{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:20:37Z","timestamp":1776205237904,"version":"3.50.1"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Radiation therapy (RT) is a cornerstone in the management of localized and locally advanced prostate cancer, traditionally delivered with a full bladder (FB) protocol to reduce radiation exposure to surrounding organs. However, consistent bladder filling is difficult to maintain, leading to workflow delays, anatomical inconsistencies, and variable toxicity outcomes. Recent evidence, including the ongoing RELIEF trial at Mayo Clinic, suggests that an empty bladder (EB) protocol provides comparable toxicity outcomes to FB while improving patient comfort and treatment consistency. To address the increased anatomical variability associated with EB protocols, we developed a deep learning (DL)-based dose prediction model tailored to EB patients. A conditional generative adversarial network (cGAN) with a modified 3D U-Net architecture was trained on 90 FB cases and fine-tuned on 20 EB cases stratified into stereotactic body radiotherapy (SBRT) and intensity-modulated radiotherapy (IMRT). Model performance was evaluated against clinical manual plans using mean absolute percentage error (MAPE) and dose-volume histogram (DVH) metrics. The EB Fine-tuning model(SBRT\/IMRT) achieved superior accuracy compared with the general FB-trained model, with an average MAPE of 3.53 \u00b1 0.40% versus 4.87 \u00b1 0.86%. DVH analyses demonstrated improved agreement with manual plans for planning target volumes and organs at risk, with discrepancies consistently within 2.5 Gy or 3%. These results demonstrate that fine-tuning with EB-specific data enhances prediction accuracy and clinical relevance of the DL-based model. The proposed framework supports efficient EB treatment planning, provides reference dose distributions for quality assurance, and offers educational value to clinicians adopting EB protocols. By combining automation with clinical applicability, this approach facilitates broader adoption of EB radiotherapy in prostate cancer while improving workflow reproducibility and patient-centered care.<\/jats:p>","DOI":"10.3389\/fonc.2025.1690416","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T06:46:12Z","timestamp":1765953972000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based dose prediction for prostate cancer with empty bladder protocol: a framework for efficient and personalized radiotherapy planning"],"prefix":"10.3389","volume":"15","author":[{"given":"Byongsu","family":"Choi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak K.","family":"Shrestha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert","family":"Attia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brad J.","family":"Stish","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Leenstra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean Claude","family":"Rwigema","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiansen","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Uk","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong Hwi","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongeun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JeongHeon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Beltran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justin C.","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2005.08.016","article-title":"Prostate position variability and dose\u2013volume histograms in radiotherapy for prostate cancer with full and empty bladder","volume":"64","author":"Pinkawa","year":"2006","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2003.10.039","article-title":"Impact of changes in bladder and rectal filling volume on organ motion and dose distribution of the bladder in radiotherapy for urinary bladder cancer","volume":"59","author":"Fokdal","year":"2004","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B3","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.ijrobp.2003.10.018","article-title":"Radiotherapy for muscle-invasive carcinoma of the bladder: results of a randomized trial comparing conventional whole bladder with dose-escalated partial bladder radiotherapy","volume":"59","author":"Cowan","year":"2004","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B4","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s00066-010-2105-6","article-title":"Variability in bladder volumes of full bladders in definitive radiotherapy for cases of localized prostate cancer","volume":"186","author":"Nakamura","year":"2010","journal-title":"Strahlenther Onkol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2006.05.007","article-title":"Variability of bladder filling in patients receiving radical radiotherapy to the prostate","volume":"79","author":"O'Doherty","year":"2006","journal-title":"Radiother Oncol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-024-02306-7","article-title":"Full bladder, empty rectum? Revisiting a paradigm in the era of adaptive radiotherapy","author":"Malygina","year":"2025","journal-title":"Strahlenther Onkol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.08.019","article-title":"Inter- and intra-fractional bladder motion during radiotherapy for bladder cancer: a comparison of full and empty bladders","volume":"113","author":"Dees-Ribbers","year":"2014","journal-title":"Radiother