Recent advances in the clinical applications of machine learning in proton therapy

ABSTRACT The present systematic review is an effort to explore the different clinical applications and current implementations of machine/deep learning in proton therapy. It will assist as a reference for scientists, researchers, and other health professionals who are working in the field of proton radiation therapy and need up-to-date knowledge regarding recent technological advances. This review utilized Pubmed and Embase to search for and identify research studies of interest published between 2019 and 2024. This systematic literature review utilized PubMed and Embase to search for and identify studies pertinent to machine learning in proton therapy. The time period of 2019 to 2024 was chosen to capture the most recent signficant advances. An initial search on PubMed was made with the search strategy “‘proton therapy’, ‘machine learning’, ‘deep learning’”, with filters including only research articles from 2019 to 2024, returning 84 results. Next, “(“proton therapy”) AND (“machine learning” OR “deep learning”)” was searched on Embase, retrieving 546 results. When filtered between 2019 to 2024 and to only research articles, 250 results were retrieved on Embase. Reviews, editorials, technical notes, and articles in any language other than English were excluded from the broad search on both databases. Filtering by title, papers were chosen based on two inclusion factors: explicit application to, or mention of, proton therapy, and inclusion of a machine learning algorithm. Assessing by abstract, works irrelevant to specific aspects of the proton therapy workflow in the scope of the review were excluded. Upon assessing and evaluating full texts for quality, studies were excluded that lacked a clear explanation of model architecture. If multiple studies of the same architecture applied to the same workflow step were identified, chronologically only the most recent advancement in application was included. An additional 5 studies that met all inclusion criteria were identified from references of chosen papers. In total, 38 relevant studies have been summarized and incorporated into this review. This is the first systematic review to comprehensively cover all current and potential areas of application of machine learning to the proton therapy clinical workflow. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award number EB033994 (to A.H.K.) Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes The funding declaration has been updated with appropriate information. Data Availability All data and studies referenced are publicly available in the cited sources.