A74 SURVEILLANCE IMAGING FOLLOWING COMPLETELY RESECTED GASTROENTEROPANCREATIC NEUROENDOCRINE TUMORS: A SINGLE CENTER AUDIT OF LOCAL PRACTICE PATTERNS
Bibliographic record
Abstract
Abstract Background Neuroendocrine tumours (NET) are a heterogenous group of neoplasms that secrete peptides and neuroamines. For patients with potentially malignant gastroenteropancreatic (GEP) NET, surgical resection represents the only curative option. GEP NETs are characterized by long periods of disease-free survival and time-to-recurrence following surgical resection. Clinical guidelines recommend surveillance with cross sectional imaging, either CT or MRI, for at least 10 years. Aims The purpose of this study was to characterize local practice patterns of imaging surveillance (modality, frequency, and duration of follow-up) and how this compares to guideline recommendations. Methods We retrospectively reviewed clinical and imaging records from patients diagnosed with well-differentiated GEP NET at our center from January 2005 to July 2020 inclusive. Eligible cases were identified by a data analyst from the Alberta Cancer Board with each case being manually screened for eligibility. Exclusion criteria included patients with metastatic disease at presentation, G1 appendiceal NET < 1 cm, R0 G1 T1 rectal NET, and insulinoma. Location of primary NET, modality of surveillance imaging, date of test and duration of follow-up collected. The mean number of surveillance scans per person and per person-year follow-up based on the location of the primary NET were calculated. Results A total of 387 cases were initially retrieved with 62 eligible cases identified. The mean length of follow-up was 71 months (range 8 to 147). The mean number of surveillance scans was 7 (range 2 to 14) and the mean number of surveillance scans per person year was 1.1. Frequency of surveillance scans per year of follow-up did not differ based on the location of the primary tumor (p=0.966). Imaging modalities included cross sectional imaging (MRI and contrast enhanced CT) and nuclear medicine imaging (octreotide, MIBG, F-18 FDG-PET, and Gallium-68 DOTATATE PET CT). Most commonly, cross-sectional imaging was performed with CT or MRI representing 38% (n=166) and 39% (n=170) of all surveillance respectively. Nuclear medicine imaging was used in 15% (n=61) of surveillance scans and 3% used combined cross-sectional and nuclear medicine. Amongst cases with resection date >10 years (n=8) mean length of follow-up was 119 months (9.9 years). Conclusions Frequency and modality of imaging at our center is generally in accordance with current clinical guidelines, though the role of nuclear medicine imaging in this setting has not been established. CT and MRI were utilized equally during surveillance. The burden of these modalities in terms of radiation exposure and cost warrants further evaluation. Funding Agencies None
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".