologists, although equivalence was not established, whereas fluid-sensitive performance was substantially weaker. T1-weighted quantitative imaging biomarkers may complement soft tissue tumor characterization, but extrapolation to fluid-sensitive sequences requires caution.

Objective: To evaluate the incremental predictive value of pretreatment tri-modal ultrasound deep learning integrating two-dimensional ultrasound (2DUS), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) for predicting breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC), and to assess performance stability across independent centers. Methods: This retrospective multicenter study included 1089 patients with primary invasive breast cancer. Center 1 provided the training cohort (n = 572) and internal validation cohort (n = 246), whereas centers 2 (n = 160) and 3 (n = 111) served as independent external validation cohorts. Using pretreatment 2DUS, CDFI, and CEUS within a standardized intratumoral and 10-mm peritumoral framework, single-modality, all three dual-modality combinations, and tri-modal models were developed. The best-performing dual-modality model was identified and locked during training as the primary imaging comparator. A Tri-modal + Clinical model further integrated pretreatment clinical and biomarker variables. Performance was assessed using AUC, Brier score, calibration, decision curve analysis, and ablation experiments. Results: Overall, 383 patients (35.2%) achieved bpCR. CEUS was the strongest single modality, and 2DUS + CEUS was the best-performing dual-modality model. Compared with 2DUS + CEUS, the tri-modal model increased the AUC from 0.895 to 0.919 in the combined external cohort (ΔAUC, +0.023; 95% CI, +0.010 to +0.038; p = 0.001), with corresponding improvements in Brier score (0.124 to 0.105) and F1 score (0.747 to 0.786). The AUC point estimate increased in both external centers, although the 95% CI for the incremental AUC included zero in external validation cohort 2. Despite modest standalone discrimination, CDFI provided measurable incremental information when added to 2DUS + CEUS. Further integration of pretreatment clinical and biomarker variables increased the combined external AUC to 0.935. Reduced-input retraining showed performance decreases after omission of any modality, with the largest reduction after CEUS omission. Conclusions: Pretreatment tri-modal ultrasound deep learning provided incremental predictive value for bpCR beyond the best dual-modality model, with a consistent direction of benefit across two independent external centers. Clinical and biomarker integration provided additional predictive information, supporting further evaluation of this multimodal framework for individualized pretreatment bpCR prediction.

Background: Prolonged length of stay (PLOS) after endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with increased complications, resource burden, and patient distress. Early identification of high-risk patients remains challenging. This study aimed to develop and externally validate a predictive model integrating clinical variables and three-dimensional deep learning (3D-DL) features from preoperative computed tomography angiography (CTA) to predict PLOS after EVAR. Methods: A total of 788 patients with AAA who underwent elective EVAR were enrolled from two hospitals. The internal training cohort consisted of 556 patients, and the external validation cohort included 232 patients. PLOS was defined as a LOS above the 75th percentile of the distribution. A clinical nomogram was developed using LASSO regression followed by multivariate logistic regression. Deep learning (DL) features were extracted from manually segmented computed tomography angiography (CTA) images using a 3D ResNet200 network, and LASSO regression was applied to select the most informative features. An ensemble model integrating the clinical predictors and the selected DL features was constructed. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis. Results: LASSO combined with multivariate logistic regression identified five independent clinical predictors: age, hemoglobin, albumin, maximum aneurysm diameter, and diabetes. The clinical model achieved an area under the curve (AUC) of 0.917 in the internal cohort and 0.944 in the external cohort. From the 3D ResNet200 network, 32 DL features were extracted. LASSO regression further selected two of these features. The ensemble predictive model, which integrated the clinical predictors and DL features, demonstrated improved discrimination, with an AUC of 0.971 in the internal cohort and 0.958 in the external cohort. Internal validation with 1,000 bootstrap resamples yielded optimism-corrected AUCs of 0.911 for the clinical model and 0.966 for the ensemble model. Subgroup analyses demonstrated consistent model performance across age and diabetes strata, while performance in the female subgroup warrants further evaluation in larger cohorts. Conclusion: This study developed an ensemble predictive model to predict PLOS following EVAR. The model demonstrated excellent discrimination and provided significant prognostic value beyond clinical variables alone. This nomogram may serve as a preoperative tool for risk stratification and resource planning in this population. Future studies should focus on prospective validation and clinical integration.

