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Med. Biol."],"published-print":{"date-parts":[[2026,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Radiation therapy (RT) is critical in head and neck cancer (HNC) treatment but often causes radiation-induced toxicities, such as xerostomia (RIX). While deep learning (DL)-based models show promise in predicting these toxicities, their black-box nature hinders clinical applications. This study aims to develop a robust DL model to predict RIX 12 months after RT and to leverage model interpretability techniques, specifically class activation maps (CAMs) and voxel-wise dose gradient maps (GMs), to guide personalized treatment plan optimization.\n                    <jats:italic>Approach<\/jats:italic>\n                    . A 3D ResNet-based model was trained using planning CTs, dose volumes, and salivary gland contours obtained from a retrospective cohort of 839 HNC patients. To address anatomical variations, we normalized each patient\u2019s volumes to a common reference frame using atlas normalization. To improve spatial correspondence between deep features and patient anatomy, we integrated blur pooling and adaptive average pooling, mitigating downsampling-induced voxel shifts. Model interpretability was achieved using Grad-CAM++ and GMs. Personalized plan optimization was performed on predicted RIX-positive cases by generating avoidance contours from both CAMs and GMs to guide dose reduction.\n                    <jats:italic>Main results<\/jats:italic>\n                    . The proposed model was tested on 30 independent test cases. It achieved an AUC of 0.77 with balanced sensitivity (0.71) and specificity (0.83). Among 9 predicted RIX-positive cases, GM-guided optimization converted the predictions to RIX-negative in 7 cases (77.8%), compared with 6 cases (66.7%) for CAM-guided optimization, and achieved a greater reduction in mean model-predicted RIX probability (26% versus 20%).\n                    <jats:italic>Significance<\/jats:italic>\n                    . The proposed atlas-normalized model achieved robust discrimination, and a 9-case plan-refinement analysis showed that CAM- and GM-derived spatial information could be translated into clinically constrained avoidance objectives that reduced the model-predicted RIX probability while preserving specified target and organs at risk dose requirements. These results demonstrate the feasibility of the proposed strategy for guiding clinical interventions.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeaac0","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T22:54:34Z","timestamp":1790031274000},"page":"195014","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning prediction of radiation-induced xerostomia and personalized outcome-driven treatment planning in head and neck cancer"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9448-1364","authenticated-orcid":true,"given":"Bohua","family":"Wan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd","family":"McNutt","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harry","family":"Quon","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3004-8625","authenticated-orcid":true,"given":"Junghoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"pmbaeaac0bib1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.241613","type":"journal-article","article-title":"TotalSegmentator MRI: robust sequence-independent segmentation of multiple anatomic structures in MRI","volume":"314","author":"Akinci D\u2019antonoli","year":"2025","journal-title":"Radiology"},{"key":"pmbaeaac0bib2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103631","type":"journal-article","article-title":"A hybrid deep CNN-Cov-19-Res-Net Transfer learning architype for an enhanced brain tumor detection and classification scheme in medical image processing","volume":"76","author":"Ananda Kumar","year":"2022","journal-title":"Biomed. 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