{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:17:44Z","timestamp":1773893864005,"version":"3.50.1"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Recurrence following prostate cancer (PCa) radiation therapy (RT) remains a persistent challenge. Although dose escalation can improve tumor control, it often results in increased toxicity. With an understanding of tumor heterogeneity, identification of radioresistant tumor subvolumes at risk of low tumor control probability (TCP) could provide an opportunity for personalized dose prescription to reduce risk of treatment failure without the increased risk of toxicity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The aim of this study was to evaluate the efficacy of an atlas\u2010based tumor control probability model in predicting prostate cancer recurrence by retrospectively integrating patient\u2010specific primary radiotherapy and histopathology\u2010informed data. A segment\u2010wise adjustment to TCP model parameters was investigated for enhancing recurrence prediction in a patient cohort with biopsy\u2010confirmed local recurrence following definitive RT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Nine patients with biopsy proven local recurrence were selected from an ethics\u2010approved study (NCT03073278) based on the availability of histopathology reports, dose\u2010fractionation schedules and treatment planning data from their primary RT. Two previously\u2010reported population\u2010based biological atlases, one comprising a cell density data (CD\u2010atlas) and the other tumor probability data (TP\u2010atlas), were deformably registered to the prostate contour of each patient. Histopathology reports were retrieved for each patient, and the registered prostate atlases were anatomically segmented based on individual histopathology findings. Radiosensitivity parameters were derived from a separate patient cohort's histology dataset using a numerical optimization method, generating a single PCa grade\u2010independent \u03b1\/\u03b2 ratio, four Gleason Pattern (GP)\u2010dependent \u03b1 parameters, and nine Gleason Score (GS)\u2010dependent \u03b1\/\u03b2 ratios. Three parameter adjustment approaches\u2014cell density alone, cell density with GP\u2010dependent \u03b1, and cell density with GS\u2010dependent \u03b1\/\u03b2, were evaluated and compared to a baseline model without adjustments. Changes in overall TCP values resulting from the adjustments were analyzed, and recurrent gross tumor volume (GTV) contours were overlaid on the TCP maps to evaluate their alignment with regions of lower TCP, assessing the model's ability to enhance recurrence prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The approach combining segment\u2010wise cell density and GS\u2010dependent \u03b1\/\u03b2 adjustments showed superior predictive capability, with all nine patients (100%) exhibiting a significant (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.004) reduction in overall TCP values and seven patients (78%) showing alignment of lower TCP regions with relapsed tumor sites. This was further supported by voxel\u2010level histogram analysis and statistically significant volume\u2010weighted TCP differences between GTV and nonGTV regions (Wilcoxon signed\u2010rank test,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.003). In contrast, GP\u2010dependent \u03b1 adjustments alongside cell density failed to predict recurrence, while cell density adjustments alone yielded moderate performance. Additionally, the generated single \u03b1\/\u03b2 ratio and GS\u2010dependent \u03b1\/\u03b2 ratios were consistent with the lower \u03b1\/\u03b2 ratios typically associated with PCa.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The atlas\u2010based TCP model, enhanced with patient\u2010specific histopathology report data, demonstrated promising capabilities in predicting PCa recurrence. This approach has the potential to support personalized treatment planning by enabling optimization of the distribution of a specific integral dose to minimize recurrence risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70282","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T10:15:42Z","timestamp":1768385742000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting prostate cancer recurrence using an atlas\u2010based tumor control probability model"],"prefix":"10.1002","volume":"53","author":[{"given":"Kazi Ridita","family":"Mahtaba","sequence":"first","affiliation":[{"name":"Institute of Medical Physics, School of Physics The University of Sydney Camperdown New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin A.","family":"Ebert","sequence":"additional","affiliation":[{"name":"School of Physics Mathematics and Computing, University of Western Australia  Crawley Western Australia Australia"},{"name":"Department of Radiation Oncology Sir Charles Gairdner Hospital  Perth Western Australia Australia"},{"name":"Centre for Advanced Technologies in Cancer Research Centre for Advanced Technologies in Cancer Research  Perth Western Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy","family":"Booth","sequence":"additional","affiliation":[{"name":"Institute of Medical Physics, School of Physics The University of Sydney Camperdown New South Wales Australia"},{"name":"Northern Sydney Cancer Centre Royal North Shore Hospital  St Leonards New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leyla","family":"Moghaddasi","sequence":"additional","affiliation":[{"name":"Northern Sydney Cancer Centre Royal North Shore Hospital  St Leonards New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Finnegan","sequence":"additional","affiliation":[{"name":"Northern Sydney Cancer Centre Royal North Shore Hospital  St Leonards New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Physics Mathematics and Computing, University of Western Australia  Crawley Western Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Burhan","family":"Javed","sequence":"additional","affiliation":[{"name":"Genesis Care Rockhampton Hospital  Rockhampton Queensland Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Hruby","sequence":"additional","affiliation":[{"name":"Northern Sydney Cancer Centre Royal North Shore Hospital  St Leonards New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Annette","family":"Haworth","sequence":"additional","affiliation":[{"name":"Institute of Medical Physics, School of Physics The University of Sydney Camperdown New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.07.014"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.11.023"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110535"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.12.022"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.20.02873"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"ReynoldsHM WilliamsS ZhangAM et\u00a0al.Cell density in prostate histopathology images as a measure of tumor distribution. 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