Background: Risk stratification of thyroid nodules on ultrasound remains inconsistent in routine practice, particularly for borderline presentations. Many deep-learning studies report discrimination alone, while probability reliability and threshold-based clinical utility receive less attention. Methods: We retrospectively included 364 development patients, each contributing one thyroid nodule labeled using surgical histopathology or fine-needle aspiration cytology. The primary analysis used a patient-level fixed training/validation/test framework, and robustness was additionally assessed with stratified patient-level five-fold cross-validation. A locked model was independently evaluated in 121 patients from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine (Shandong Provincial Hospital of Traditional Chinese Medicine), Jinan, China. The task was formulated as three-class classification: benign, indeterminate or suspicious, and malignant. Results: The multimodal model outperformed clinical-only and image-only baselines, with the largest gains observed on class-balanced metrics. On the held-out test set, UWMT-Net achieved an accuracy of 0.838 and a macro-AUC of 0.943. Post-hoc temperature scaling reduced ECE from 0.040 to 0.015 without changing hard-label metrics. Five-fold cross-validation yielded a macro-AUC of 0.936 ± 0.011. In the independent external cohort, the locked model achieved an accuracy of 0.810, macro-F1 of 0.804, macro-AUC of 0.921, and ECE of 0.031. The added fusion baselines and paired bootstrap analysis supported a modest, but not statistically significant, numerical advantage of uncertainty-weighted multi-task learning. Conclusions: Integrating ultrasound with structured clinical information improved three-class classification and probability reliability in the development cohort, with retained performance in the independent external cohort. Larger prospective multicenter validation remains required before clinical use.

Atrial fibrillation (AFib) is the most prevalent sustained cardiac arrhythmia, yet most deep learning detectors are validated on a single cohort and few report uncertainty. We developed a compact 14M-parameter convolutional neural network for binary AFib classification from 10 s, 12-lead ECG at 500 Hz, trained exclusively on the HDXML dataset (67,432 ECGs; 9628 AFib) with on-the-fly augmentation for noise, drift and missing channels. Using one unchanged checkpoint, we evaluated six independently sourced public cohorts totaling over 1.25 million recordings, including MIMIC-IV critical care and two ambulatory Holter cohorts. All metrics are reported with 95% confidence intervals from a cluster bootstrap at the patient, recording or subject levels. ROC-AUC exceeded 0.95 in every cohort, from 0.960 (95% CI 0.956-0.963) on CODE-15 to 0.999 (0.998-0.999) on SPH, including 0.966 (0.965-0.967) on MIMIC-IV with 75.3% sensitivity and 97.7% specificity, 0.963 (0.929-0.987) on CPSC 2021 and 0.995 (0.988-0.999) on MIT-BIH. Performance was stable under progressive lead masking without retraining, and median inference took 110 ms per segment on a server-class CPU without a GPU. A compact network trained on one curated source can therefore generalize across hospitals, countries and recording hardware. This study is retrospective; prospective validation is required before clinical use.

Background/Objectives: We evaluated molecular selection stability and external prediction beyond age, sex and broad pathological stage in resected stage I-II lung adenocarcinoma. Methods: Models were developed in 228 patients from two sources and frozen before evaluation in four historical cohorts containing 778 patients. The primary comparison added pathway scores using elastic-net Cox regression. Secondary analyses examined a gene model, 200 development bootstrap resamples, alternative modeling specifications and locally updated baselines. Prediction error was measured by the integrated Brier score over 1-3 years (IBS); negative molecular-minus-clinical differences (ΔIBS) favored molecular models. Evaluation intervals used 2000 paired bootstrap resamples. Results: The pathway model retained no molecular terms. The final gene model retained 16 genes, whereas both source-held-out fits and 159/200 development resamples retained none. Penalties of 0.5, 0.15 and 0.05 were selected in 160, 38 and 2 resamples. Alternative specifications changed gene retention from 0 to 61 and pathway retention from 0 to 5. Frozen gene-model ΔIBS ranged from -0.0034 to 0.0055 across cohorts. Its equal cohort mean was 0.0016 (95% CI, -0.0012 to 0.0042), compared with -0.0008 (-0.0024 to 0.0007) after cross-fitted local baseline updating. The clinical model predicted a 3-year mortality of 19.6% in Japan and 22.6% in Moffitt, versus observed values of 9.1% and 28.6%. Conclusions: Molecular selection was unstable, and incremental performance depended on specification and population. The results characterize these learning procedures and their limited development sample; broader transcriptomic prognostic value and contemporary clinical utility remain unestablished.

Background: Esophageal squamous cell carcinoma (ESCC) accounts for over 80% of esophageal cancer cases globally. Lymph node status is the strongest determinant of survival after resection, yet occult disease is found in about 17% of patients classified as node-negative. We developed a postoperative model estimating the probability of lymph node metastasis (LNM) from the resection specimen, combining nuclear features with clinicopathological variables. Methods: Of 477 consecutive ESCC patients undergoing radical esophagectomy without neoadjuvant therapy, 416 were analyzed (LNM 217, 52.2%). Nuclear features were extracted from H&E whole-slide images (20×) by QuPath optical-density detection; LASSO logistic regression selected 17. A nomogram combining the pathomics score with age, sex, tumor location, pT category and grade was validated by 10-fold cross-validation (out-of-fold, OOF) and applied without refitting to 101 patients from another institution. Results: OOF AUC was 0.821 (95% CI 0.779-0.858) for the combined model, versus 0.791 (clinical) and 0.762 (pathomics alone). The pathomics score remained an independent predictor (adjusted OR 2.47, 95% CI 1.80-3.39; p < 0.001). LNM prevalence rose across tertiles (15.8%, 55.1%, 85.6%; p < 0.001). Among 199 node-negative patients, high pathomics risk predicted worse 3-year overall survival (77.2% vs. 94.3%; p = 0.005) and progression-free survival (75.5% vs. 88.9%; p = 0.007). Externally (n = 101; LNM 24.8%), AUCs were 0.812, 0.801 and 0.697, and adding pathomics did not significantly improve discrimination. Conclusions: Within the development cohort, pathomics added modest but significant incremental value and identified node-negative patients at elevated risk. Because the model requires the resection specimen, it cannot guide preoperative treatment selection. External validation did not confirm incremental value: the clinical component; the pathomics signature did not.

