Toward Single-Time-Point Image-Based Dosimetry of<sup>177</sup>Lu-PSMA-617 Therapy
Bibliographic record
Abstract
Radiopharmaceutical therapies (RPTs) with <sup>177</sup>Lu-prostate-specific membrane antigen (PSMA) ligands have demonstrated promising results for the treatment of metastatic castration-resistant prostate cancer. The lack of absorbed-dose–effect relationships currently prevents patient-specific activity personalization. To ease the implementation of dosimetry in the routine clinical workflow for RPT, simplified methods such as single-time-point (STP) instead of multiple-time-point (MTP) imaging protocols are required. This work aimed at assessing differences in the time-integrated activity (TIA) of STP versus MTP image-based dosimetry for <sup>177</sup>Lu-PSMA-617 therapy. <b>Methods:</b> Twenty metastatic castration-resistant prostate cancer patients with MTP quantitative <sup>177</sup>Lu-SPECT imaging data (∼24, 48, and 72 h post injection (p.i.)) available on first and second <sup>177</sup>Lu-PSMA-617 therapy cycles were included in this study. Time–activity curves were fitted for kidneys and lesions to derive effective half-lives and yield a reference TIA. STP approaches involved the formula by Hänscheid (STP<sub>H</sub>) and a prior-information method (STP<sub>prior</sub>) that uses the effective half-lives from the first therapy cycle. All time points were considered for the STP approaches. Percentage differences (PDs) in TIA between STP and MTP were compared for the second therapy cycle. <b>Results:</b> Using STP<sub>H</sub> at 48 h p.i. for kidneys showed a −1.3% ± 5.6% PD from MTP, whereas STP<sub>prior</sub> showed a PD of 4.6% ± 6.2%. The smallest average PDs for the 56 investigated individual lesions were found using STP<sub>prior</sub> at 48 h p.i., at only 0.4% ± 14.9%, whereas STP<sub>H</sub> at 72 h p.i. had a smallest PD of −1.9% ± 14.8%. <b>Conclusion:</b> STP dosimetry for <sup>177</sup>Lu-PSMA-617 therapy using a single SPECT/CT scan at 48 or 72 h p.i. is feasible, with a PD of less than ±20% compared with MTP. The validity of both STP<sub>H</sub> and STP<sub>prior</sub> has been demonstrated. We believe this finding can increase the adoption of dosimetry and facilitate implementation in routine clinical RPT workflows. Doing so will ultimately enable the finding of dose–effect relationships based on fixed therapy activities that may, in future, allow for absorbed-dose–based RPT activity personalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".