{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T04:30:18Z","timestamp":1793939418215,"version":"4.8.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008977","name":"Universit\u00e4t Ulm","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008977","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EJNMMI Phys"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>\n                      Predicting the time-activity curve (TAC) of [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-DOTA-TATE for organs at risk and neuroendocrine tumours (NETs) is an essential element in the calculation of the absorbed dose (AD) and a critical step for individualising peptide receptor radionuclide therapy (PRRT) treatment planning. This study aims to predict the TAC of [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-DOTA-TATE using a single quantitative image of [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga-DOTA-TATE and population data with a physiologically based pharmacokinetic (PBPK) model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A PBPK model was developed for [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga-DOTA-TATE and [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-DOTA-TATE, including organs and NETs. To generate reference TACs, general physiological parameters were taken from the literature, while individual model parameters were estimated using pre-therapy (PET\/CT) and post-therapy (planar and SPECT\/CT) image-based organ activity measurements from patients with NETs. Different error models were evaluated to determine the best one. To predict the TAC of [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-DOTA-TATE from a single [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga-DOTA-TATE PET\/CT, individual model parameters were estimated using only [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga-DOTA-TATE organ and tumour activity measurements. Finally, the predicted [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-DOTA-TATE TACs for modelled organs and NETs were compared to the reference.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The best error model was the proportional data-based error model, where the proportionality parameter\n                      <jats:italic>b<\/jats:italic>\n                      differs between diagnostic and therapeutic data, and between tumours and organs: b\n                      <jats:sub>T, Organ<\/jats:sub>\n                      , b\n                      <jats:sub>T, Tumour<\/jats:sub>\n                      , and b\n                      <jats:sub>D, Organ<\/jats:sub>\n                      , b\n                      <jats:sub>D, Tumour<\/jats:sub>\n                      . The medians for b\n                      <jats:sub>T, Organ<\/jats:sub>\n                      , b\n                      <jats:sub>T, Tumour<\/jats:sub>\n                      and b\n                      <jats:sub>D, Organ<\/jats:sub>\n                      , b\n                      <jats:sub>D, Tumour<\/jats:sub>\n                      were determined to be 0.16, 0.39, 0.35, and 0.27, respectively. For the prediction, b\n                      <jats:sub>D, Organ<\/jats:sub>\n                      and b\n                      <jats:sub>D, Tumour<\/jats:sub>\n                      were used as patient-specific proportional errors. The relative prediction error (RPE) was calculated for the predicted time-integrated activity (TIA). The mean and standard deviation for the RPEs were found to be (\u2212 5\u2009\u00b1\u200951)%, (\u2212 4\u2009\u00b1\u200922)%, (\u2212 13\u2009\u00b1\u200940)%, and (\u2212 10\u2009\u00b1\u200921)% for tumours, kidneys, liver, and spleen, respectively. The mean absolute percentage errors (MAPEs) were 43%, 18%, 31% and 17% for tumours, kidney, liver, and spleen, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The integration of the PBPK model with a data-based proportional error model represents a significant improvement in predicting TACs for estimating tumour and organ ADs following [\n                      <jats:sup>177<\/jats:sup>\n                      Lu]Lu-DOTA-TATE therapy, using single-time-point PET\/CT imaging with [\n                      <jats:sup>68<\/jats:sup>\n                      Ga]Ga-DOTA-TATE. These results emphasise the importance of error model analysis in PBPK modelling.