Surveillance imaging with FDG-PET/CT in the post-operative follow-up of stage 3 melanoma
Bibliographic record
Abstract
Background: As early detection of recurrent melanoma maximizes treatment options, patients usually undergo post-operative imaging surveillance, increasingly with FDG-PET/CT (PET). To assess this, we evaluated stage 3 melanoma patients who underwent prospectively applied and sub-stage-specific schedules of PET surveillance. Patients and methods: From 2009, patients with stage 3 melanoma routinely underwent PET +/- MRI brain scans via defined schedules based on sub-stage-specific relapse probabilities. Data were collected regarding patient characteristics and outcomes. Contingency analyses were carried out of imaging outcomes. Results: One hundred and seventy patients (stage 3A: 34; 3B: 93; 3C: 43) underwent radiological surveillance. Relapses were identified in 65 (38%) patients, of which 45 (69%) were asymptomatic. False-positive imaging findings occurred in 7%, and 6% had treatable second (non-melanoma) malignancies. Positive predictive values (PPV) of individual scans were 56%-83%. Negative scans had predictive values of 89%-96% for true non-recurrence [negative predictive values (NPV)] until the next scan. A negative PET at 18 months had NPVs of 80%-84% for true non-recurrence at any time in the 47-month (median) follow-up period. Sensitivity and specificity of the overall approach of sub-stage-specific PET surveillance were 70% and 87%, respectively. Of relapsed patients, 33 (52%) underwent potentially curative resection and 10 (16%) remained disease-free after 24 months (median). Conclusions: Application of sub-stage-specific PET in stage 3 melanoma enables asymptomatic detection of most recurrences, has high NPVs that may provide patient reassurance, and is associated with a high rate of detection of resectable and potentially curable disease at relapse.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".