68Ga-DOTATATE PET/CT in the Initial Staging of Well-Differentiated Gastroenteropancreatic and Non-Gastroenteropancreatic Neuroendocrine Tumors: Results of a Prospective Registry
Bibliographic record
Abstract
Background and Objectives: At diagnosis, the initial staging of well-differentiated neuroendocrine tumors (WD NETs) aids in treatment planning. The somatostatin receptor (SSTR)-PET has been recommended for staging of WD NETs although limited data are available on its impact on non-gastroeneteropancreatic (GEP) NETs. The main purpose of this study was to compare the stage migration after the addition of SSTR-PET to the workup of patients at the initial staging of GEP NETs to those with non-GEP NETs, and its potential impact on patient management. Methods: This prospective study included patients with WD NETs at initial staging. Demographic data, results of conventional and SSTR-PET staging, and SUVmax were recorded. Three panels of experts assessed the potential impact of SSTR-PET to management. Results: There were 482 patients, including 376 with gastroenteropancreatic (GEP) NETs and 106 non-GEP NETs with a median SUVmax of 34.7 [Q1, Q3: 22.8, 59.1]) and 19.0 [Q1, Q3: 7.9, 39.8]), respectively; p < 0.001. The discordant M-stage was recorded in 111/473 patients (23.5%). PET suggested a higher stage in 78/369 GEP NETs (21.1%), including the detection of extrahepatic metastatic disease in 42/114 (36.8%) patients with liver metastases only on CI. For non-GEP NETs, PET suggested a higher stage in 10/104 (9.6%) and CI suggested a higher stage in 15/104 (14.4%), with CI detecting liver metastases more frequently. The potential impact to management for patients with discordant M-stage was scored as moderate to high between 57/101 (56.4%) and 79/101 (78.2%) of patients. Conclusions: One in five patients are upstaged following SSTR-PET, more frequently with GEP NETs than others. SSTR-PET identifies extrahepatic metastatic disease in >1/3 of patients with presumed liver-only metastases on CI. Stage migration following SSTR-PET may result in frequent moderate or significant management change.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".