Imaging-derived biomarkers from 68Ga-DOTATOC PET/CT scans to predict survival of patients with neuroendocrine tumors after PRRT with 177Lu-DOTATATE
Bibliographic record
Abstract
Abstract Background Neuroendocrine tumors have increased in prevalence and diversity in recent years and are often diagnosed at metastatic stages. Compared with nonradioactive systemic treatment with somatostatin analogs, peptide receptor radionuclide therapy (PRRT) has shown superior overall survival benefits for well-differentiated neuroendocrine tumor patients. This study aimed to identify biomarkers from 68Ga‒DOTATOC PET/CT scans to predict survival in patients treated with PRRT in the clinic. Methodology This retrospective study analyzed 68Ga-DOTATOC PET/CT data from 67 NET patients undergoing PRRT. Tumor volumes and SUV metrics were segmented using standardized protocols. Radiomics features from liver metastases were extracted and preprocessed for analysis. Data were analysed via Kaplan-Meier, Cox regression, and PCA to evaluate the prognostic value of volumetric-, radiomics-, and clinicopathological parameters. Results This study included scans from 67 patients with an average age of 67 years. The mean survival time was 46.5 months, with 43% of patients alive or lost to follow-up at the conclusion of data collection. Despite comprehensive analyses, neither volumetric parameters, including total tumor volume and organ-specific tumor volume, nor SUV values (SUVmax and SUVmean) were robust predictors of overall survival. K‒M and Cox regression analyses revealed no significant differences in survival between the high- and low-risk groups for these parameters. Furthermore, radiomics features extracted from liver metastases did not demonstrate significant prognostic value. Conclusion Quantification of 68Ga-DOTATOC PET/CT-derived parameters offers limited prognostic value for OS in NET patients who are receiving PRRT in clinical practice. These findings might emphasize the current robust integration of imaging in clinical decision-making for NET management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".