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Choosing between intensity‐modulated radiation therapy (IMRT) and three‐dimensional conformal radiation therapy (3D‐CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints. Purpose This study aims to develop a decision‐making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatment techniques. Methods A 2D U‐Net convolutional neural network (CNN) model was used to predict dose distribution maps and dose‐volume histogram (DVH) metrics for breast cancer patients undergoing IMRT and 3D‐CRT. The model was trained and fine‐tuned using retrospective datasets from two medical centers, accounting for variations in CT systems, dosimetric protocols, and clinical practices, over 346 patients. An additional 30 consecutive patients were selected for external validation, where both 3D‐CRT and IMRT plans were manually created. To show the potential of the approach, an independent medical physicist evaluated both dosimetric plans and selected the most appropriate one based on applicable clinical criteria. Confusion matrices were used to compare the decisions of the independent observer with the historical decision and the proposed decision‐making framework. Results Evaluation metrics, including dice similarity coefficients (DSC) and DVH analyses, demonstrated high concordance between predicted and clinical dose distribution for both IMRT and 3D‐CRT techniques, especially for organs at risk (OARs). The decision‐making framework demonstrated high accuracy (90), recall (95.7), and precision (91.7) when compared to independent clinical evaluations, while the historical decision‐making had lower accuracy (50), recall (47.8), and precision (78.6). Conclusions The proposed decision‐making model accurately predicts dose distributions for both 3D‐CRT and IMRT, ensuring reliable OAR dose estimation. This decision‐making framework significantly outperforms historical decision‐making, demonstrating higher accuracy, recall, and precision. "],"publicationDate":"2024-12-03","publisher":"Wiley","embargoEndDate":null,"sources":["Crossref","Med Phys","MEDICAL PHYSICS","ISSN: 00942405","ISSNe: 24734209","reponame:r-IIB SANT PAU. 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