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In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Twenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with\n                      <jats:sup>177<\/jats:sup>\n                      Lu-PSMA I&amp;T RLT were included retrospectively. They had available pre-therapy\n                      <jats:sup>68<\/jats:sup>\n                      \u00a0Ga-PSMA-HEBD-CC PET\/CT and at least 3 planar and 1 SPECT\/CT imaging for dosimetry. Overall, 43 cycles of\n                      <jats:sup>177<\/jats:sup>\n                      Lu-PSMA I&amp;T RLT were applied. Organ-based standard uptake values (SUVs) were obtained from pre-therapy PET\/CT scans. Patient dosimetry was calculated for the kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the planar and SPECT\/CT images. Machine learning methods were explored for dose prediction from organ SUVs and laboratory measurements. The uncertainty of these dose predictions was compared with the population-based dosimetry estimates. Mean absolute percentage error (MAPE) was used to assess the prediction uncertainty of estimated dosimetry.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      An optimal machine learning method achieved a dosimetry prediction MAPE of 15.8\u2009\u00b1\u200913.2% for the kidney, 29.6%\u2009\u00b1\u200913.7% for the liver, 23.8%\u2009\u00b1\u200913.1% for the salivary glands, and 32.1\u2009\u00b1\u200931.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01), 25.5\u2009\u00b1\u200917.3% for the kidney, 139.1%\u2009\u00b1\u2009111.5% for the liver, 67.0\u2009\u00b1\u200958.3% for the salivary glands, and 54.1\u2009\u00b1\u2009215.3% for the spleen.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-022-05883-w","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T02:05:24Z","timestamp":1656554724000},"page":"4064-4072","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with 177Lu-PSMA I&amp;T therapy"],"prefix":"10.1007","volume":"49","author":[{"given":"Song","family":"Xue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Gafita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giles","family":"Tetteh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjoern H.","family":"Menze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibylle","family":"Ziegler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Weber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Afshar-Oromieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Rominger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Eiber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8714-3084","authenticated-orcid":false,"given":"Kuangyu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"5883_CR1","doi-asserted-by":"publisher","first-page":"103108","DOI":"10.18632\/oncotarget.21600","volume":"8","author":"H Ahmadzadehfar","year":"2017","unstructured":"Ahmadzadehfar H, Schlolaut S, Fimmers R, Yordanova A, Hirzebruch S, Schlenkhoff C, et al. 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