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Treatment replanning ensures patients do not receive excessive doses to normal tissue. However, it is a time\u2010 and resource\u2010intensive process, as it takes 1 to 2 weeks to acquire a new treatment plan, and during this time, overtreatment of normal tissues could lead to increased toxicities. Currently, there are limited prognostic factors to determine which patients will require a replan. There remains an unmet need for predictive models to assist in identifying patients who could benefit from the knowledge of a replan prior to treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>We aimed to develop and evaluate a CT\u2010based radiomic model, integrating clinical and dosimetric information, to predict the need for a replan prior to treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A dataset of patients (<jats:italic>n<\/jats:italic>\u00a0=\u00a0315) with OPC treated with chemoradiation was used for this study. The dataset was split into independent training (<jats:italic>n<\/jats:italic>\u00a0=\u00a0220) and testing (<jats:italic>n<\/jats:italic>\u00a0=\u00a095) datasets. Tumor volumes and organs at risk (OARs) were contoured on planning CT images. PyRadiomics was used to compute radiomic image features (<jats:italic>n<\/jats:italic>\u00a0=\u00a01218) on the original and filtered images from each of the primary tumor, nodal volumes, and ipsilateral and contralateral parotid glands. Nine clinical features and nine dose features extracted from the OARs were collected and those significantly (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05) associated with the need for a replan in the training dataset were used in a baseline model. Random forest feature selection was applied to select the optimal radiomic features to predict replanning. Logistic regression, Na\u00efve Bayes, support vector machine, and random forest classifiers were built using the non\u2010correlated selected radiomic, clinical, and dose features on the training dataset and performance was assessed in the testing dataset. The area under the curve (AUC) was used to assess the prognostic value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 78 patients (25%) required a replan. Smoking status, nodal stage, base of tongue subsite, and larynx mean dose were found to be significantly associated with the need for a replan in the training dataset and incorporated into the baseline model, as well as into the combined models. Five predictive radiomic features were selected (one nodal volume, one primary tumor, two ipsilateral and one contralateral parotid gland). The baseline model comprised of clinical and dose features alone achieved an AUC of 0.66 [95% CI: 0.51\u20130.79] in the testing dataset. The random forest classifier was the top\u2010performing radiomics model and achieved an AUC of 0.82 [0.75\u20130.89] in the training dataset and an AUC of 0.78 [0.68\u20130.87] in the testing dataset, which significantly outperformed the baseline model (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.023, testing dataset).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This is the first study to use radiomics from the primary tumor, nodal volumes, and parotid glands for the prediction of replanning for patients with OPC. Radiomic features augmented clinical and dose features for predicting the need for a replan in our testing dataset. Once validated, this model has the potential to assist physicians in identifying patients that may benefit from a replan, allowing for better resource allocation and reduced toxicities.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16893","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T13:50:09Z","timestamp":1702648209000},"page":"3510-3520","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model"],"prefix":"10.1002","volume":"51","author":[{"given":"Tricia A.","family":"Chinnery","sequence":"first","affiliation":[{"name":"Department of Medical Biophysics Western University  London Ontario Canada"},{"name":"Baines Imaging Research Laboratory  London Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pencilla","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Oncology Western University  London Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony C.","family":"Nichols","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Western University  London Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah A.","family":"Mattonen","sequence":"additional","affiliation":[{"name":"Department of Medical Biophysics Western University  London Ontario Canada"},{"name":"Baines Imaging Research Laboratory  London Ontario Canada"},{"name":"Department of Oncology Western University  London Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.21.01961"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.06.025"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/1533034616662165"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2005.07.957"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/ajco.12516"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.579917"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/hed.23477"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20160085"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2017.01.039"},{"key":"e_1_2_8_11_1","unstructured":"JiaF.Data\u2010Driven Two\u2010stage Appointment Radiotherapy Scheduling Model for Resource Optimization at a Tertiary Cancer Center. doi:10.21203\/rs.3.rs\u20102693973\/v1"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.01050"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"e_1_2_8_16_1","unstructured":"Limbus AI\u2014Automatic Contouring for Radiation Therapy. 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