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40658-025-00826-4","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T03:15:27Z","timestamp":1768533327000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["[177Lu]Lu-DOTA-TATE tumour and organ time-activity curves: prediction from a single-time-point [68Ga]Ga-DOTA-TATE PET\/CT measurement"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5989-5609","authenticated-orcid":false,"given":"Valentina","family":"Vasi\u0107","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Gustafsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Yousefzadeh-Nowshahr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ambros J.","family":"Beer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarina","family":"Sj\u00f6green Gleisner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerhard","family":"Glatting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"issue":"6","key":"826_CR1","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1080\/02841860701441848","volume":"46","author":"M Van Essen","year":"2007","unstructured":"Van Essen M, Krenning EP, De Jong M, Valkema R, Kwekkeboom DJ. Peptide receptor radionuclide therapy with radiolabelled somatostatin analogues in patients with somatostatin receptor positive tumours. Acta Oncol. 2007;46(6):723\u201334.","journal-title":"Acta Oncol"},{"key":"826_CR2","unstructured":"Applications AA. LUTATHERA Prescribing Information [package insert]. 2018."},{"issue":"8","key":"826_CR3","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1007\/s00259-012-2124-x","volume":"39","author":"H H\u00e4nscheid","year":"2012","unstructured":"H\u00e4nscheid H, Sweeney RA, Flentje M, Buck AK, Lohr M, Samnick S, et al. PET SUV correlates with radionuclide uptake in peptide receptor therapy in meningioma. Eur J Nucl Med Mol Imaging. 2012;39(8):1284\u20138.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"6","key":"826_CR4","doi-asserted-by":"publisher","first-page":"e141","DOI":"10.1097\/RLU.0b013e31823926e5","volume":"37","author":"S Ezziddin","year":"2012","unstructured":"Ezziddin S, Lohmar J, Yong-Hing CJ, Sabet A, Ahmadzadehfar H, Kukuk G, et al. Does the pretherapeutic tumor SUV in 68Ga DOTATOC PET predict the absorbed dose of 177Lu octreotate? Clin Nucl Med. 2012;37(6):e141\u20137.","journal-title":"Clin Nucl Med"},{"issue":"10","key":"826_CR5","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.1007\/s00259-023-06252-x","volume":"50","author":"A Akhavanallaf","year":"2023","unstructured":"Akhavanallaf A, Peterson AB, Fitzpatrick K, Roseland M, Wong KK, El-Naqa I, et al. The predictive value of pretherapy [68Ga]Ga-DOTA-TATE PET and biomarkers in [177Lu]Lu-PRRT tumor dosimetry. Eur J Nucl Med Mol Imaging. 2023;50(10):2984\u201396.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"5","key":"826_CR6","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s00259-015-3248-6","volume":"43","author":"D Hardiansyah","year":"2016","unstructured":"Hardiansyah D, Maass C, Attarwala A, M\u00fcller B, Kletting P, Mottaghy F, et al. The role of patient-based treatment planning in peptide receptor radionuclide therapy. Eur J Nucl Med Mol Imaging. 2016;43(5):871\u201380.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"9","key":"826_CR7","doi-asserted-by":"publisher","first-page":"5145","DOI":"10.1118\/1.4961012","volume":"43","author":"D Hardiansyah","year":"2016","unstructured":"Hardiansyah D, Guo W, Kletting P, Mottaghy FM, Glatting G. Time-integrated activity coefficient Estimation for radionuclide therapy using PET and a Pharmacokinetic model: a simulation study on the effect of sampling schedule and noise. Med Phys. 2016;43(9):5145\u201354.","journal-title":"Med Phys"},{"issue":"Supplement C","key":"826_CR8","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.ejmp.2017.06.024","volume":"42","author":"D Hardiansyah","year":"2017","unstructured":"Hardiansyah D, Attarwala AA, Kletting P, Mottaghy FM, Glatting G. Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a Pharmacokinetic model. Phys Med. 2017;42(Supplement C):298\u2013304.","journal-title":"Phys Med"},{"issue":"1","key":"826_CR9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.2967\/jnumed.118.210377","volume":"60","author":"P Kletting","year":"2019","unstructured":"Kletting P, Thieme A, Eberhardt N, Rinscheid A, D`Alessandria C, Allmann J, et al. Modeling and predicting tumor response in radioligand therapy. J Nucl Med. 2019;60(1):65\u201370.","journal-title":"J Nucl Med"},{"issue":"1","key":"826_CR10","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13550-022-00947-2","volume":"12","author":"A Stenvall","year":"2022","unstructured":"Stenvall A, Gustafsson J, Larsson E, Roth D, Sundl\u00f6v A, J\u00f6nsson L, et al. Relationships between uptake of [68Ga]Ga-DOTA-TATE and absorbed dose in [177Lu]Lu-DOTA-TATE therapy. EJNMMI Res. 2022;12(1):75.","journal-title":"EJNMMI Res"},{"issue":"13","key":"826_CR11","doi-asserted-by":"publisher","first-page":"3047","DOI":"10.1007\/s00259-020-04832-9","volume":"47","author":"S Krebs","year":"2020","unstructured":"Krebs S, O\u2019Donoghue JA, Biegel E, Beattie BJ, Reidy D, Lyashchenko SK, et al. Comparison of 68Ga-DOTA-JR11 PET\/CT with dosimetric 177Lu-satoreotide tetraxetan (177Lu-DOTA-JR11) SPECT\/CT in patients with metastatic neuroendocrine tumors undergoing peptide receptor radionuclide therapy. Eur J Nucl Med Mol Imaging. 2020;47(13):3047\u201357.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"826_CR12","doi-asserted-by":"crossref","unstructured":"Bruvoll R, Blakkisrud J, Mikalsen LT, Connelly J, Stokke C. Correlations between [(68)Ga]Ga-DOTA-TOC uptake and absorbed dose from [(177)Lu]Lu-DOTA-TATE. Cancers (Basel). 2023;15(4).","DOI":"10.3390\/cancers15041134"},{"issue":"7","key":"826_CR13","doi-asserted-by":"publisher","first-page":"e17852","DOI":"10.1002\/mp.17852","volume":"52","author":"H Yang","year":"2025","unstructured":"Yang H, Qi M, Chen Z, Liu F, Xu J, Xu X, et al. Predicting [177Lu]Lu-DOTA-TATE dosimetry by using pre-therapy [68Ga]Ga-DOTA-TATE PET\/CT and biomarkers in patient with neuroendocrine tumors. Med Phys. 2025;52(7):e17852.","journal-title":"Med Phys"},{"issue":"4","key":"826_CR14","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.zemedi.2013.03.005","volume":"23","author":"G Glatting","year":"2013","unstructured":"Glatting G, Bardi\u00e8s M, Lassmann M. Treatment planning in molecular radiotherapy. Z Med Phys. 2013;23(4):262\u20139.","journal-title":"Z Med Phys"},{"issue":"2","key":"826_CR15","first-page":"S37","volume":"40","author":"JA Siegel","year":"1999","unstructured":"Siegel JA, Thomas SR, Stubbs JB, Stabin MG, Hays MT, Koral KF, et al. MIRD pamphlet 16: techniques for quantitative radiopharmaceutical biodistribution data acquisition and analysis for use in human radiation dose estimates. J Nucl Med. 1999;40(2):S37\u201361.","journal-title":"J Nucl Med"},{"issue":"7","key":"826_CR16","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.1002\/mp.12984","volume":"45","author":"A Zvereva","year":"2018","unstructured":"Zvereva A, Kamp F, Schlattl H, Zankl M, Parodi K. Impact of interpatient variability on organ dose estimates according to MIRD schema: uncertainty and variance-based sensitivity analysis. Med Phys. 2018;45(7):3391\u2013403.","journal-title":"Med Phys"},{"key":"826_CR17","doi-asserted-by":"crossref","unstructured":"Hardiansyah D, Patrianesha BB, Shi K, Saboury B, Rahmim A, Glatting G. An overview of physiologically based pharmacokinetic and population pharmacokinetic models: applications to radiopharmaceutical therapies for analysis and personalization. PET Clinics; 