False-positive Findings of Large Vessel Vasculitis on FDG-PET in Patients Treated With Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Fluorine-18 fluorodeoxygluocose positron emission tomography (FDG-PET) is increasingly used in the evaluation of response to immune checkpoint inhibitor (ICI) therapy. Incidental findings of increased vessel wall uptake may prompt the concern for ICI-induced large vessel vasculitis (LVV). Precise radiographic and clinical evaluation is required to determine if this represents true vasculitis, as use of immune suppression and ICI discontinuation can have significant impacts on patient outcomes. We performed a retrospective case analysis of 4 consecutive patients referred to 2 rheumatology clinics treated with ICI with incidental findings of LVV on FDG-PET, reviewing their clinical course and radiographic findings. All 4 cases had FDG-PET scans for routine oncology indications and had no associated clinical features of LVV. One patient was treated with corticosteroids and no patients developed any clinical evidence of vasculitis during a mean follow-up period of 17 months (range: 7-33 mo). All FDG-PET images reporting LVV underwent a standardized analysis to identify any technical issues or concerns with interpretation. In review of imaging, 3 of the cases may have been due to delayed tracer to scan interval leading to misinterpretation of vascular uptake as suspected LVV. Recognition of technical pitfalls in FDG-PET interpretation is crucial to inform the need for immunosuppression and the safety of continued ICI therapy.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".