<sup>18</sup> F-FDG PET/CT for the Detection of Immune-Related Adverse Events in Patients With Metastatic Melanoma Receiving Immunotherapy
Bibliographic record
Abstract
Purpose: To evaluate frequency and distribution of immune-related adverse events detected by 18 F-FDG PET/CT in patients with metastatic melanoma undergoing immunotherapy. Materials and Methods: Retrospective observational cohort study evaluating 147 patients with metastatic melanoma treated with immunotherapy and referred for therapy response assessment with 18 F-FDG PET/CT at our institution from January 2010 to August 2022. In total, 201 PET/CT scans performed at various time points were analyzed. IRAEs detected on PET/CT were compared against clinical reference standards, including physical examinations, laboratory tests, and biopsies. Diagnostic performance metrics (sensitivity, specificity, positive predictive value, negative predictive value), and diagnostic yields were calculated. Results: There were 36/147 patients (24.5%) with IRAEs recorded according to standard of reference, with 39 IRAEs in the entire cohort. At time point level, PET/CT identified 36/36 (100%) patients with IRAEs confirmed by the reference standard, while clinical suspicion identified 26/36 (72%) cases. At IRAE level, PET/CT identified 36/39 (92%) of IRAEs confirmed by the reference standard. Thirteen out of 39 (33.3%) cases identified on PET/CT were not suspected clinically but confirmed by the reference standard. The most frequent IRAEs, both suspected clinically and on PET/CT, corresponded to thyroiditis and colitis. Among the PET/CT positive cases, the majority corresponded to grade 2 severity. Conclusion: 18 F-FDG PET/CT is highly effective in detecting IRAEs in patients with metastatic melanoma on immunotherapy, uncovering clinically unsuspected events in up to 33% of cases. These results highlight its important role in early detection, guiding timely interventions, and improving overall outcomes of immunotherapy-related toxicities.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".