Oncol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"S29","DOI":"10.1016\/j.radi.2019.11.071","article-title":"Is the bladder filling protocol for prostate cancer patients undergoing radiotherapy fit for purpose","volume":"26","author":"Burns","year":"2020","journal-title":"Radiography"},{"key":"B9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1120\/jacmp.v8i1.2286","article-title":"Effect of bladder filling on doses to prostate and organs at risk: a treatment planning study","volume":"8","author":"Moiseenko","year":"2006","journal-title":"J Appl Clin Med Phys"},{"key":"B10","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1186\/s13014-020-01681-6","article-title":"Strict bladder filling and rectal emptying during prostate SBRT: Does it make a dosimetric or clinical difference","volume":"15","author":"Byun","year":"2020","journal-title":"Radiat Oncol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.tipsro.2017.10.001","article-title":"The impact of bladder preparation protocols on post treatment toxicity in radiotherapy for localised prostate cancer patients","author":"Tsang","year":"2017","journal-title":"Tech Innov Patient Support Radiat Oncol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16267","article-title":"Technical note: Progressive deep learning: An accelerated training strategy for medical image segmentation","volume":"50","author":"Choi","year":"2023","journal-title":"Med Phys"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1002\/mp.1639814","article-title":"Acute coronary event (ACE) prediction following breast radiotherapy by features extracted from 3D CT, dose, and cardiac structures","volume":"50","author":"Choi","year":"2023","journal-title":"Med Phys"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3389\/fvets.2021.721612","article-title":"Deep-learning-based automatic segmentation of head and neck organs for radiation therapy in dogs","volume":"8","author":"Park","year":"2021","journal-title":"Front Vet Sci"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.3390\/cancers14102555","article-title":"Deep-learning-based automatic detection and segmentation of brain metastases with small volume for stereotactic ablative radiotherapy","volume":"14","author":"Yoo","year":"2022","journal-title":"Cancers"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.09.045","article-title":"Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer","volume":"153","author":"Choi","year":"2020","journal-title":"Radiother Oncol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.10.027","article-title":"Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices","volume":"153","author":"Kandalan","year":"2020","journal-title":"Radiother Oncol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1038\/s41598-018-37741-x","article-title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning","volume":"9","author":"Nguyen","year":"2019","journal-title":"Sci Rep"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14374","article-title":"Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy","volume":"47","author":"Bohara","year":"2020","journal-title":"Med Phys"},{"key":"B20","doi-asserted-by":"publisher","first-page":"100057","DOI":"10.1016\/j.metrad.2024.100057","article-title":"A review of dose prediction methods for tumor radiation therapy","volume":"2","author":"Kui","year":"2024","journal-title":"Meta-Radiology"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac279e","article-title":"Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy","volume":"66","author":"Olberg","year":"2021","journal-title":"Phys Med Biol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16087","article-title":"Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction","volume":"50","author":"Olberg","year":"2023","journal-title":"Med Phys"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17361","article-title":"Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL","volume":"51","author":"Choi","year":"2024","journal-title":"Med Phys"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1259\/bjr.20200548","article-title":"The implementation of an empty bladder filling protocol for localised prostate volumetric modulated arctherapy (VMAT): early results of a single institution service evaluation","volume":"93","author":"Chetiyawardana","year":"2020","journal-title":"Br J Radiol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2022.12.003","article-title":"Deep learning-based dose prediction for automated, individualized quality assurance of head and neck radiation therapy plans","volume":"13","author":"Gronberg","year":"2023","journal-title":"Pract Radiat Oncol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1016\/j.canrad.2021.06.012","article-title":"Artificial intelligence for quality assurance in radiotherapy","volume":"25","author":"Simon","year":"2021","journal-title":"Cancer Radiother"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1690416\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T06:46:18Z","timestamp":1765953978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1690416\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":26,"alternative-id":["10.3389\/fonc.2025.1690416"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1690416","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,17]]},"article-number":"1690416"}}