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The PSMA SUV (SUV\n                      <jats:sub>mean<\/jats:sub>\n                      , SUV\n                      <jats:sub>SD<\/jats:sub>\n                      , SUV\n                      <jats:sub>peak<\/jats:sub>\n                      ) and corresponding diffusion weighted imaging (DWI) parameters (ADC\n                      <jats:sub>mean<\/jats:sub>\n                      , ADC\n                      <jats:sub>kurtosis<\/jats:sub>\n                      , ADC\n                      <jats:sub>vol<\/jats:sub>\n                      ) were extracted from five hottest lesions (highest SUV\n                      <jats:sub>mean<\/jats:sub>\n                      ) on PSMA-PET\/CT. Lesion response was assessed using modified PERCIST and MET-RADS-P criteria. Multilevel logistic regression and area under receiver operating characteristic (AUROC) analyses identified predictive biomarkers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      65 bone lesions and 30 lymph nodes were analysed pre- and post-therapy. Forty-four (68%) bone and 16 (53%) lymph nodes lesions responded to treatment. SUV\n                      <jats:sub>mean<\/jats:sub>\n                      and SUV\n                      <jats:sub>peak<\/jats:sub>\n                      were almost identical (rank-correlation\u2009=\u20090.97) and had high predictive performance for response with AUROC of 0.74 (95% CI: 0.62\u20130.86) and 0.75 (95% CI: 0.61\u20130.88) respectively. Lesion volume showed good performance (AUROC\u2009=\u20090.69, 95% CI 0.57\u20130.82) and moderately correlated with SUV\n                      <jats:sub>mean<\/jats:sub>\n                      (rank-correlation\u2009=\u20090.43). Neither ADC\n                      <jats:sub>mean<\/jats:sub>\n                      (AUROC\u2009=\u20090.45,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.47) or ADC\n                      <jats:sub>kurtosis<\/jats:sub>\n                      (AUROC\u2009=\u20090.49,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.171) were predictive. On regression modelling, a 1-unit volume increase, raised response odds by 4.3 (95% CI 0.7\u201327) in bone lesions, and 6.2 (95% CI 0.6\u201367) in lymph nodes (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.06). A 1-unit SUV\n                      <jats:sub>mean<\/jats:sub>\n                      increase raised response odds by 1.5 (95% CI 1.1\u20132.0) in bone lesions and 1.2 (95% CI 1.0\u20131.4) in lymph nodes (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). No evidence suggested combining volume with SUVmean enhanced predictive performance over SUV\n                      <jats:sub>mean<\/jats:sub>\n                      alone (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.58).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Baseline PSMA SUV\n                      <jats:sub>mean<\/jats:sub>\n                      , SUV\n                      <jats:sub>peak<\/jats:sub>\n                      , and volume are promising predictive biomarkers for lesion-level response to [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]LuPSMA therapy. Combining functional and structural imaging biomarkers could improve treatment stratification and response assessment. Further validation in larger studies, alongside patient-level analysis, and expansion beyond the five lesions is needed to refine predictive models for clinical application.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40644-026-01066-0","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:31:49Z","timestamp":1782847909000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive imaging biomarkers on whole-body diffusion-weighted MRI (WB-DWMRI) and [68Ga]GaPSMA-PET\/CT for [177Lu]LuPSMA therapy in metastatic prostate cancer (mCRPC)"],"prefix":"10.1186","volume":"26","author":[{"given":"Minal","family":"Padden-Modi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Taprogge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Dutey-Magni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina G. 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