2025.","DOI":"10.1016\/j.cpet.2025.09.004"},{"issue":"10","key":"826_CR18","doi-asserted-by":"publisher","first-page":"102504","DOI":"10.1118\/1.4820367","volume":"40","author":"P Kletting","year":"2013","unstructured":"Kletting P, Schimmel S, Kestler HA, H\u00e4nscheid H, Luster M, Fern\u00e1ndez M, et al. Molecular radiotherapy: the NUKFIT software for calculating the time-integrated activity coefficient. Med Phys. 2013;40(10):102504.","journal-title":"Med Phys"},{"key":"826_CR19","doi-asserted-by":"publisher","first-page":"103192","DOI":"10.1016\/j.ejmp.2023.103192","volume":"117","author":"OV Ivashchenko","year":"2024","unstructured":"Ivashchenko OV, O\u2019Doherty J, Hardiansyah D, Cremonesi M, Tran-Gia J, Hippel\u00e4inen E, et al. Time-activity data fitting in molecular radiotherapy: methodology and pitfalls. Phys Med. 2024;117:103192.","journal-title":"Phys Med"},{"issue":"8","key":"826_CR20","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.1002\/mp.13049","volume":"45","author":"LD Jim\u00e9nez-Franco","year":"2018","unstructured":"Jim\u00e9nez-Franco LD, Kletting P, Beer AJ, Glatting G. Treatment planning algorithm for peptide receptor radionuclide therapy considering multiple tumor lesions and organs at risk. Med Phys. 2018;45(8):3516\u201323.","journal-title":"Med Phys"},{"issue":"1","key":"826_CR21","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s40658-023-00565-4","volume":"10","author":"H Siebinga","year":"2023","unstructured":"Siebinga H, de Wit-van der Veen BJ, Beijnen JH, Stokkel MPM, Dorlo TPC, Huitema ADR, et al. Predicting [177Lu]Lu-HA-DOTATATE kidney and tumor accumulation based on [68Ga]Ga-HA-DOTATATE diagnostic imaging using semi-physiological population pharmacokinetic modeling. EJNMMI Phys. 2023;10(1):48.","journal-title":"EJNMMI Phys"},{"key":"826_CR22","doi-asserted-by":"crossref","unstructured":"Fani M, Mansi R. Radiolabeled somatostatin Analogs-A continuously evolving class of radiopharmaceuticals. 2022;14(5).","DOI":"10.3390\/cancers14051172"},{"issue":"6","key":"826_CR23","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1007\/s00259-022-05727-7","volume":"49","author":"K Sj\u00f6green Gleisner","year":"2022","unstructured":"Sj\u00f6green Gleisner K, Chouin N, Gabina PM, Cicone F, Gnesin S, Stokke C, et al. EANM dosimetry committee recommendations for dosimetry of 177Lu-labelled somatostatin-receptor- and PSMA-targeting ligands. Eur J Nucl Med Mol Imag. 2022;49(6):1778\u2013809.","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"1","key":"826_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/14736691211060117","volume":"21","author":"G Sgouros","year":"2021","unstructured":"Sgouros G, Bolch WE, Chiti A, Dewaraja YK, Emfietzoglou D, Hobbs RF, et al. ICRU REPORT 96, dosimetry-guided radiopharmaceutical therapy. J ICRU. 2021;21(1):1\u2013212.","journal-title":"J ICRU"},{"issue":"11","key":"826_CR25","doi-asserted-by":"publisher","first-page":"3830","DOI":"10.1007\/s00259-022-05786-w","volume":"49","author":"A Sundl\u00f6v","year":"2022","unstructured":"Sundl\u00f6v A, Gleisner KS, Tennvall J, Ljungberg M, Warfvinge CF, Holgersson K, et al. Phase II trial demonstrates the efficacy and safety of individualized, dosimetry-based 177Lu-DOTATATE treatment of NET patients. Eur J Nucl Med Mol Imag. 2022;49(11):3830\u201340.","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"4","key":"826_CR26","doi-asserted-by":"publisher","first-page":"503","DOI":"10.2967\/jnumed.115.164699","volume":"57","author":"P Kletting","year":"2016","unstructured":"Kletting P, Kull T, Maa\u00df C, Malik N, Luster M, Beer AJ, et al. Optimized peptide amount and activity for 90Y-Labeled DOTATATE therapy. J Nucl Med. 2016;57(4):503\u20138.","journal-title":"J Nucl Med"},{"key":"826_CR27","doi-asserted-by":"publisher","first-page":"103299","DOI":"10.1016\/j.ejmp.2024.103299","volume":"119","author":"V Vasi\u0107","year":"2024","unstructured":"Vasi\u0107 V, Gustafsson J, Nowshahr EY, Stenvall A, Beer AJ, Gleisner KS, et al. A PBPK model for PRRT with [177Lu]Lu-DOTA-TATE: comparison of model implementations in SAAM II and MATLAB\/SimBiology. Phys Med. 2024;119:103299.","journal-title":"Phys Med"},{"issue":"1","key":"826_CR28","first-page":"41","volume":"37","author":"NRR Zaid","year":"2022","unstructured":"Zaid NRR, Kletting P, Winter G, Beer AJ, Glatting G. A Whole-Body physiologically based pharmacokinetic model for alpha particle emitting bismuth in rats. Cancer Biother Radiopharm. 