{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T03:17:35Z","timestamp":1792639055574,"version":"4.5.0"},"reference-count":31,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity\u2010modulated radiation therapy (IMRT) and three\u2010dimensional conformal radiation therapy (3D\u2010CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a decision\u2010making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatment\u00a0techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A 2D U\u2010Net convolutional neural network (CNN) model was used to predict dose distribution maps and dose\u2010volume histogram (DVH) metrics for breast cancer patients undergoing IMRT and 3D\u2010CRT. The model was trained and fine\u2010tuned 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\u2010CRT 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\u2010making\u00a0framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Evaluation metrics, including dice similarity coefficients (DSC) and DVH analyses, demonstrated high concordance between predicted and clinical dose distribution for both IMRT and 3D\u2010CRT techniques, especially for organs at risk (OARs). The decision\u2010making framework demonstrated high accuracy (90), recall (95.7), and precision (91.7) when compared to independent clinical evaluations, while the historical decision\u2010making had lower accuracy (50), recall (47.8), and precision (78.6).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed decision\u2010making model accurately predicts dose distributions for both 3D\u2010CRT and IMRT, ensuring reliable OAR dose estimation. This decision\u2010making framework significantly outperforms historical decision\u2010making, demonstrating higher accuracy, recall, and precision.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17527","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T07:30:03Z","timestamp":1733211003000},"page":"1798-1809","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Breast radiotherapy planning: A decision\u2010making framework using deep learning"],"prefix":"10.1002","volume":"52","author":[{"given":"Pedro","family":"Gallego","sequence":"first","affiliation":[{"name":"Radiof\u00edsica i Radioprotecci\u00f3 Hospital de la Santa Creu i Sant Pau Barcelona Spain"},{"name":"Department de Matem\u00e0tiques I Inform\u00e0tica Universitat de Barcelona Barcelona 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