Surveillance imaging with FDG-PET/CT in the post-operative follow-up of stage 3 melanoma
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 it