Radiomics and AI in Oncology Imaging: From Diagnosis to Outcome Prediction
This perspective from Nature Reviews Clinical Oncology examines the next generation of AI tools for cancer imaging, moving beyond diagnosis toward prognostication, treatment response prediction, and molecular profiling from radiology images. The authors contrast hand-crafted radiomic features with deep learning representations and address critical barriers to clinical adoption including data curation, interpretability, and regulatory challenges.
The original study
Predicting cancer outcomes with radiomics and artificial intelligence in radiology.
- Authors
- Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A
- Journal
- Nature reviews. Clinical oncology
- Type
- Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Review
- PMID
- 34663898
Original abstract
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.