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Predicting cancer outcomes with radiomics and artificial intelligence in radiology

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.

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Fig. 1: Workflow for AI-enabled biomarkers in radiology.
Fig. 2: Examples of the types of radiomic feature used in oncology.
Fig. 3: Building blocks and types of neural network commonly applied to medical imaging data.
Fig. 4: Different levels of annotation detail in radiomics and deep learning studies.

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Acknowledgements

Research reported in this publication was supported by the Clinical and Translational Science Collaborative of Cleveland (UL1TR0002548) from the National Center for Advancing Translational Sciences (NCATS) component of the NIH and NIH roadmap for Medical Research; the Kidney Precision Medicine Project (KPMP) Glue Grant; the National Cancer Institute (award numbers 1F31CA221383-01A1, 1U24CA199374-01, R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01 and 1U54CA254566-01); the National Center for Research Resources (award number 1 C06 RR12463-01); the National Heart, Lung and Blood Institute (1R01HL15127701A1 and R01HL15807101A1); the National Institute of Biomedical Imaging and Bioengineering (1R43EB028736-01 and T32EB007509); the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345) and the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851); the Ohio Third Frontier Technology Validation Fund; the VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service; and The Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University; and through sponsored research agreements from AstraZeneca, Boehringer Ingelheim and Bristol Myers Squibb. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions named.

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K.B., N.B. and A.M. researched data for this manuscript. All authors contributed to all other aspects of preparation of this manuscript.

Corresponding author

Correspondence to Anant Madabhushi.

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Competing interests

N.B. is a current employee of Tempus Labs and a former employee of IBM Research, with both of which he is an inventor on several pending patents pertaining to medical image analysis. He additionally holds equity in Tempus Labs. V.V. is a consultant for Alkermes, AstraZeneca, Bristol Myers Squibb, Celgene, Foundation Medicine, Genentech, Merck, Nektar Therapeutics and Takeda, has current or pending grants from Alkermes, AstraZeneca, Bristol Myers Squibb, Genentech and Merck, is on the speakers’ bureaus of Bristol Myers Squibb, Celgene, Foundation Medicine and Novartis, and has received payment for the development of educational presentations from Bristol Myers Squibb and Foundation Medicine. A.M. holds equity in Elucid Bioimaging and Inspirata, has been or is a scientific advisory board member for Aiforia, AstraZeneca, Bristol Myers Squibb, Inspirata and Merck, serves as a consultant for Caris, Inc. and Roche Diagnostics, has sponsored research agreements with AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb and Philips, has developed a technology relating to cardiovascular imaging that has been licensed to Elucid Bioimaging, and is involved in an NIH U24 grant with PathCore and three different NIH R01 grants with Inspirata. The other authors declare no competing interests.

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Nature Reviews Clinical Oncology thanks D. Kontos, R. Mak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Cross-validation

A method of analysing model validity without an independent validation set on a limited data sample by dividing the training data into subsets for training and assessing the performance on the complementary subset of data. Several methods of cross-validation include holdout, k-fold or leave-one-out.

Elastic net survival model

Type of Cox proportional hazard model that is used to calculate hazard ratios, which are a way of evaluating the strength of the association of a variable (for example, survival outcomes) with a time point. An elastic net has the added advantage over a standard Cox model of adjusting for high dimensional data and covariates that might be correlated with each other, while making survival estimations.

Grey-level co-occurrence matrix features

Class of commonly used radiomic features, also known as Haralick features, which rely on higher-order statistics to describe the spatial arrangement and apparent position of the different grey levels present throughout the analysed image.

Kurtosis

Statistical measure to indicate the shape of a probability distribution in terms of its ‘tailedness’. High kurtosis means high deviation from the mean.

Laws’ energy measures

Eponymously named after K. I. Laws, this radiomic feature focuses on measuring variations of energy within a fixed window size, to calculate a combined texture energy of the pixels analysed.

Long short-term memory

Type of recurrent neural network that has been supplemented by the addition of recurrent or ‘forget’ gates, which enables the network to learn by looking back at propagated errors.

Skewness

Statistical measure to indicate the apparent distance between the mean and mode of a distribution. Skewness = (mean – mode)/standard deviation.

Support vector machine

Supervised machine learning model used to classify data by constructing hyperplanes and choosing the hyperplane that has the largest separation between the two classes of interest.

Tumour-infiltrating lymphocytes

(TILs). Lymphocytes that have invaded the tumour tissue from the bloodstream. In the past few years, studies have found TILs to be prognostic of survival and predictive of treatment benefit in several solid tumour types, including breast and lung tumours.

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Bera, K., Braman, N., Gupta, A. et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol (2021). https://doi.org/10.1038/s41571-021-00560-7

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