The process of time-activity curve (TAC) determination/calculation for radiopharmaceutical therapy (RPT) is generally heavily dependent on user- and site-dependent steps (e.g., the number and time schedule of measurement points to be used, selection of the fit function), each having a profound effect on the final values of the time-integrated activity (TIA) and thus on the calculated absorbed doses. Despite the high clinical importance of the TIA values, there is no consensus on how to process time-activity data or even a clear understanding of the influence of uncertainties and variations in personalized RPT dosimetry related to user-dependent TAC calculation. To address this critical unmet need, the TACTIC Challenge provided an opportunity to explore the variations in the way TACs are modeled for RPT applications. More than 120 groups from 30+ countries signed up for this data-driven challenge. In total, 95 individual groups submitted their results throughout the challenge.
Launched in January 2023, the TACTIC challenge consisted of three phases: warm-up, individual patient-based TAC, and feasibility of TAC fitting improvement using population-based data. By sharing already processed synthetic biokinetic data of 177Lu-PSMA (kidney, blood and tumor), participants were asked to model the TAC and calculate the time-integrated activity (TIA) values for each target organ to the best of their capability. Additionally, information about the type of the TAC curve and fit optimization parameters were submitted by each participant. The best-performing team was defined as the one with the lowest total RMSE error in the phase.
The final results of the challenge will be presented at the AAPM 2023 Annual Meeting & Exhibition in Houston and reported on in the upcoming publication of the study.
Table 1. Leaderboard of the 1st competition phase: fitting using individual biokinetic data
*optional
| # | Team/participant (university*) | Total RMSE | Organ RMSE | ||
| kidney | tumor | blood | |||
| 1 | Centro di Riferimento Oncologico di Aviano (CRO) IRCCS | 0.162 | 0.042 | 0.010 | 0.110 |
| 2 | Team Lee (University of Western Ontario) | 0.184 | 0.065 | 0.007 | 0.112 |
| 3 | William Erwin (MD Anderson) | 0.192 | 0.076 | 0.009 | 0.107 |
| 4 | CCNM (Cleveland Clinic) | 0.193 | 0.046 | 0.017 | 0.130 |
| 5 | Wencke (University Medical Center Hamburg-Eppendorf) | 0.194 | 0.080 | 0.007 | 0.107 |
| 6 | Berlteam (Charité – Universitätsmedizin Berlin) | 0.197 | 0.097 | 0.011 | 0.089 |
| 7 | Dosicro (Invicro) | 0.198 | 0.093 | 0.013 | 0.092 |
| 8 | MUSC Radiology (University of South Carolina) | 0.200 | 0.038 | 0.008 | 0.154 |
| 9 | RTX (Ratio Therapeutics) | 0.202 | 0.089 | 0.011 | 0.102 |
| 10 | UKA (University Hospital Augsburg) | 0.204 | 0.047 | 0.010 | 0.147 |
| 11 | Apple Fritter (Washington University in St. Louis) | 0.219 | 0.054 | 0.011 | 0.154 |
| 12 | POLI (Fondazione Poliambulanza) | 0.223 | 0.063 | 0.011 | 0.148 |
| 13 | Kesner Lab (Memorial Sloan Kettering Cancer Center) | 0.223 | 0.067 | 0.009 | 0.147 |
| 14 | University of Leipzig Medical Centre | 0.223 | 0.055 | 0.015 | 0.153 |
| 15 | Azienda Ospedaliero-Universitaria Pisana | 0.230 | 0.100 | 0.006 | 0.125 |
| 16 | Powerplayjamusa (Loyola University Medical Center) | 0.251 | 0.089 | 0.028 | 0.134 |
| 17 | LEDI (L'Institut de Radioprotection et de Sûreté Nucléaire) | 0.263 | 0.108 | 0.009 | 0.147 |
| 18 | CHUM (Université de Montréal) | 0.302 | 0.188 | 0.011 | 0.102 |
| 19 | European Radiation Dosimetry Group | 0.320 | 0.046 | 0.009 | 0.265 |
