Predicting cancer outcomes with radiomics and artificial intelligence in radiology

By K. Bera, Nathaniel Braman, Amit Gupta, V. Velcheti, Anant Madabhushi. Nature Reviews Clinical Oncology, 2021

693 citations recorded by the source corpus.

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. Prognostication of outcome across multiple cancers and prediction of response to various treatment modalities are among the next generation of challenges that artificial intelligence (AI) tools can solve using radiology images. The authors of this Perspective describe the evolution of AI-based approaches in oncology imaging and address the path to their adoption as decision-support tools in the clinic.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cb0cae7f42d0666d9d90b1ca93416abcccef7c44

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Citations in the corpus

Segment anything in medical images (2023, background): “MedSAM, as the inaugural foundation model in medical image segmentation, holds great potential to accelerate the advancement of new diagnostic and therapeutic tools, and ultimately contribute to improved patient care [11].”
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, positive): “Similar ideas have been explored in other medical imaging domains, such as prediction of survival from computer tomography images 42,43 and prediction of molecular markers from echocardiography images 44 .”
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, methodology): “Advances in ML for computer vision have been adapted for medical imaging and are already showing great promise for rapidly and accurately analyzing a variety of imaging modalities in clinical oncology.(6,7) Although imaging informs many aspects of cancer care, molecular characterization can provide ”
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “AI in radiology is not only about detecting abnormalities; it also helps in quantifying disease progression [96], assessing response to treatment [97], and predicting patient outcomes [98].”