2022;37(1):41\u20136.","journal-title":"Cancer Biother Radiopharm"},{"issue":"1","key":"826_CR29","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1055\/a-1253-1535","volume":"60","author":"NJ Begum","year":"2021","unstructured":"Begum NJ, Glatting G, Eiber M, Beer AJ, Kletting P. An in Silico study on the effect of the radionuclide half-life on PET\/CT imaging with PSMA-targeting radioligands. Nuklearmedizin. 2021;60(1):33\u20137.","journal-title":"Nuklearmedizin"},{"issue":"2","key":"826_CR30","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1002\/mp.14622","volume":"48","author":"D Hardiansyah","year":"2021","unstructured":"Hardiansyah D, Kletting P, Begum NJ, Eiber M, Beer AJ, Pawiro SA, et al. Important Pharmacokinetic parameters for individualization of 177Lu-PSMA therapy: a global sensitivity analysis for a physiologically-based pharmacokinetic model. Med Phys. 2021;48(2):556\u201368.","journal-title":"Med Phys"},{"issue":"12","key":"826_CR31","doi-asserted-by":"publisher","first-page":"2132","DOI":"10.3390\/pharmaceutics13122132","volume":"13","author":"NRR Zaid","year":"2021","unstructured":"Zaid NRR, Kletting P, Winter G, Prasad V, Beer AJ, Glatting G. A physiologically based pharmacokinetic model for in vivo alpha particle generators targeting neuroendocrine tumors in mice. Pharmaceutics. 2021;13(12):2132.","journal-title":"Pharmaceutics"},{"issue":"1","key":"826_CR32","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1093\/neuonc\/2.1.45","volume":"2","author":"DR Groothuis","year":"2000","unstructured":"Groothuis DR. The blood-brain and blood-tumor barriers: a review of strategies for increasing drug delivery. Neuro Oncol. 2000;2(1):45\u201359.","journal-title":"Neuro Oncol"},{"issue":"2","key":"826_CR33","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1097\/00004032-199508000-00003","volume":"69","author":"RW Leggett","year":"1995","unstructured":"Leggett RW, Williams LR. A proposed blood circulation model for reference man. Health Phys. 1995;69(2):187\u2013201.","journal-title":"Health Phys"},{"issue":"2","key":"826_CR34","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1097\/00004032-199102000-00001","volume":"60","author":"RW Leggett","year":"1991","unstructured":"Leggett RW, Williams LR. Suggested reference values for regional blood volumes in humans. Health Phys. 1991;60(2):139\u201354.","journal-title":"Health Phys"},{"key":"826_CR35","volume-title":"Report of the task group on reference Man. ICRP publication 23","author":"WS Snyder","year":"1975","unstructured":"Snyder WS, Cook MJ, Nasset ES, Karhausen RS, Howells GP. Report of the task group on reference Man. ICRP publication 23. Oxford: Elsevier; 1975."},{"issue":"Suppl 2","key":"826_CR36","first-page":"S414P200","volume":"41","author":"G Winter","year":"2014","unstructured":"Winter G, Drescher A, Baur B, Solbach C, Reske SN, Beer AJ. Comparative analysis of chelator-modified peptides for imaging of prostate carcinoma. Eur J Nucl Med Mol Imaging. 2014;41(Suppl 2):S414P200.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"826_CR37","unstructured":"Estimation MATLABML. Error model: error parameter estimation functions, proportional https:\/\/de.mathworks.com\/help\/simbio\/ref\/sbiofit.html#bual8r7."