Does the Pretherapeutic Tumor SUV in 68Ga DOTATOC PET Predict the Absorbed Dose of 177Lu Octreotate?
Bibliographic record
Abstract
PURPOSE: Selection of candidates for peptide receptor radionuclide therapy (PRRT) is increasingly based on receptor positron emission tomography (PET) imaging, including the common tracer 68Ga DOTATOC. However, no studies have yet compared standardized uptake values (SUVs) and absorbed doses in this field. MATERIALS AND METHODS: We retrospectively analyzed a consecutive cohort of 21 patients with 61 evaluable tumor lesions undergoing both pretherapeutic 68Ga DOTATOC-PET/CT (Biograph Duo [Siemens Medical Solutions, Erlangen, Germany]; PET acquisition, 75.3 ± 15.4 minutes postinjection; 117.3 ± 33.9 MBq 68Ga DOTATOC) and PRRT with Lu octreotate (7.47 ± 1.39 GBq; intratherapeutic tumor dosimetry with serial whole-body scans; 1, 2, and 4 days postinjection) at our institution. SUVs were compared with the tumor-absorbed doses per injected activity (D/A0) of the subsequent first treatment cycle. RESULTS: The correlation of SUV and D/A0 was r = 0.72 (SUVmean) and r = 0.71 (SUVmax), both P < 0.001. Pancreatic origin and hepatic localization were associated with higher D/A0, and chromogranin A level and Ki-67 index had no influence on SUV or D/A0. High-SUV lesions (SUVmean >15; SUVmax >25) resulted in high D/A0 (>10 Gy/GBq) in 66.7% to 70.8% and low D/A0 (<5 Gy/GBq) in only 8.3% to 12.5% on subsequent PRRT. The mentioned low D/A0 range, on the other hand, was achieved by all lesions with SUVmean <7 or SUVmax <9. CONCLUSIONS: Somatostatin receptor PET imaging may predict tumor-absorbed doses. The ability to indicate insufficient target irradiation by a low SUV could aid in selection of appropriate candidates for PRRT. However, larger series are needed to confirm and validate these initial findings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".