{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:01:14Z","timestamp":1792785674336,"version":"4.5.0"},"reference-count":32,"publisher":"IOP Publishing","issue":"10","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":14,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"europe","award":["18P03532\/18 E01765"],"award-info":[{"award-number":["18P03532\/18 E01765"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,5,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Introduction.<\/jats:italic>\n                    Radiation-induced optic neuropathy (RION) is a rare but functionally-disabling complication of radiotherapy for brain and head\/neck tumors. Even with modern proton therapy and adherence to dose constraints, about 6% of patients develop near-blindness from RION. Unlike regression models that estimate additive effects, a Bayesian network (BN) explicitly encodes conditional dependencies among input variables, and was used for more reliable assessment and individualized outcome prediction of RION.\n                    <jats:italic>Methods.<\/jats:italic>\n                    Demographic, ophthalmological and treatment data were prospectively collected from patients undergoing scanned proton beams between 2018 and 2024. Visual field deficit served as surrogate for grade \u2a7e2 clinically-relevant RION. Using clinical and treatment\/dosimetry variables, under expert-informed constraints, BN with Markov-blanket identified direct RION predictors; its explanatory value was evaluated via log-likelihood ratio (LLR), integrated discrimination improvement (IDI), and net reclassification index (NRI).\n                    <jats:italic>Results.<\/jats:italic>\n                    Of 179 eyes (105 patients), 24% developed RION. The BN identified 15 directional associations, 12 were clinically valid, 2 conceivable and 1 weak. The Markov-blanket of the RION node identified baseline visual field deficit, hypertension, and hypercholesterolemia as key predictors. A logistic regression model based on these variables improved patient stratification (LLR = 63.34,\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.001, IDI = 0.35, NRI = 0.25); AUC reached 0.75 (95%CI: 0.66\u20130.84). Age and dosimetric factors did not improve RION risk prediction.\n                    <jats:italic>Conclusion.<\/jats:italic>\n                    BN analysis highlighted associations between vascular comorbidities and baseline visual function and occurrence of RION, while dosimetric factors were not direct predictors. This contrast with photon models is hypothesis-generating and favors proton-specific models for RION prediction.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae752c","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:54:26Z","timestamp":1780095266000},"page":"105031","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Modeling variable interactions using Bayesian networks to identify direct predictors of radiation-induced optic neuropathy after proton therapy: implications for personalized toxicity risk stratification"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9342-6464","authenticated-orcid":true,"given":"Thao-Nguyen","family":"Pham","sequence":"first","affiliation":[{"name":"Centre Fran\u00e7ois Baclesse"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7005-8692","authenticated-orcid":true,"given":"Ibrahim","family":"Chamseddine","sequence":"additional","affiliation":[{"name":"Harvard Medical School"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorothee","family":"Lebhertz","sequence":"additional","affiliation":[{"name":"Centre Fran\u00e7ois Baclesse"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Claude","family":"Quintyn","sequence":"additional","affiliation":[{"name":"University Hospital of Caen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6257-2413","authenticated-orcid":true,"given":"Harald","family":"Paganetti","sequence":"additional","affiliation":[{"name":"Harvard Medical School"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4755-496X","authenticated-orcid":true,"given":"Juliette","family":"Thariat","sequence":"additional","affiliation":[{"name":"Centre Fran\u00e7ois Baclesse"},{"name":"France; Normandie Universit\u00e9"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"pmbae752cbib1","doi-asserted-by":"publisher","first-page":"S28","DOI":"10.1016\/j.ijrobp.2009.07.1753","type":"journal-article","article-title":"Radiation dose\u2013volume effects of optic nerves and chiasm","volume":"76","author":"Mayo","year":"2010","journal-title":"Int. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-02-19","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-29","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}