医学
放射性武器
放射成像
放射科
模态(人机交互)
医学影像学
计算机科学
人工智能
作者
Jia Wu,Chao Li,Michael F. Gensheimer,Sukhmani K. Padda,Fumiki Kato,Hiroki Shirato,Yiran Wei,Carola‐Bibiane Schönlieb,Stephen J. Price,D. Jaffray,John V. Heymach,Joel W. Neal,Billy W. Loo,Heather A. Wakelee,Maximilian Diehn,Ruijiang Li
标识
DOI:10.1038/s42256-021-00377-0
摘要
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
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