{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T07:33:15Z","timestamp":1793345595795,"version":"4.5.0"},"reference-count":27,"publisher":"IOP Publishing","issue":"21","license":[{"start":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:00:00Z","timestamp":1793318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:00:00Z","timestamp":1793318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:00:00Z","timestamp":1793318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:00:00Z","timestamp":1793318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,11,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    To develop and evaluate a physics-informed machine-learning framework for rapid organ-dose estimation in fluoroscopically guided interventional procedures without requiring patient-specific imaging.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We used 385 adult radiation dose structured reports (RDSRs) from three procedure types\u2014bilateral carotid angiography, percutaneous biliary drainage, and liver chemoembolization\u2014comprising more than 50 000 irradiation events. A body-size-matched computational phantom was selected using patient sex, height, and weight. RDSR-derived beam parameters and engineered descriptors of beam-organ geometry and attenuation were used to train organ-specific XGBoost models for localized organs and a multi-output multilayer perceptron model for four distributed tissues. Monte Carlo (MC)-derived dose coefficients from NCIRF3 were used as target values.\n                    <jats:italic>Main results.<\/jats:italic>\n                    In the held-out test set of 12 683 irradiation events, 49.4% and 84.8% of XGBoost-based localized-organ estimates were within \u00b15% and \u00b120% of the MC-derived values, respectively. The corresponding proportions were 88.2% and 98.0% for the distributed-tissue estimates and 87.6% and 98.3% for detriment-weighted dose. The median relative prediction error across organs and tissues was 0.37% (interquartile range, \u22123.68% to 4.99%). A reliability score was developed to support uncertainty-aware interpretation. The framework required approximately 0.3 s per irradiation event, corresponding to an approximately 290-fold acceleration relative to conventional MC simulation under the benchmark conditions.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The proposed image-free framework enables rapid organ-dose estimation when patient imaging is unavailable and may support large retrospective epidemiological studies and uncertainty-aware dose monitoring. Because development and validation were limited to three procedure types and relied on reconstructed phantom-beam geometry, external validation is required before application to other procedures or high-precision individual clinical decisions.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeb597","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T22:53:49Z","timestamp":1792191229000},"page":"215005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-informed machine learning-based dose calculation for patients undergoing fluoroscopically guided interventional procedures"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7635-1611","authenticated-orcid":true,"given":"Haegin","family":"Han","sequence":"first","affiliation":[{"name":"National Cancer Institute, National Institutes of Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9628-3791","authenticated-orcid":true,"given":"Keith T","family":"Griffin","sequence":"additional","affiliation":[{"name":"National Cancer Institute, National Institutes of Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6470-3544","authenticated-orcid":false,"given":"Monjoy","family":"Saha","sequence":"additional","affiliation":[{"name":"National Cancer Institute, National Institutes of Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A Kyle","family":"Jones","sequence":"additional","affiliation":[{"name":"The University of Texas MD Anderson Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2217-2933","authenticated-orcid":true,"given":"Steven D","family":"Mann","sequence":"additional","affiliation":[{"name":"Duke University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3256-5335","authenticated-orcid":false,"given":"Kevin A","family":"Wunderle","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4599-5086","authenticated-orcid":false,"given":"Donald L","family":"Miller","sequence":"additional","affiliation":[{"name":"Center for Devices and Radiological Health, U.S. Food and Drug Administration"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9092-3041","authenticated-orcid":false,"given":"Nina","family":"Petoussi-Henss","sequence":"additional","affiliation":[{"name":"External and Internal Dosimetry, Biokinetics, German Federal Office for Radiation Protection"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2786-4506","authenticated-orcid":true,"given":"David","family":"Sutton","sequence":"additional","affiliation":[{"name":"Ninewells Hospital and Medical School"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-6622","authenticated-orcid":false,"given":"Choonsik","family":"Lee","sequence":"additional","affiliation":[{"name":"National Cancer Institute, National Institutes of Health"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,30]]},"reference":[{"key":"pmbaeb597bib1","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.nima.2016.06.125","type":"journal-article","article-title":"Recent developments in Geant4","volume":"835","author":"Allison","year":"2016","journal-title":"Nucl. 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