Radiological tumor classification across imaging modality and histology.

Jia Wu (Lead / Corresponding author), Chao Li, Michael Gensheimer, Sukhmani Padda, Fumi Kato, Hiroki Shirato, Yiran Wei, Carola-Bibiane Schönlieb, Stephen John Price, David Jaffray, John Heymach, Joel W. Neal, Billy W. Loo Jr, Heather Wakelee, Maximilian Diehn, Ruijiang Li

Research output: Contribution to journalArticlepeer-review


Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for the prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumour 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 tumour 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 tumour 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 tumour-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumour segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumour classification may inform prognosis and treatment response for precision medicine.
Original languageEnglish
Pages (from-to)787–798
Number of pages11
JournalNature Machine Intelligence
Issue number9
Early online date9 Aug 2021
Publication statusPublished - Sept 2021


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