{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:16:24Z","timestamp":1780352184068,"version":"3.54.1"},"reference-count":32,"publisher":"IOP Publishing","issue":"21","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Pudong New area Health Commission foundation","award":["PW2021A-41"],"award-info":[{"award-number":["PW2021A-41"]}]},{"DOI":"10.13039\/501100007279","name":"Shanghai Municipal Health Bureau","doi-asserted-by":"crossref","award":["202140028"],"award-info":[{"award-number":["202140028"]}],"id":[{"id":"10.13039\/501100007279","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,11,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> To develop a machine learning-enhanced normal tissue complication probability (NTCP) model for predicting late sciatic nerve toxicity (LSNT) in sacrococcygeal chordoma (SC) and locally recurrent rectal cancer (LRRC) patients undergoing carbon-ion radiotherapy (CIRT). <jats:italic>Methods.<\/jats:italic> This dual-modeling study analyzed 106 CIRT-treated SC\/LRRC patients. Notice that the unit of Gy is the relative biological effectiveness -weighted dose with local effect model version I in this study for the prescription of our CIRT treatments. The hybrid framework integrated the Lyman\u2013Kutcher\u2013Burman model with generalized machine learning. Radiation dosimetry (the equivalent uniform dose (EUD), the uniform dose over the sciatic nerve for a 50% complication probability [TD<jats:sub>50<\/jats:sub>], the parameter for the volume effect of the organ [<jats:italic>n<\/jats:italic>], the slop steepness of the dose response curve [<jats:italic>m<\/jats:italic>]) and biological parameters were analyzed through univariate\/multivariate regression. Model performance was validated using the area under the receiver operating characteristic area under the curve, sensitivity, and specificity metrics. <jats:italic>Results.<\/jats:italic> 16.9% (18\/106) over all patients developed grade \u2a7e1 LSNT with no grade \u2a7e4 toxicity. Stratified outcome analysis across various subgroups revealed significant variations, especially that pathological subtype of re-irradiated rectal cancer exhibited manifested 15.6% G2 toxicity. We first successfully obtained coefficients for the NTCP models with univariate analysis by utilizing the correlation between significant variations of stratified outcome and TD<jats:sub>50<\/jats:sub>. We then establish an NTCP model by machine learning-enhanced multivariate analysis that allow identifying the critical dose\u2013volume thresholds of <jats:italic>V<\/jats:italic>\n                  <jats:sub>62<\/jats:sub>, <jats:italic>V<\/jats:italic>\n                  <jats:sub>64<\/jats:sub>, and <jats:italic>D<\/jats:italic>\n                  <jats:sub>3cc<\/jats:sub> to correlate dose-dependent progression of G1, G2, ad G3 toxicities, respectively. <jats:italic>Significance.<\/jats:italic> EUD &gt; 61.1 Gy significantly elevates G1 LSNT risk. Our center recommends: <jats:italic>V<\/jats:italic>\n                  <jats:sub>62<\/jats:sub> \u2a7d 6.2%, <jats:italic>V<\/jats:italic>\n                  <jats:sub>64<\/jats:sub> \u2a7d 4.69%, <jats:italic>D<\/jats:italic>\n                  <jats:sub>3cc<\/jats:sub> \u2a7d 32.3 Gy to balance tumor control and neuroprotection in CIRT planning. The n\/m parameters provide critical insights into individual radiation sensitivity gradients across toxicity grades with machine learning-enhanced NTCP modeling for LSNT prediction in CIRT.<\/jats:p>","DOI":"10.1088\/1361-6560\/ae110d","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:52:47Z","timestamp":1759963967000},"page":"215002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-enhanced normal tissue complication probability modeling for late sciatic nerve toxicity prediction in carbon-ion radiotherapy: model development and clinical validation"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8110-8835","authenticated-orcid":true,"given":"Yongqiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3988-3731","authenticated-orcid":true,"given":"Ruoxi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2365-9495","authenticated-orcid":true,"given":"Wenchien","family":"Hsi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"pmbae110dbib1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.radonc.2005.03.021","type":"journal-article","article-title":"Acute esophageal toxicity in non-small cell lung cancer patients after high dose conformal radiotherapy","volume":"75","author":"Belderbos","year":"2005","journal-title":"Radiother. 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