The RATAB Framework: An Interpretable AI-Driven Radiographic Risk Stratification Model for Suspected Lung Lesions

Kahraman E. G., Cohen J. P., Ozdemir O., Varol Y., Erol Y., Hasbay E., ...More

JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 2026 (SCI-Expanded, Scopus)

Abstract

The objective of this study is to develop and evaluate the RATAB (Radiological Assessment To Avoid Biopsy) framework as an interpretable, methodology-focused proof-of-concept for malignancy risk ranking on pre-procedural chest radiographs (CXR) using pre-trained TorchXRayVision (TxRV) features, with rigorous leakage-free validation. This retrospective single-center diagnostic accuracy study included patients who underwent transthoracic needle biopsy (TTNB) after a suspicious posteroanterior CXR and had definitive histopathology. The index radiograph was the pre-procedural PA film obtained within 7 days before biopsy. Feature representations extracted from pre-trained TorchXRayVision (TxRV) models were processed using soft-binarization with parameters fixed a priori and evaluated through a strictly leakage-free nested fivefold cross-validation protocol. Model stability was confirmed via a 10 & times; fivefold repeated cross-validation (50 folds). The primary model (MAIN_B1) was a balanced L2-regularized logistic regression on soft-binarized six-feature inputs with quadratic terms. Uncertainty was quantified by patient-level stratified bootstrap (B = 2000). Reporting followed TRIPOD and CLAIM recommendations. Overall, 285 patients (mean age, 57.8 +/- 13.2 years; 160 men) were evaluated, including 206 malignant and 79 benign cases. The primary model achieved a locked 10 & times; fivefold repeated CV AUC of 0.6883 +/- 0.0155 (single fivefold CV AUC = 0.6980 +/- 0.0450). Pooled out-of-fold (OOF) AUC was 0.6998 (95% CI 0.6324-0.7659). At the mean training-fold Youden threshold (0.492), OOF sensitivity was 0.670 (95% CI 0.607-0.738), specificity 0.658 (95% CI 0.544-0.759), and cohort PPV 0.836 (95% CI 0.792-0.882); fold-averaged operating metrics were sensitivity 64.6%, specificity 67.1%, and PPV 83.8%. In a theoretical Bayes recalculation at 25% prevalence, PPV was 0.395 (0.328-0.488) and NPV 0.850 (0.824-0.887). Brier score was 0.2210 (95% CI 0.2042-0.2379), with calibration intercept 0.925 (0.848-1.017) and slope 0.980 (0.605-1.457). The RATAB framework delineates the realistic performance boundaries of pre-trained AI representations on CXR. While providing interpretable risk ranking, its prevalence-dependent theoretical predictive values underscore that CXR-based AI cannot substitute for cross-sectional CT evaluation.