{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T23:44:46Z","timestamp":1789515886260,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA237269"],"award-info":[{"award-number":["R01CA237269"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA254377"],"award-info":[{"award-number":["R01CA254377"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA258987"],"award-info":[{"award-number":["R01CA258987"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,12]]},"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Throughout a patient's course of radiation therapy, maintaining accuracy of their initial treatment plan over time is challenging due to anatomical changes\u2010for example, stemming from patient weight loss or tumor shrinkage. Online adaptation of their RT plan to these changes is crucial, but hindered by manual and time\u2010consuming processes. While deep learning (DL) based solutions have shown promise in streamlining adaptive radiation therapy (ART) workflows, they often require large and extensive datasets to train population\u2010based models.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n This study extends our prior research by introducing a minimalist approach to patient\u2010specific adaptive dose prediction. In contrast to our prior method, which involved fine\u2010tuning a pre\u2010trained population model, this new method trains a model from scratch using only a patient's initial treatment data. This patient\u2010specific dose predictor aims to enhance clinical accessibility, thereby empowering physicians and treatment planners to make more informed, quantitative decisions in ART. We hypothesize that patient\u2010specific DL models will provide more accurate adaptive dose predictions for their respective patients compared to a population\u2010based DL model.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We selected 33 patients to train an adaptive population\u2010based (AP) model. Ten additional patients were selected, and their respective initial RT data served as single samples for training patient\u2010specific (PS) models. These 10 patients contained an additional 26 ART plans that were withheld as the test dataset to evaluate AP versus PS model dose prediction performance. We assessed model performance using Mean Absolute Percent Error (MAPE) by comparing predicted doses to the originally delivered ground truth doses. We used the Wilcoxon signed\u2010rank test to determine statistically significant differences in terms of MAPE between the AP and PS model results across the test dataset. Furthermore, we calculated differences between predicted and ground truth mean doses for segmented structures and determined statistical significance in the differences for each of them.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The average MAPE across AP and PS model dose predictions was 5.759% and 4.069%, respectively. The Wilcoxon signed\u2010rank test yielded two\u2010tailed\n p<\/jats:italic>\n \u2010value\u00a0=\u00a0, indicating that the MAPE differences between the AP and PS model dose predictions are statistically significant, and 95% confidence interval\u00a0=\u00a0[\u22122.1610, \u22121.0130], indicating 95% confidence that the MAPE difference between the AP and PS models for a population lies in this range. Out of 24 total segmented structures, the comparison of mean dose differences for 12 structures indicated statistical significance with two\u2010tailed\n p<\/jats:italic>\n \u2010values\u00a0<\u00a00.05.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Our study demonstrates the potential of patient\u2010specific deep learning models in application to ART. Notably, our method streamlines the training process by minimizing the size of the required training dataset, as only a single patient's initial treatment data is required. External institutions considering the implementation of such a technology could package such a model so that it only requires the upload of a reference treatment plan for model training and deployment. Our single patient learning strategy demonstrates promise in ART due to its minimal dataset requirement and its utility in personalization of cancer treatment.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/mp.16799","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T06:20:36Z","timestamp":1697782836000},"page":"7324-7337","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Single patient learning for adaptive radiotherapy dose prediction"],"prefix":"10.1002","volume":"50","author":[{"given":"Austen","family":"Maniscalco","sequence":"first","affiliation":[{"name":"Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Liang","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu\u2010Han","family":"Lin","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Jiang","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,20]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8140(91)90120-6"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0958-3947(00)00059-5"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2006.08.054"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1120\/jacmp.v12i1.3270"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12959"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2021.07.121"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2007.04.001"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2007.11.016"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2014.12.003"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/690595"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2004.07.696"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2012.10.040"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.04.023"},{"issue":"3","key":"e_1_2_9_15_1","first-page":"164","article-title":"Impact of repeat computerized tomography replans in the radiation therapy of head and neck cancers","volume":"39","author":"Bhandari V","year":"2014","journal-title":"J Med Phys Assoc Med Phys India"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2959609"},{"key":"e_1_2_9_18_1","unstructured":"LiuW ZengK SparseNet. A sparse DenseNet for image classification.arXiv preprint arXiv:180405340.2018."},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2984130"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab039b"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.580919"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-53286-z"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.02.040"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13097"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aba5eb"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14827"},{"key":"e_1_2_9_27_1","doi-asserted-by":"crossref","unstructured":"WangB TengL MeiL et\u00a0al.Deep learning\u2010based head and neck radiotherapy planning dose prediction via beam\u2010wise dose decomposition.Paper presented at: International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.2022.","DOI":"10.1007\/978-3-031-16449-1_55"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13655"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.10.038"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13040702"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.6.064003"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16056"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15352"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16616"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12884"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-021-01864-9"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16799","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T16:29:20Z","timestamp":1719505760000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.16799"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,20]]},"references-count":35,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10.1002\/mp.16799"],"URL":"https:\/\/doi.org\/10.1002\/mp.16799","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,20]]},"assertion":[{"value":"2023-04-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}