Octreotide scintigraphy versus DOTATOC PET: Concordance of Krenning scores and implications on PRRT treatment eligibility.
Bibliographic record
Abstract
e16339 Background: Peptide receptor radionuclide therapy (PRRT) is an established treatment for patients with somatostatin receptor (SSTR)-positive gastroenteropancreatic neuroendocrine tumours (GEP-NETs). Treatment eligibility uses the Krenning score. SSTR PET improves sensitivity compared to planar octreotide scintigraphy, but has reduced availability in clinical practice. We compared Krenning scores obtained with planar octreotide scans and SSTR PET for well-differentiated NETs and assessed changes in PRRT eligibility based on criteria from NETTER-1 and NETTER-2. Methods: We retrospectively reviewed 171 patients with well-differentiated GEP-NETs. Patients were imaged using In-111 octreotide scintigraphy and Ga-68 DOTATOC PET. Tumour uptake was graded using the Krenning score. Additional features such as primary tumour site, tumour grade, metastatic status, and FDG PET status were recorded. Wilcoxon’s signed rank test was used to compare Krenning scores for each imaging modality, and chi-squared was used to compare between groups. Logistic regression was used to identify correlations between each feature and SSTR PET-based Krenning score. Results: Krenning score was significantly higher for SSTR PET (2.92 ± 1.37) than planar imaging (1.94 ± 1.67) ( P < 10 -12 ). The percentage of patients eligible for PRRT based on NETTER-1 criteria (i.e., Krenning 2-4) was 58.5% and 83.0% with planar imaging and SSTR PET, respectively ( P < 10 -6 ). Meanwhile, the percentage of patients eligible based on NETTER-2 (Krenning score 3-4) was 49.7% and 78.4% with planar imaging and SSTR PET, respectively ( P < 10 -8 ). In patients that met eligibility criteria with planar imaging, 96.0% and 96.5% had concordant findings with SSTR PET (Krenning score 2-4 and 3-4, respectively). However, 60.5% and 64.8% of patients that did not meet criteria for PRRT with planar imaging (i.e., Krenning score 0-1 and 0-2, respectively) were eligible based on SSTR PET. Baseline metastatic disease status was significantly correlated with having Krenning score 3-4 on SSTR PET imaging (OR = 7.5 [2.9-19.5], P < 10 -5 ). For patients classified as Krenning 0-2 with planar imaging, those with metastatic disease were more likely to have discordant findings with SSTR PET (78.0% metastatic vs. 36.1% non-metastatic, P < 10 -4 ). Primary disease site, tumour grade, and FDG PET disease status did not correlate with SSTR PET Krenning score. Conclusions: SSTR PET results in higher Krenning scores than planar scintigraphy, which may impact the eligibility for PRRT, as defined by the NETTER-2 trial. SSTR PET may not be needed to confirm PRRT eligibility for patients with Krenning score 3-4 on In-111 octreotide scintigraphy. Caution is advised when extrapolating the results of one modality to another.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".