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Differentiating between amyloidosis subtypes is crucial due to distinct treatment strategies. The individual conventional diagnostic methods lack the accuracy needed for effective subtype identification. This study aimed to evaluate the efficacy of combining\n                      <jats:sup>11<\/jats:sup>\n                      C-PiB PET\/CT and\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-DPD scintigraphy in detecting CA and distinguishing between its main subtypes, light chain (AL) and transthyretin (ATTR) amyloidosis while assessing the association of imaging findings with patient prognosis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively evaluated the diagnostic efficacy of combining\n                      <jats:sup>11<\/jats:sup>\n                      C-PiB PET\/CT and\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-DPD scintigraphy in a cohort of 50 patients with clinical suspicion of CA. Semi-quantitative imaging markers were extracted from the images. Diagnostic performance was calculated against biopsy results or genetic testing. Both machine learning models and a rationale-based model were developed to detect CA and classify subtypes. Survival prediction over five years was assessed using a random survival forest model. Prognostic value was assessed using Kaplan-Meier estimators and Cox proportional hazards models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The combined imaging approach significantly improved diagnostic accuracy, with\n                      <jats:sup>11<\/jats:sup>\n                      C-PiB PET and\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-DPD scintigraphy showing complementary strengths in detecting AL and ATTR, respectively. The machine learning model achieved an AUC of 0.94 (95% CI 0.93\u20130.95) for CA subtype differentiation, while the rationale-based model demonstrated strong diagnostic ability with AUCs of 0.95 (95% CI 0.88-1.00) for ATTR and 0.88 (95% CI 0.770\u20130.961) for AL. Survival prediction models identified key prognostic markers, with significant stratification of overall mortality based on predicted survival (\n                      <jats:italic>p<\/jats:italic>\n                      value\u2009=\u20090.006; adj HR 2.43 [95% CI 1.03\u20135.71]).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The integration of\n                      <jats:sup>11<\/jats:sup>\n                      C-PiB PET\/CT and\n                      <jats:sup>99m<\/jats:sup>\n                      Tc-DPD scintigraphy, supported by both machine learning and rationale-based models, enhances the diagnostic accuracy and prognostic assessment of cardiac amyloidosis, with significant implications for clinical practice.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical abstracts<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-025-07157-7","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:24:52Z","timestamp":1740702292000},"page":"3321-3332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Enhanced diagnostic and prognostic assessment of cardiac amyloidosis using combined 11C-PiB PET\/CT and 99mTc-DPD scintigraphy"],"prefix":"10.1007","volume":"52","author":[{"given":"Zhihui","family":"Hong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clemens P.","family":"Spielvogel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raffaella","family":"Calabretta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zewen","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kilian","family":"Kluge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Haberl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Nitsche","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Gr\u00fcnert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus","family":"Hacker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0016-6233","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"7157_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/13506129.2022.2093635","volume":"30","author":"AD Wechalekar","year":"2023","unstructured":"Wechalekar AD, Cibeira MT, Gibbs SD, Jaccard A, Kumar S, Merlini G, et al. 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