Deep learning for radiotherapy outcome prediction using dose data – a review

Appelt, A.L., Elhaminia, B., Gooya, A. , Gilbert, A. and Nix, M. (2022) Deep learning for radiotherapy outcome prediction using dose data – a review. Clinical Oncology, 34(2), e87-e96. (doi: 10.1016/j.clon.2021.12.002) (PMID:34924256)

Abstract

Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy prognostic modelling is still limited, however, especially as applied to toxicity and tumour response prediction from radiation dose distributions. We review and summarise studies that applied deep learning to radiotherapy dose data, in particular studies that utilised full three-dimensional dose distributions. Ten papers have reported on deep learning models for outcome prediction utilising spatial dose information, whereas four studies used reduced dimensionality (dose volume histogram) information for prediction. Many of these studies suffer from the same issues that plagued early normal tissue complication probability modelling, including small, single-institutional patient cohorts, lack of external validation, poor data and model reporting, use of late toxicity data without taking time-to-event into account, and nearly exclusive focus on clinician-reported complications. They demonstrate, however, how radiation dose, imaging and clinical data may be technically integrated in convolutional neural networks-based models; and some studies explore how deep learning may help better understand spatial variation in radiosensitivity. In general, there are a number of issues specific to the intersection of radiotherapy outcome modelling and deep learning, for example translation of model developments into treatment plan optimisation, which will require further combined effort from the radiation oncology and artificial intelligence communities.

• Item Type: Articles
• Additional Information: Funding: The authors would like to acknowledge Cancer Research UK funding (grant C19942/A28832) for the Leeds Radiotherapy Research Centre of Excellence (RadNet). A.L. Appelt is supported by Yorkshire Cancer Research Academic Fellowship funding (grant L389AA). A. Gilbert is supported by Cancer Research UK C
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gooya, Dr Ali
• Authors: Appelt, A.L., Elhaminia, B., Gooya, A., Gilbert, A., and Nix, M.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Clinical Oncology
• Publisher: Elsevier
• ISSN: 0936-6555
• ISSN (Online): 1433-2981
• Published Online: 16 December 2021