{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:50:55Z","timestamp":1774158655319,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003030","name":"Ag\u00e8ncia de Gesti\u00f3 d'Ajuts Universitaris i de Recerca","doi-asserted-by":"publisher","award":["2021SGR01094"],"award-info":[{"award-number":["2021SGR01094"]}],"id":[{"id":"10.13039\/501100003030","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Breast cancer is one of the most prevalent malignancies in women, with radiotherapy (RT) playing a key role in its treatment. Advances in RT techniques, such as 3D conformal radiotherapy (3D\u2010CRT) and intensity\u2010modulated radiotherapy (IMRT), have improved dose precision and reduced side effects. However, RT modality selection and treatment planning remain manual, time\u2010consuming, and subject to variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study presents and validates TARS\u2010B (Treatment Automation and Radiotherapy Selection for Breast Cancer), an automated framework that combines a deep learning\u2010based decision\u2010making module (DMF) for selecting the optimal RT technique and a fully automated treatment planning system (ATP) for generating deliverable plans that meet clinical quality standards and are deemed acceptable for clinical use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>TARS\u2010B functions in two stages. First, the DMF analyzes individual patient data to determine whether 3D\u2010CRT or IMRT is more appropriate. Second, the ATP generates the corresponding treatment plan. For 3D\u2010CRT, a field\u2010in\u2010field (FiF) method is used to enhance dose homogeneity and minimize hotspots. For IMRT, the DMF provides neural network\u2010based dose predictions, which are used to generate constraints for organs\u2010at\u2010risk (OARs). Both processes are fully scripted within the treatment planning system (TPS).<\/jats:p>\n                    <jats:p>The framework was tested on 60 breast cancer patients: 30 originally treated with 3D\u2010CRT and 30 with IMRT. Two analyses were conducted. First, the ATP's performance was evaluated by comparing automated plans with their manually generated clinical counterparts for both techniques. Second, the full TARS\u2010B pipeline was assessed by applying the DMF to select the RT modality and automatically generating the plan, comparing results to the original clinical plans. Dosimetric parameters, including planning target volume (PTV) coverage, OAR constraints, and low\u2010 and intermediate\u2010dose bath, were analyzed. Planning times were also compared.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>No statistically significant differences () were found between manual and automated plans in key dosimetric metrics, including PTV coverage (V95), hotspots (V105), and OAR constraints, for both 3D\u2010CRT and IMRT. TARS\u2010B confirmed the appropriateness of 3D\u2010CRT in all patients originally treated with it and recommended re\u2010planning with 3D\u2010CRT for 15 of 30 IMRT cases. Of these, 14 re\u2010plans met all criteria; one failed due to anatomical\u00a0anomalies.<\/jats:p>\n                    <jats:p>Re\u2010planning led to a reduction in low\u2010dose bath (up to 2800 ) and intermediate\u2010dose bath (up to 3000 ). The reduction in intermediate\u2010dose bath was statistically significant (). Planning times decreased substantially: from  to  min for IMRT, and from  to  min for 3D\u2010CRT ().<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>TARS\u2010B effectively automates both the selection of the most appropriate RT technique and the generation of high\u2010quality treatment plans. This framework improves workflow efficiency, reduces planning time, and preserves dosimetric quality, highlighting its potential for clinical implementation in breast cancer\u00a0RT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70491","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:08:17Z","timestamp":1772964497000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From data to treatment plan: An AI\u2010driven path for automated breast radiotherapy planning"],"prefix":"10.1002","volume":"27","author":[{"given":"P.","family":"Gallego","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology\u2010Medical Physics Hospital de la Santa Creu i Sant Pau Barcelona Spain"},{"name":"Department of Mathematics and Computer Science University of Barcelona Barcelona Spain"},{"name":"Institut de Recerca Sant Pau (IR SANT PAU) Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E.","family":"Ambroa","sequence":"additional","affiliation":[{"name":"Servei de F\u00edsica i Protecci\u00f3 Radiol\u00f2gica Hospital del Mar Barcelona Spain"},{"name":"Department of Mathematics and Computer Science University of Barcelona Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"P\u00e9rez\u2010Alija","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology\u2010Medical Physics Hospital de la Santa Creu i Sant Pau Barcelona Spain"},{"name":"Institut de Recerca Sant Pau (IR SANT PAU) Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. 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