{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T08:55:36Z","timestamp":1790931336348,"version":"4.1.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of Western Australia","id":[{"id":"https:\/\/ror.org\/047272k79","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      This study aimed to validate an automated lesion-tracking algorithm for [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga-PSMA-11 PET\/CT against expert reference matches and to evaluate its integration into three PSMA-specific treatment response frameworks\u2014the Response Evaluation Criteria in PSMA PET\/CT (RECIP 1.0), PSMA PET Progression (PPP) criteria, and the Traffic Light Workflow (TLW)\u2014in men with biochemically recurrent (BCR) prostate cancer (PCa).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Paired baseline and follow-up [\n                      <jats:sup>68<\/jats:sup>\n                      \u00a0Ga]Ga-PSMA-11 PET\/CT scans from 132 BCR PCa patients were analysed using a registration-based pipeline that automatically assigned one-to-one lesion correspondences. Lesions were labelled as corresponding (CL), disappearing (DL) or new (NL). Algorithm-derived RECIP 1.0, PPP, and TLW categories were compared with expert reference labels and tested for overall survival (OS) association using univariable Cox regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1,066 lesions were identified (559 baseline, 507 follow-up), including 371 CLs, 188 DLs, and 136 NLs, yielding 695 matching decisions. The lesion matching algorithm achieved 95.1% matching accuracy. Lesion mismatches were associated with poorer registration quality. Algorithm-derived patient-level response classifications showed high concordance with ground-truth frameworks, with classification accuracy of 95.5% for RECIP 1.0, 96.2% for PPP, and 96.2% for TLW. All frameworks retained prognostic significance for OS, with progressive disease conferring hazard ratios (HR) of\u2014RECIP 1.0 HR\u2009=\u20092.57 (95% CI 1.33\u20134.98), PPP HR\u2009=\u20092.02 (95% CI 1.04\u20133.90), and TLW HR\u2009=\u20092.29 (95% CI 1.22\u20134.28).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The automated lesion-tracking algorithm reproduced expert matches with high accuracy and preserved the prognostic utility of RECIP 1.0, PPP, and TLW in a BCR cohort, supporting its use as a reproducible lesion-matching method for quantitative response assessment on PSMA PET\/CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration number<\/jats:title>\n                    <jats:p>ACTRN ACTRN12615000608561. Registered 11 June 2015. Retrospectively registered.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-026-08071-2","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:41:39Z","timestamp":1784173299000},"page":"7109-7120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated lesion tracking and treatment response classification on [68Ga]Ga-PSMA-11 PET\/CT across PPP, RECIP 1.0 and TLW frameworks"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3585-0897","authenticated-orcid":false,"given":"Theo","family":"Lucas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikaela","family":"Dell\u2019Oro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy S. 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