{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:06:05Z","timestamp":1787317565301,"version":"build-2736575974"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum M\u00fcnster"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      This retrospective study aims to develop a deep learning-based approach to whole-body CT segmentation out of standard PSMA-PET-CT to assess body composition in metastatic castration resistant prostate cancer (mCRPC) patients prior to [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-PSMA radioligand therapy (RLT). Our goal is to go beyond standard PSMA-PET-based pretherapeutic assessment and identify additional body composition metrics out of the CT-component, with potential prognostic value.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We used a deep learning segmentation model to perform fully automated segmentation of different tissue compartments, including visceral- (VAT), subcutaneous- (SAT), intra\/intermuscular- adipose tissue (IMAT) from [\n                      <jats:sup>68<\/jats:sup>\n                      \u00a0Ga]Ga-PSMA-PET-CT scans of\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200986 prostate cancer patients before RLT. The proportions of different adipose tissue compartments to total adipose tissue (TAT) assessed on a 3D CT-volume of the abdomen or on a 2D single slice basis (centered at third lumbal vertebra (L3)) were compared for their prognostic value. First, univariate and multivariate Cox proportional hazards regression analyses were performed. Subsequently, the subjects were dichotomized at the median tissue composition, and these subgroups were evaluated by Kaplan\u2013Meier analysis with the log-rank test.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The automated segmentation model was useful for delineating different adipose tissue compartments and skeletal muscle across different patient anatomies. Analyses revealed significant correlations between lower SAT and higher IMAT ratios and poorer therapeutic outcomes in Cox regression analysis (SAT\/TAT:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.038; IMAT\/TAT:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) in the 3D model. In the single slice approach only IMAT\/SAT was significantly associated with survival in Cox regression analysis (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001; SAT\/TAT:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05). IMAT ratio remained an independent predictor of survival in multivariate analysis when including PSMA-PET and blood-based prognostic factors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this proof-of-principle study the implementation of a deep learning-based whole-body analysis provides a robust and detailed CT-based assessment of body composition in mCRPC patients undergoing RLT. Potential prognostic parameters have to be corroborated in larger prospective datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-025-07416-7","type":"journal-article","created":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T03:14:49Z","timestamp":1751080489000},"page":"330-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prognostic value of body composition out of PSMA-PET\/CT in prostate cancer patients undergoing PSMA-therapy"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3746-9393","authenticated-orcid":false,"given":"Wolfgang","family":"Roll","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"Plagwitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ventura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Masthoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clemens","family":"Backhaus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Varghese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kambiz","family":"Rahbar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Schindler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,28]]},"reference":[{"key":"7416_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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