Utility of Gallium-68-DOTATATE PET CT in Surveillance of Resected Gastroenteropancreatic NET
Bibliographic record
Abstract
Background/Objectives: For completely resected well differentiated (WD) gastroenteropancreatic (GEP) NET, guidelines differ in recommendations for utilization of SSTR-based functional imaging in post-operative surveillance. While 111In-Octreotide has previously been the standard of care, imaging with 68Ga-labelled peptides has expanded in recent years due to increased sensitivity to detect smaller volume diseases and reduced costs. Though many centres have widely adopted imaging with 68Ga-labelled peptides, its role in surveillance of resected GEP NET has not been well defined. We sought to characterize current utilization of imaging with 68Ga-DOTATATE PET CT (68Ga-DOTA) for post-operative surveillance of WD GEP NET and assess the impact on clinical management. Methods: We conducted a retrospective review of all 68Ga-DOTA scans performed from April 2019 to August 2024. Inclusion criteria were age ≥ 18 years with WD grade 1 and 2 GEP NET that had undergone curative-intent surgery, had Stage I-III disease at diagnosis, and had 68Ga-DOTA post-operatively. Results: Forty-six scans met the inclusion criteria. We identified four indications for 68Ga-DOTA: (1) post-operative assessment (n = 12); (2) routine surveillance (n = 18); (3) recurrence suspected based on cross-sectional imaging (n = 10); and (4) recurrence suspected based on biochemical monitoring (n = 6). Avidity for each indication was observed in 45%, 8%, 50%, and 80%, respectively. Initiation of long-acting somatostatin analogue was the most common management following avidity. Conclusions: 68Ga-DOTA best informed clinical decision making when there was clinical suspicion for residual or metastatic disease post-operatively or based on cross-sectional imaging or biochemistry. The utility of this modality for routine surveillance appears limited.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".