{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:45:13Z","timestamp":1792071913787,"version":"4.4.0"},"reference-count":32,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L2609075"],"award-info":[{"award-number":["L2609075"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12375339"],"award-info":[{"award-number":["12375339"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11675042"],"award-info":[{"award-number":["11675042"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate dose\u2010volume histogram (DVH) prediction is essential for high\u2010quality and automated radiotherapy (RT) planning. However, existing deep learning methods primarily focus on voxel\u2010level dose prediction followed by post\u2010processing to extract DVHs, a workflow that can introduce cumulative errors and limit clinical interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>In this study, we propose a novel deep learning framework that directly predicts full DVHs for multiple organs\u2010at\u2010risk (OARs) from computed tomography (CT) and structure contours.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our method combines a three\u2010dimensional convolutional neural network (CNN) for anatomical feature extraction with a graph neural network (GNN) that models each DVH as a structured graph, enabling sequential dose\u2010volume dependencies to be learned explicitly. The model was extensively validated on nasopharyngeal cancer cases from an external institute and rectal cancer cases from our affiliated hospital, encompassing treatments with TomoTherapy and volumetric modulated arc therapy (VMAT). We additionally conducted a model\u2010guided clinical DVH assessment on suboptimal cases to demonstrate the method's clinical utility<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The proposed method achieved low dose\u2010wise prediction errors and outperformed previous approach. Specifically, it reduced the mean dose error for the brain stem from 1.24\u00a0Gy to 0.66\u00a0Gy and for the larynx from 1.53\u00a0Gy to 0.78\u00a0Gy, with notable improvements also observed in the parotid glands and temporal lobes. Statistical analysis of clinical indices demonstrated non\u2010inferiority to ground\u2010truth plans (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&gt;\u00a00.05). Moreover, the predicted DVHs effectively guided plan revision and improved plan quality.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our results suggest that direct DVH prediction using a CNN\u2010GNN framework offers a robust and clinically interpretable solution for treatment planning and quality assurance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70663","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T07:08:25Z","timestamp":1788505705000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable graph\u2010conditioned CNNs for dose\u2010volume histogram prediction in radiotherapy"],"prefix":"10.1002","volume":"53","author":[{"given":"Xiaoling","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology Shanghai Medical College Fudan University  Shanghai China"},{"name":"Shanghai Clinical Research Center for Radiation Oncology  Shanghai China"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chifang","family":"Ling","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology Shanghai Medical College Fudan University  Shanghai China"},{"name":"Shanghai Clinical Research Center for Radiation Oncology  Shanghai China"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Fudan University Shanghai Cancer 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