Chronic obstructive pulmonary disease and heart failure frequently coexist, and the mortality risk for patients with these comorbidities is significantly elevated in the intensive care unit (ICU) setting. Therefore, developing an interpretable model for early mortality prediction is crucial to improving the prognosis and management of these patients. This study developed models based on the Medical Information Mart for Intensive Care IV database (3167 patients) and performed external validation using the eICU Collaborative Research Database (1781 patients). Least absolute shrinkage and selection operator regression combined with the Boruta algorithm was used to screen clinical features within the first 24 hours of ICU admission. Based on the selected features, 5 machine learning models (eXtreme Gradient Boosting, Categorical Boosting, Light Gradient Boosting Machine, Random Forest, and Support Vector Machine) were constructed and compared for their performance in predicting 28-day all-cause mortality. The optimal model was interpreted using the Shapley Additive Explanations framework. In internal validation, the Categorical Boosting model demonstrated the best overall performance, achieving an area under the receiver operating characteristic curve of 0.788, with a sensitivity of 0.482 and a specificity of 0.900. In independent external validation, the model's area under the receiver operating characteristic curve was 0.739. Feature selection identified 14 key predictive variables. Shapley Additive Explanations analysis revealed that the Sequential Organ Failure Assessment score, Simplified Acute Physiology Score II score, anion gap, vasopressor use, and respiratory rate were the top 5 features contributing most to the model's predictions. This study developed and validated an interpretable machine learning model based on multicenter data for the early prediction of 28-day mortality risk in ICU patients with chronic obstructive pulmonary disease and heart failure. The model demonstrates good discriminative ability and reasonable generalizability, with its interpretability analysis clarifying core predictive factors to aid in clinical risk stratification and intervention.

Background: Pelvic lymph node metastasis is a major determinant of staging, recurrence risk, and treatment selection in cervical cancer, yet conventional magnetic resonance imaging criteria based on nodal size and morphology remain insufficient for occult nodal disease. We developed and temporally validated a multi-sequence magnetic resonance imaging radiomics-clinical fusion framework for pretreatment prediction of pelvic lymph node metastasis. Methods: This single-center retrospective-prospective cohort study included 200 women with histologically confirmed cervical cancer who underwent pretreatment pelvic magnetic resonance imaging. A retrospective training cohort (n = 100) was accrued from January 2018 to December 2022, and a prospective temporal validation cohort (n = 100) was accrued from January 2023 to December 2024. Reference standards comprised surgical histopathology in 154 patients and composite clinical-imaging adjudication in 46 patients. Primary tumors were manually segmented on axial T2-weighted imaging and registered to T1-weighted imaging, diffusion-weighted imaging/apparent diffusion coefficient maps, and dynamic contrast-enhanced imaging. An Image Biomarker Standardisation Initiative-compliant radiomics pipeline was used, including resampling, intensity discretization, reproducibility filtering, correlation pruning, and nested feature selection. Clinical, radiomics, clinical-radiomics, and deep fusion models were evaluated in the prospective cohort using discrimination, calibration, decision-curve, and reclassification analyses. Results: The deep fusion model achieved the best validation discrimination, with an area under the receiver operating characteristic curve of 0.910 (95% confidence interval, 0.845-0.959) and an area under the precision-recall curve of 0.849. At the training-derived threshold, accuracy was 0.84, sensitivity was 0.79, specificity was 0.86, positive predictive value was 0.75, negative predictive value was 0.89, and Brier score was 0.125. Deep fusion outperformed the clinical model (area under the curve, 0.730; mean delta area under the curve, 0.180; P < 0.001) and improved reclassification relative to the clinical model. Conclusion: Multi-sequence magnetic resonance imaging radiomics-clinical fusion improved pretreatment risk stratification for pelvic lymph node metastasis in cervical cancer. External multicenter validation is warranted before clinical implementation. These preliminary single-center findings should be interpreted cautiously and require multicenter external validation before broad clinical implementation.

Active tuberculosis remains a major global health problem, and distinguishing active from non-active disease on chest radiographs could accelerate triage where radiology resources are limited. We developed ShuffleNetV2 using 4,613 radiographs from two centers and evaluated it in retrospective external (1,318 patients) and prospective (560 patients) cohorts. The model achieved AUCs of 0.917 and 0.896, respectively, and performance was stable across prespecified demographic and scanner subgroups. In the prospective cohort, artificial-intelligence assistance increased radiologist AUCs to 0.897-0.937 across experience levels. These findings support ShuffleNetV2 as a rapid decision-support tool for prioritizing confirmatory clinical and microbiological assessment; positive predictions should not be used alone to initiate treatment.