2026-11-04

Objective: To develop a pathology-based preoperative decision-support system (Structured Imaging Report System for Pancreatic Cystic Lesions, SIRS-PCL) for subtype classification and risk stratification of PCLs. Methods: We conducted a multicentre retrospective study involving 1,102 patients treated between 1 January 2011 and 31 December 2024. The study cohort was divided into a development dataset (n = 858) and a temporal validation dataset (n = 244) from the periods 2011-2022 and 2023-2024, respectively. Three logistic prediction models were used to classify the five PCL subtypes and to perform separate three-tier risk stratifications (benign/low, medium, and high) for intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) cases. SIRS-PCL was prospectively implemented at Tianjin Medical University Cancer Institute and Hospital from 25 June 2025 to 25 June 2026, and the diagnostic accuracy, sensitivity, and specificity were evaluated in a cohort of 76 patients. Results: A total of 1,102 patients with PCLs were included in the study (median age 56 years, interquartile range 44-64), of whom 714 were female (64.8%). The five-category model included in SIRS-PCL achieved a mean area under the receiver operating characteristic curve (AUC) of 0.93 [95% confidence interval (CI) 0.91-0.95] in temporal validation. The IPMN model showed good discrimination for risk stratification (mean AUC 0.86, 95% CI 0.79-0.93), with the highest overall accuracy of 77.1% exceeding current guidelines (range 50.0%-58.6%). The MCN risk model also showed high performance (mean AUC 0.97, 95% CI 0.91-1.00). In comparison to current practice, SIRS-PCL use reduced unnecessary surgeries 11.5% vs. 23.1% and over-examinations 6.7% vs 33.7%. In the prospective implementation cohort, SIRS-PCL achieved accuracy of 86.2% for five-category pathological classification, 73.7% for IPMN risk stratification, and 100% for MCN risk stratification. Conclusions: SIRS-PCL is a pathology-based, quantitative decision-support system for preoperative pathological subtype classification and pathological risk stratification of surgically evaluated neoplastic PCLs.

Objective: The aim in this study was to characterize the 8-week hemoglobin response among patients with anemia receiving Zaizao Shengxue capsule (ZSC) in real-world care and develop and externally evaluate a model for estimating the individual probability of response. Methods: This prospective multicenter study included 440 patients from 14 centers who received ZSC for 8 weeks. Response was defined as an increase in hemoglobin of at least 20 g/L from baseline at week 8. The development cohort (n = 338) was divided into a training cohort (n = 238) and an internal validation cohort (n = 100). Baseline predictors were selected in the training cohort. Ten candidate models were trained using different algorithms and compared in the internal validation cohort. After model selection and locking, the final model was independently evaluated in 102 patients from four external centers. SHAP and LIME were used for model interpretation. Results: Overall, 204 of 440 patients (46.4%) achieved an 8-week hemoglobin response. Response rates varied across anemia etiologies: 24.0% in cancer-related anemia (60/250), 74.4% in iron-deficiency anemia (67/90), and 77.0% in anemia of chronic disease (77/100). Anemia etiology, age, and estimated glomerular filtration rate (eGFR) were retained as predictors in the final Extra Trees model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.821 (95% CI, 0.729-0.902) in internal validation and 0.769 (95% CI, 0.670-0.860) in external validation. SHAP analysis identified anemia etiology as the largest contributor to model predictions, followed by age and eGFR. Conclusion: Eight-week hemoglobin response varied across anemia etiologies among patients receiving ZSC. A model incorporating anemia etiology, age, and eGFR may support individualized pretreatment estimation of response probability based on routinely available clinical information.

Introduction: To develop and evaluate a multimodal deep learning (DL) model for subtype classification of viral keratitis. Methods: This retrospective multicenter study included 3,501 slit-lamp images (807 eyes) as the internal development set and two external validation cohorts (2,957 images total). DeepFusion-HSK integrates three complementary illumination modalities-diffuse light imaging (DLI), slit-beam imaging (SBI), and fluorescein staining imaging (FSI)-through a novel fusion architecture. The framework uses a tri-way higher-order interaction layer to capture cross-modal pathological signatures, dual-task learning with auxiliary heads to prevent modality-specific feature degradation, and a dynamic gating mechanism that uses high-confidence coarse priors to calibrate fine-grained predictions and mitigate softmax probability spillover. To simulate real-world data deficiency, we evaluated model resilience under complete FSI dropout. Results: On the internal test set, DeepFusion-HSK achieved an accuracy of 85.1% (95% CI: 78.5%-90.0%) and AUCs of 0.947-0.949 for discriminating epithelial herpes simplex keratitis (E-HSK) and non-epithelial herpes simplex keratitis (NE-HSK), significantly outperforming Swin-Transformer, EfficientNetV2, and ResNet50. External validation confirmed robust generalization, with accuracies of 81.7% on Dataset I and 78.2% on Dataset II, demonstrating clinical resilience. Ablation studies confirmed the hierarchical synergy of multimodal inputs: the best single modality (FSI) yielded 78.5% accuracy, the optimal dual-modality combination (DLI + FSI) reached 81.7%, and full tri-modal fusion improved performance to 85.1%, validating that integrated global morphology, depth contour, and superficial ulcer signatures are essential for robust HSK subtyping. Discussion: DeepFusion-HSK offers a robust, generalizable, and clinically deployable solution for therapeutic HSK subtyping. Its resilience to missing imaging modalities makes it well suited for heterogeneous real-world ophthalmic practice, providing a decision-support tool to reduce vision-threatening misdiagnoses and guide therapy.