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Background Conservative treatment remains a viable option for selected patients with ectopic pregnancy (EP), but failure may lead to rupture and serious complications. Currently, serum β -hCG is the main predictor for treatment outcomes, yet its accuracy is limited. This study aimed to develop and validate a predictive model that integrates radiomic features derived from super-resolution (SR) ultrasound images with clinical biomarkers to improve risk stratification. Methods A total of 228 patients with EP receiving conservative treatment were retrospectively included, with 169 classified as treatment success and 59 as failure. SR images were generated using a deep learning-based generative adversarial network (GAN). Radiomic features were extracted from both normal-resolution (NR) and SR ultrasound images. Features with intraclass correlation coefficient (ICC) ≥ 0.75 were retained after intra- and inter-observer evaluation. Feature selection involved statistical testing and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Random forest algorithms were used to construct NR and SR models. A clinical model based on serum β -hCG was also developed. The Clin-SR model was constructed by fusing SR radiomics with β -hCG values. Model performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis (DCA). An independent temporal validation cohort ( n  = 40; 20 failures, 20 successes) was used to validation of the nomogram derived from the Clin-SR model. Results The SR model significantly outperformed the NR model in the test cohort (AUC: 0.791 ± 0.015 vs. 0.629 ± 0.083). In a representative iteration, the Clin-SR fusion model achieved an AUC of 0.870 ± 0.015, with good calibration and net clinical benefit, suggesting reliable performance in predicting conservative treatment failure. In the independent validation cohort, the nomogram demonstrated good generalizability with an AUC of 0.808 and consistent calibration across risk thresholds. Key contributing radiomic features included Gray Level Variance and Voxel Volume, reflecting lesion heterogeneity and size. Conclusions The Clin-SR model, which integrates deep learning-enhanced SR ultrasound radiomics with serum β -hCG, offers a robust and non-invasive tool for predicting conservative treatment failure in ectopic pregnancy. This multimodal approach enhances early risk stratification and supports personalized clinical decision-making, potentially reducing overtreatment and emergency interventions.

Objective To evaluate value of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection (ISMAD). Methods Symptomatic ISMAD patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, relevant risk factors for conservative treatment failure in ISMAD patients were analyzed, and a Nomogram prediction model for treatment outcome of ISMAD was constructed with risk factors. The predictive value of the model was evaluated. Results Low true lumen residual ratio (TLRR), long dissection length, and large arterial angle (superior mesenteric artery [SMA]/abdominal aorta [AA]) were identified as independent high-risk factors for conservative treatment failure ( P  < 0.05). The receiver operating characteristic curve (ROC) results showed that the area under curve (AUC) of Nomogram prediction model was 0.826 (95% CI: 0.740–0.912), indicating good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted curve and the ideal curve of the Nomogram prediction model. The decision curve analysis (DCA) analysis results showed that when probability threshold for the occurrence of conservative treatment failure predicted was 0.05–0.98, patients could obtain more net benefits. Similar results were obtained for the predictive value in the validation set. Conclusion Low TLRR, long dissection length, and large arterial angle (SMA/AA) are independent high-risk factors for conservative treatment failure in ISMAD. The Nomogram model constructed with independent high-risk factors has good clinical effectiveness in predicting the failure.

Background Nonoperative treatment, including platelet-rich plasma injection is increasingly used for partial-thickness rotator cuff tears, yet predictors of treatment failure and the incremental prognostic value of laboratory biomarkers remain unclear. We aimed to identify predictors of short-term treatment failure under nonoperative management and to evaluate whether inflammatory and metabolic biomarkers improve risk stratification beyond clinical and imaging variables. Methods We conducted a prospective cohort study at Hai Phong International Hospital from 2020 to 2025 including patients with magnetic resonance imaging–confirmed partial-thickness supraspinatus tears. Patients received either platelet-rich plasma injection or conservative treatment and were followed for 6 months. Clinical failure was defined as less than 30% improvement in pain (Visual Analog Scale) or function (Quick Disabilities of the Arm, Shoulder and Hand score). Multivariable logistic regression models were developed in the overall nonoperative cohort to identify independent predictors of failure. Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was evaluated using calibration slope and intercept. Results Of 2,502 screened patients, 939 (284 platelet-rich plasma; 655 conservative treatment) completed follow-up and were included in analyses. In the analytic cohort, failure occurred in 33.8% of platelet-rich plasma–treated patients and 64.9% of conservatively managed patients, although continuous improvements were broadly comparable between groups.In adjusted analyses, baseline tear size was the strongest and most consistent predictor of clinical failure, with approximately threefold higher odds per 1-SD increase. Inflammatory and metabolic biomarkers did not show independent associations after multivariable adjustment. The clinical–imaging model demonstrated acceptable discrimination (area under the curve 0.721, 95% confidence interval 0.687–0.755) and good calibration (slope 0.993; intercept 0.001). Addition of inflammatory and metabolic biomarkers did not meaningfully improve discrimination or calibration. Conclusions Structural severity, particularly tear size, was the strongest predictor of short-term clinical failure in partial-thickness supraspinatus tears managed nonoperatively. Routine inflammatory and metabolic biomarkers provided limited incremental prognostic value beyond clinical and imaging variables. These findings are based on internal validation and require external validation before broader clinical application.

Objectives To develop and validate an interpretable machine learning model based on clinicoradiological features and radiomic features based on magnetic resonance imaging (MRI) to predict the failure of conservative treatment in lateral epicondylitis (LE). Methods This retrospective study included 420 patients with LE from three hospitals, divided into a training cohort ( n  = 245), an internal validation cohort ( n  = 115), and an external validation cohort ( n  = 60). Patients were categorized into conservative treatment failure ( n  = 133) and conservative treatment success ( n  = 287) groups based on the outcome of conservative treatment. We developed two predictive models: one utilizing clinicoradiological features, and another integrating clinicoradiological and radiomic features. Seven machine learning algorithms were evaluated to determine the optimal model for predicting the failure of conservative treatment. Model performance was assessed using ROC, and model interpretability was examined using SHapley Additive exPlanations (SHAP). Results The LightGBM algorithm was selected as the optimal model because of its superior performance. The combined model demonstrated enhanced predictive accuracy with an area under the ROC curve (AUC) of 0.96 (95% CI: 0.91, 0.99) in the external validation cohort. SHAP analysis identified the radiological feature “CET coronal tear size” and the radiomic feature “AX_log-sigma-1-0-mm-3D_glszm_SmallAreaEmphasis” as key predictors of conservative treatment failure. Conclusions We developed and validated an interpretable LightGBM machine learning model that integrates clinicoradiological and radiomic features to predict the failure of conservative treatment in LE. The model demonstrates high predictive accuracy and offers valuable insights into key prognostic factors.