TFE3‐DualNet: An Interpretable Foundation Model‐Based Deep Learning Ensemble for Diagnosing TFE3‐Rearranged Renal Cell Carcinoma From Whole‐Slide Images in a Two‐Center Cohort
Aug 2026· Cancer Medicine· Vol 15· 0 citations· 32 references
Medicine
Abstract
TFE3‐rearranged renal cell carcinoma (TFE3‐rRCC) is a rare, aggressive subtype that predominantly affects adolescents and young adults. Its marked morphologic heterogeneity can delay recognition and downstream confirmatory testing.
Highlights • MD-Mamba integrates state-space modeling with multi-scale dilated convolutions.• Dual-path attention improves interpretability and focuses on diagnostic tissue regions.• Achieves 96.25% accuracy with perfect malignant classification on BACH dataset.• Enables efficient, interpretable image biomarkers for breast cancer pathology.
Gengxun Liu, Shengquan Luo, Can Wu et al.· Translational Oncology· 0 citations
The 2021 WHO classification reclassified “IDH‐mutant glioblastoma (GBM)” as “Astrocytoma, IDH‐mutant, grade 4.” This study aims to provide real‐world validation of this reclassification using the specific ICD‐O‐3 code (9445/3) from the Surveillance, Epidemiology, and End Results (SEER) “Transition Era” (2018–2022) and develop a machine learning (ML)‐based prognostic model.
De-Wei Du, Amu Jike, Dongnan Yu et al.· Brain and Behavior· 0 citations
Objective
To develop and externally validate an interpretable MRI-based machine-learning model for preoperative identification of the vascular dissemination phenotype in hepatocellular carcinoma (HCC), defined by vessels encapsulating tumor clusters (VETC) and/or microvascular invasion (MVI).
Materials and Methods
This dual-center retrospective study included 642 patients with surgically confirmed HCC who underwent preoperative contrast-enhanced MRI. Patients from Institution I (n = 435) and Institution II (n = 207) formed the training and independent external validation cohorts, respectively. Clinical and conventional MRI predictors were selected using univariable logistic regression, collinearity assessment, and recursive feature elimination. Nine machine-learning models were developed and externally validated. Performance was assessed using area under the curve (AUC), sensitivity, specificity, calibration, decision-curve analysis, and subgroup analyses. SHAP was used for model interpretation, and transcriptomic analysis was performed in 30 patients.
Results
Among the nine machine-learning models, XGBoost achieved the highest observed AUCs in the training cohort (0.85; 95% CI: 0.81, 0.88) and external validation cohort (0.82; 95% CI: 0.75, 0.88), with higher AUCs than logistic regression in both cohorts (p < 0.001 and p = 0.047, respectively). In external validation, sensitivity and specificity were 71.1% and 85.5%, respectively. Calibration and decision-curve analyses supported model performance. Subgroup discrimination remained acceptable for tumors ≤ 5.0 cm and BCLC stage 0 or A disease, although sensitivity was lower for tumors ≤ 5.0 cm. SHAP identified intratumoral artery, nonsimple nodular growth type, and necrosis or severe ischemia as leading contributors. Transcriptomic analysis suggested exploratory enrichment of cell-cycle and metabolic pathways.
Conclusion
The interpretable MRI-based XGBoost model showed favorable performance for identifying the vascular dissemination phenotype in HCC, with SHAP-based interpretability and exploratory transcriptomic context.
Junhan Pan, Cong Zhang, Yi-Tian Wu et al.· Journal of Hepatocellular Ca...· 0 citations
Highlights • Prognostic tools remain limited for central conventional chondrosarcoma.• A multimodal signature integrated CT radiomics, deep learning, and pathomics.• The signature predicted progression-free survival in independent cohorts.• The signature may support postoperative risk stratification.
Qiushi Su, Jie Wu, Ben Li et al.· Translational Oncology· 0 citations
We address a fundamental ground-truth fallacy in the study's design: utilizing admittedly imperfect clinical diagnoses as the absolute standard to train classification models creates a circular logic. We propose that BRAF amplification in these cases more likely serves as a diagnostic corrective marker for misclassified dermal metastases, rather than a true prognostic indicator for isolated PDM.
Jingyang Zhang, Hai-Wen Wang, Qin Zheng et al.· British Journal of Dermatolo...· 0 citations