Can<sup>177</sup>Lu-DOTATATE Kidney Absorbed Doses be Predicted from Pretherapy SSTR PET? Findings from Multicenter Data
Bibliographic record
Abstract
Before performing 177Lu-DOTATATE therapy for neuroendocrine tumors, somatostatin receptor (SSTR) PET imaging is currently used to confirm sufficient tumor SSTR expression, but it also has potential to be used to personalize treatment by predicting absorbed doses to critical organs. This study aims to validate the predictive capability of SSTR PET in anticipating renal absorbed dose in the first cycle of 177Lu-DOTATATE using a multicenter dataset to analyze and derive insights from a broader patient population. Methods: Retrospective data from 5 centers were included in this study: 1 in Canada (n = 25), 1 in Norway (n = 75), 1 in Sweden (n = 18), and 2 in the United States (n = 36 and n = 26). At each center, pretherapy SSTR PET/CT imaging and postcycle 1 177Lu imaging–based dosimetry were performed according to site-specific protocols. The mixed-effects model treating centers as random effects was developed using baseline SSTR PET renal uptake values to predict renal absorbed dose from 177Lu-DOTATATE. Additionally, leave-one-center-out cross-validation and leave-one-sample-out cross-validation were implemented for external and internal validation, respectively, measuring mean absolute error and mean relative absolute error. Results: Across all participating centers, the median cycle 1 renal absorbed dose was 0.56 Gy/GBq (range, 0.14–1.27 Gy/GBq), whereas the median pretherapy SSTR PET renal uptake was 110.7 Bq/mL/MBq (range, 28.6–287.7 Bq/mL/MBq). The differences among center means were statistically significant for both absorbed dose and PET uptake (P < 0.0001 from 1-way ANOVA). A significant (P < 0.05) correlation was observed between kidney SSTR PET uptake and 177Lu-DOTATATE absorbed dose for each center (center-specific coefficient of determination ranged from 0.14 to 0.53). When data across all centers were aggregated, the mixed-effects model achieved a coefficient of determination of 0.25 (P < 0.01), resulting in an mean absolute error of 0.15 Gy/GBq (SD, 0.11 Gy/GBq) and an mean relative absolute error of 28% (SD, 24%) for external validation and 0.12 Gy/GBq (SD, 0.10 Gy/GBq) and 22% (SD, 20%) for internal validation. Conclusion: The correlations observed between SSTR PET renal uptake and 177Lu-DOTATATE absorbed dose to kidneys across a multicenter population are statistically significant yet modest. The prediction model achieved a mean relative absolute error 28% or less for both external and internal validation of PET-predicted absorbed doses. The intercenter differences suggest the need for standardized imaging protocols and dosimetry workflows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".