An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification

2026-07-03

AI Summary

This study addresses the limited interpretability of current deep learning approaches in medical image–based tumor classification, which often fail to yield biologically meaningful imaging biomarkers. To overcome this, the authors propose an integrated framework that combines deep learning–based segmentation, Grad-CAM attention guidance, and radiomics analysis. Individualized quantitative imaging biomarkers are extracted from diagnostically relevant regions using mutual information–driven adaptive thresholding. These biomarkers are then validated and interpreted through conventional machine learning classifiers enhanced with SHAP (SHapley Additive exPlanations). Evaluated across multiple public and private tumor imaging datasets, the proposed method significantly outperforms whole-tumor radiomics baselines while maintaining high classification accuracy. Crucially, it enables the discovery of reproducible, interpretable, and biologically plausible imaging biomarkers, thereby bridging the gap between data-driven deep learning and clinically actionable insights.

Abstract

Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning. Although deep learning has shown great potential for imaging signature discovery, its limited interpretability remains a major barrier to clinical adoption. Existing approaches often achieve high predictive performance but provide little biological insight into the identified signatures. We propose a unified framework for interpretable imaging signature discovery by integrating deep learning based segmentation, explainable classification, and radiomic analysis. A robust segmentation model is first used to accurately delineate tumors, followed by a Grad-CAM guided pipeline that identifies diagnostically important regions as candidate imaging signatures. A mutual information based adaptive thresholding strategy enables patient-specific signature extraction. The resulting signatures are validated using a downstream deep learning classification model, while radiomic features extracted from the signature regions are evaluated with traditional machine learning models and interpreted using SHAP to identify the most discriminative biomarkers. The proposed framework is evaluated on the public BUSI breast ultrasound, KiTS renal CT, and BraTS brain tumor datasets, as well as a private UF Health renal CT cohort. Compared with conventional whole-tumor radiomics, the proposed signature-based approach achieves improved discriminative performance while providing greater biological interpretability. By converting deep learning attention into reproducible quantitative imaging biomarkers, this framework offers an interpretable and reproducible solution for non-invasive tumor characterization and imaging biomarker discovery.

Problem

• interpretability
• deep learning
• radiomic signatures
• tumor classification
• imaging biomarkers

Innovation

• interpretable deep learning
• radiomic signatures
• Grad-CAM
• mutual information thresholding
• SHAP interpretability