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Towards interpretable AI in personalized medicine through a radiological-biological radiomics dictionary linking semantic Lung-RADS and imaging radiomics features
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DOI：10.1016/j.jbi.2026.105047.png)
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Abstract
En 中文
Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival largely dependent on early detection. Standard-dose computed tomography (CT) screening, guided by the Lung Imaging Reporting and Data System (Lung-RADS), provides standardized criteria for nodule evaluation. However, interpretation is limited by inter-reader variability and reliance on qualitative descriptors. Radiomics offers quantitative biomarkers but faces challenges of clinical interpretability. In this work, we introduce a radiological-biological dictionary of radiomic features (RFs) that aligns quantitative metrics with Lung-RADS semantic categories, thereby bridging computational and clinical reasoning.
Keywords:
Lung-RADS
Radiomics
Explainable and interpretable AI
Semantic mapping
Radiological-biological radiomics dictionary
Precision oncology
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