},{"issue":"2","key":"826_CR38","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1118\/1.2135907","volume":"33","author":"RF Muzic Jr","year":"2006","unstructured":"Muzic RF Jr., Christian BT. Evaluation of objective functions for estimation of kinetic parameters. Med Phys. 2006;33(2):342\u201353.","journal-title":"Med Phys"},{"issue":"8","key":"826_CR39","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1007\/s002590050462","volume":"26","author":"M Cremonesi","year":"1999","unstructured":"Cremonesi M, Ferrari M, Zoboli S, Chinol M, Stabin MG, Orsi F, et al. Biokinetics and dosimetry in patients administered with (111)In-DOTA-Tyr(3)-octreotide: implications for internal radiotherapy with (90)Y-DOTATOC. Eur J Nucl Med. 1999;26(8):877\u201386.","journal-title":"Eur J Nucl Med"},{"key":"826_CR40","doi-asserted-by":"crossref","unstructured":"Eigenmann MJ, Karlsen TV, Wagner M, Tenstad O, Weinzierl T, Fauti T et al. Pharmacokinetics and pharmacodynamics of T-Cell bispecifics in the tumour. Interstitial Fluid. 2021;13(12).","DOI":"10.3390\/pharmaceutics13122105"},{"key":"826_CR41","doi-asserted-by":"crossref","unstructured":"Wastney ME, Patterson BH, Linares OA, Greif PC. Investigating biological systems using modeling. Academic Press Inc. 1998.","DOI":"10.1016\/B978-0-08-092645-2.50025-0"},{"issue":"1","key":"826_CR42","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13550-023-01007-z","volume":"13","author":"AB Peterson","year":"2023","unstructured":"Peterson AB, Mirando DM, Dewaraja YK. Accuracy and uncertainty analysis of reduced time point imaging effect on time-integrated activity for 177Lu-DOTATATE PRRT in patients and clinically realistic simulations. EJNMMI Res. 2023;13(1):57.","journal-title":"EJNMMI Res"},{"key":"826_CR43","doi-asserted-by":"crossref","unstructured":"G\u00e5lne A, Almquist H, Almquist M, Hindorf C, Ohlsson T, Nordenstr\u00f6m E et al. A prospective observational study to evaluate the effects of long-acting somatostatin analogs on 68Ga-DOTATATE uptake in patients with neuroendocrine tumors. J Nuclear Med. 2019:1717\u201323.","DOI":"10.2967\/jnumed.119.226332"},{"key":"826_CR44","doi-asserted-by":"crossref","unstructured":"Chahid Y, Hashimi K, van de Garde E, Kl\u00fcmpen H-J, Hendrikse NH, Booij J, et al. The influence of long-acting somatostatin analogs on 68Ga-DOTATATE uptake in patients with neuroendocrine tumors. Clinical Nuclear Medicine; 2023.","DOI":"10.1097\/RLU.0000000000004776"},{"issue":"12","key":"826_CR45","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.2967\/jnumed.119.226332","volume":"60","author":"A G\u00e5lne","year":"2019","unstructured":"G\u00e5lne A, Almquist H, Almquist M, Hindorf C, Ohlsson T, Nordenstr\u00f6m E, et al. A prospective observational study to evaluate the effects of Long-Acting somatostatin analogs on 68Ga-DOTATATE uptake in patients with neuroendocrine tumors. J Nucl Med. 2019;60(12):1717\u201323.","journal-title":"J Nucl Med"},{"issue":"9","key":"826_CR46","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1097\/RLU.0000000000004776","volume":"48","author":"Y Chahid","year":"2023","unstructured":"Chahid Y, Hashimi K, van de Garde EMW, Kl\u00fcmpen H-J, Hendrikse NH, Booij J, et al. The influence of long-acting somatostatin analogs on 68Ga-DOTATATE uptake in patients with neuroendocrine tumors. Clin Nucl Med. 2023;48(9):757\u201362.","journal-title":"Clin Nucl Med"}],"container-title":["EJNMMI Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40658-025-00826-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40658-025-00826-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40658-025-00826-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:13:17Z","timestamp":1769566397000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40658-025-00826-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["826"],"URL":"https:\/\/doi.org\/10.1186\/s40658-025-00826-4","relation":{},"ISSN":["2197-7364"],"issn-type":[{"value":"2197-7364","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]},"assertion":[{"value":"11 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The trial was conducted in accordance with ICH GCP and the Declaration of Helsinki and was approved by the regional ethics review board (EPN Lund 2011\/287).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors read the manuscript and consented to its publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"9"}}