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0This study introduces a bio-interpretable ensemble learning model designed to accurately categorize the severity of invasive pulmonary adenocarcinoma (IPA). By integrating computed tomography (CT) scans with whole slide pathology images, the researchers developed a multimodal tool that identifies high-grade tumors more reliably than traditional manual assessments. Beyond mere prediction, the project connects specific imaging phenotypes to underlying biological pathways, such as cell proliferation and DNA repair, through extensive genomic analysis. Results across multiple medical centers demonstrate that combining macro-scale radiological data with micro-scale pathological details significantly improves diagnostic consistency. Ultimately, this multi-omics approach offers a stable benchmark for personalized lung cancer treatment by bridging the gap between artificial intelligence and biological transparency.
References:
Yang Z, Li F, Han Q, et al. Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images[J]. NPJ Precision Oncology, 2025.
