AccScience Publishing / ARNM / Online First / DOI: 10.36922/ARNM026040003
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ORIGINAL RESEARCH ARTICLE

Machine learning-based prediction of tumor volume changes in MRIdian-based adaptive radiotherapy for prostate cancer

Merve Konuk1 ,  Ziya Kemal1 ,  Ozan Toker1 ,  Serhat Aras2* ,  Banu Atalar3 ,  Orhan İçelli1
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1 Department of Physics, Science and Art Faculty, Yıldız Technical University, Istanbul, Türkiye
2 Department of Radiation Oncology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye
3 Department of Radiation Oncology, Faculty of Medicine, Acıbadem Mehmet Ali Aydınlar University, Istanbul, Türkiye
ARNM, 026040003 https://doi.org/10.36922/ARNM026040003
Received: 22 January 2026 | Revised: 12 March 2026 | Accepted: 24 April 2026 | Published online: 15 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Accurate prediction of inter-fractional tumor volume changes may improve the efficiency and personalization of magnetic resonance-guided adaptive radiotherapy in prostate cancer (PCa). This study investigates the feasibility of using machine learning algorithms to predict inter-fraction gross tumor volume (GTV) changes in PCa during adaptive radiotherapy (ART), aiming to identify the optimal similarity coefficient and model to improve ART decision-making. Retrospective magnetic resonance images from 22 PCa patients treated using the ViewRay MRIdian LINAC system were analyzed. The GTV of each patient was recorded before treatment (GTV0) and during five fractions (GTV1–5). Four different similarity coefficients—dice, Jaccard, Tanimoto, and Ochiai similarity coefficients (DSC, JSC, TSC, and OSC)—were calculated to evaluate inter-fractional GTV variations. Machine learning models—artificial neural networks (ANNs), extreme gradient boosting machine, random forests, classification and regression trees, and k-nearest neighbors—were trained with appropriate hyperparameters to predict tumor volume changes between fractions. Their predictive performances were compared to determine the most effective algorithm. The ANN model demonstrated superior performance in predicting GTV variations across treatment fractions. Among the similarity coefficients, DSC contributed most to predicting inter-fractional tumor volume changes, while OSC had the least impact. The ANN model achieved the highest predictive performance on the independent test dataset (R2 = 0.822). These findings indicate that ANN models can successfully predict inter-fractional GTV variations in PCa, thereby providing valuable support for ART decision-making. Incorporating DSC-based similarity analysis may facilitate ART planning and improve clinical outcomes. The study supports integrating AI-based methods into the ART workflow.

Graphical abstract
Keywords
Prostate cancer
Adaptive radiotherapy
MRIdian LINAC
Gross tumor volume
Machine learning
Similarity coefficients
Funding
None.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Advances in Radiotherapy & Nuclear Medicine, Electronic ISSN: 2972-4392 Print ISSN: 3060-8554, Published by AccScience Publishing
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