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Routinely, only a small subset of index lesions out of multiple lesions is analyzed. However, this operator dependent selection may bias the results due to possible significant inter-metastatic heterogeneity of response to therapy. Automated, AI-based approaches for lesion tracking hold promise in enabling the analysis of many more lesions and thus providing a better assessment of tumor response. This work introduces a Siamese CNN approach for lesion tracking between PET\/CT scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Our approach is applied on the laborious task of tracking a high number of bone lesions in full-body baseline and follow-up [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga- or [\n                      <jats:sup>18<\/jats:sup>\n                      F]F-PSMA PET\/CT scans after two cycles of [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-PSMA therapy of metastatic castration resistant prostate cancer patients. Data preparation includes lesion segmentation and affine registration. Our algorithm extracts suitable lesion patches and forwards them into a Siamese CNN trained to classify the lesion patch pairs as corresponding or non-corresponding lesions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experiments have been performed with different input patch types and a Siamese network in 2D and 3D. The CNN model successfully learned to classify lesion assignments, reaching an accuracy of 83 % in its best configuration with an AUC = 0.91. For corresponding lesions the pipeline accomplished lesion tracking accuracy of even 89 %.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We proved that a CNN may facilitate the tracking of multiple lesions in PSMA PET\/CT scans. Future clinical studies are necessary if this improves the prediction of the outcome of therapies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-025-07426-5","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:44:39Z","timestamp":1751949879000},"page":"429-441","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["AI lesion tracking in PET\/CT imaging: a proposal for a Siamese-based CNN pipeline applied to PSMA PET\/CT scans"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4160-5763","authenticated-orcid":false,"given":"Stefan P.","family":"Hein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Schultheiss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Gafita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raphael","family":"Zaum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farid","family":"Yagubbayli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Tauber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabel","family":"Rauscher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Eiber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franz","family":"Pfeiffer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang A.","family":"Weber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"7426_CR1","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1016\/j.eururo.2016.06.021","volume":"70","author":"M Perera","year":"2016","unstructured":"Perera M, Papa N, Christidis D, Wetherell D, Hofman MS, Murphy DG, et al. 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