{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:47:52Z","timestamp":1792133272855,"version":"4.4.0"},"reference-count":37,"publisher":"IOP Publishing","issue":"20","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,10,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Selective internal radiation therapy delivers Y\u201090 microspheres through the hepatic arterial system to achieve tumoricidal absorbed dose while limiting normal\u2010tissue irradiation. Current pre\u2010treatment dosimetry requires angiography and 99 mTc\u2010MAA imaging, resulting in an invasive and resource\u2010intensive workflow. This study evaluates the feasibility of estimating post\u2010therapy Y\u201090 dose distribution directly from diagnostic multiphasic CT using an automated deep\u2010learning pipeline, without requiring angiography or surrogate\u2010particle imaging.\n                    <jats:italic>Approach.<\/jats:italic>\n                    In 152 treatments from the multicenter PROACTIF registry, arterial\u2010 and portal\u2010phase CT, together with clinically documented perfusion territories and injected activities, were processed through automated segmentation, rigid registration, differential CT generation, and a generative model synthesizing a PET\u2010like Y\u201090 activity map. Absorbed\u2010dose maps were computed using the Local Dose Deposition method, and predictive accuracy was assessed using tumor dose\u2013volume metrics and voxelwise spatial agreement.\n                    <jats:italic>Main results.<\/jats:italic>\n                    For the best\u2010performing configuration, the median tumor D70 error was 23 Gy, with substantial inter\u2010patient variability reflecting the challenges of CT\u2010only dose prediction. High\u2010dose coverage (V200) showed a similar pattern. In contrast, spatial agreement was consistently strong, with perfused\u2010volume gamma passing rates around 93% and tumor\u2010level rates around 85%, indicating that the macro\u2010scale distribution of microspheres can be approximated from diagnostic CT. The full pipeline was fully automated and produced predictive dosimetry in under\n                    <jats:italic>30 S. Significance.<\/jats:italic>\n                    Pre\u2010angiography CT contains exploitable information about arterial enhancement and perfusion patterns that enables feasible CT\u2010only prediction of Y\u201090 spatial distribution. While tumor\u2010level absorbed\u2010dose metrics remain variable, the strong spatial agreement suggests potential utility as an early planning tool to support patient selection or guide catheterization strategy prior to angiography.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeabea","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T22:53:46Z","timestamp":1790204026000},"page":"205011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards artificial intelligence based preoperative dosimetry for liver\n                    <sup>90<\/sup>\n                    Y selective internal radiation therapy"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1263-8220","authenticated-orcid":true,"given":"E","family":"Morel\u2013Corlu","sequence":"first","affiliation":[{"name":"LTSI\u2014UMR_S"},{"name":"Therenva"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6754-9298","authenticated-orcid":false,"given":"F","family":"Lalys","sequence":"additional","affiliation":[{"name":"Therenva"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Petit","sequence":"additional","affiliation":[{"name":"Therenva"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3451-8193","authenticated-orcid":false,"given":"P","family":"Haigron","sequence":"additional","affiliation":[{"name":"LTSI\u2014UMR_S"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S","family":"Esneault","sequence":"additional","affiliation":[{"name":"Therenva"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4765-6683","authenticated-orcid":false,"given":"Y","family":"Rolland","sequence":"additional","affiliation":[{"name":"LTSI\u2014UMR_S"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5728-1403","authenticated-orcid":false,"given":"M","family":"Garreau","sequence":"additional","affiliation":[{"name":"LTSI\u2014UMR_S"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"pmbaeabeabib1","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.ejrad.2018.01.025","type":"journal-article","article-title":"Diagnosis and staging of hepatocellular carcinoma (HCC): current guidelines","volume":"101","author":"Ayuso","year":"2018","journal-title":"Eur. 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Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2026-06-04","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-23","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-16","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}