| 20 | Vappu (Helsinki University Hospital) | 0.336 | 0.055 | 0.013 | 0.267 |
| 21 | OHSU Physics (Oregon Health & Science University) | 0.357 | 0.074 | 0.010 | 0.272 |
| 22 | Blazers (University of Alabama at Birmingham) | 0.362 | 0.256 | 0.009 | 0.097 |
| 23 | SoltaniBioLab (UC Davis) | 0.399 | 0.085 | 0.028 | 0.286 |
| 24 | TORVE (Homepage Università degli Studi di Roma Tor Vergata) | 0.402 | 0.127 | 0.027 | 0.248 |
| 25 | Fittin this donut (Texas Children’s Hospital) | 0.452 | 0.151 | 0.165 | 0.136 |
| 26 | NukeDudes (University of Alberta) | 0.480 | 0.052 | 0.006 | 0.421 |
| 27 | QICPM (Princess Margaret Cancer Centre) | 0.513 | 0.120 | 0.006 | 0.386 |
| 28 | NUK I (Tirol Kliniken) | 0.559 | 0.065 | 0.009 | 0.484 |
| 29 | Lberens (University of Chicago) | 0.660 | 0.051 | 0.019 | 0.590 |
| 30 | Panda ( Pontifícia Universidade Católica do Rio Grande do Sul) | 0.670 | 0.172 | 0.014 | 0.485 |
| 31 | JNDT (Karolinska University) | 1.199 | 0.097 | 0.008 | 1.095 |
| 32 | Rhode Island Hospital, Brown University | 1.533 | 1.066 | 0.324 | 0.143 |
| 33 | UMN (University of Minnesota) | 1.687 | 0.100 | 0.021 | 1.566 |
Table 2. Leaderboard of the 2nd competition phase: fit model incorporating population-based biokinetic data
*optional
| # | Team/participant (university*) | Total RMSE | Organ RMSE | ||
| kidney | tumor | blood | |||
| 1 | CCNM (Cleveland Clinic) | 0.117 | 0.040 | 0.020 | 0.058 |
| 2 | University of Leipzig Medical Centre | 0.148 | 0.066 | 0.015 | 0.067 |
| 3 | European Radiation Dosimetry Group | 0.160 | 0.028 | 0.011 | 0.121 |
| 4 | Centro di Riferimento Oncologico di Aviano (CRO) IRCCS | 0.165 | 0.042 | 0.008 | 0.115 |
| 5 | MUSC Radiology (University of South Carolina) | 0.176 | 0.038 | 0.008 | 0.131 |
| 6 | Vappu (Helsinki University Hospital) | 0.177 | 0.074 | 0.007 | 0.096 |
| 7 | William Erwin (MD Anderson) | 0.192 | 0.076 | 0.009 | 0.107 |
| 8 | Wencke (University Medical Center Hamburg-Eppendorf) | 0.194 | 0.080 | 0.007 | 0.107 |
| 9 | UKA (University Hospital Augsburg) | 0.204 | 0.047 | 0.010 | 0.147 |
| 10 | Alepi | 0.206 | 0.047 | 0.006 | 0.152 |
| 11 | RTX (Ratio Therapeutics) | 0.213 | 0.086 | 0.012 | 0.116 |
| 12 | Kesner Lab (Memorial Sloan Kettering Cancer Center) | 0.223 | 0.067 | 0.009 | 0.147 |
| 13 | Team Lee (University of Western Ontario) | 0.228 | 0.075 | 0.006 | 0.146 |
| 14 | POLI (Fondazione Poliambulanza) | 0.229 | 0.075 | 0.006 | 0.148 |
| 15 | QIPCM (Princess Margaret Cancer Centre) | 0.231 | 0.066 | 0.013 | 0.153 |
| 16 | Paganetti Lab (MGH, Harvard University) | 0.241 | 0.051 | 0.005 | 0.185 |
| 17 | AppleFritter (Washington University in St. Louis) | 0.246 | 0.055 | 0.011 | 0.180 |
| 18 | Blazers (University of Alabama at Birmingham) | 0.475 | 0.475 | ||
| 19 | NukeDudes (University of Alberta) | 0.480 | 0.052 | 0.006 | 0.421 |
| 20 | LEDI (L'Institut de Radioprotection et de Sûreté Nucléaire) | 0.534 | 0.378 | 0.011 | 0.145 |
| 21 | Lberens (University of Chicago) | 0.860 | 0.066 | 0.016 | 0.778 |
| 22 | UMN (University of Minnesota) | 0.870 | 0.240 | 0.020 | 0.609 |
| 23 | SoltaniBioLab (UC Davis) | 0.927 | 0.547 | 0.061 | 0.319 |
| 24 | Oregon Health & Science University | 0.957 | 0.057 | 0.017 | 0.883 |
| 25 | Powerplayjamusa (Loyola University Medical Center) | 0.999 | 0.549 | 0.101 | 0.349 |
| 26 | Rhode Island Hospital, Brown University | 1.104 | 0.517 | 0.070 | 0.517 |
| 27 | Sajid Bashir | 2.208 | 0.626 | 0.446 | 1.136 |
| 28 | Panda (Pontifícia Universidade Católica do Rio Grande do Sul) | 4.069 | 0.304 | 3.107 | 0.658 |