Performance of 18F-fluorodesoxyglucose positron-emission tomography combined with low-dose computed tomography for cancer screening in patients with unprovoked venous thromboembolism
Bibliographic record
Abstract
PURPOSE: Small series have suggested that Fluorodesoxyglucose Positron-Emission-Tomography with Computed-Tomography (FDG-PET/CT) is feasible to screen for cancer in patients with unprovoked venous thromboembolism (VTE), but without validation in a large population. The aim was to assess diagnostic accuracy indices of FDG-PET/CT for occult cancer diagnosis in patients with unprovoked VTE. MATERIALS AND METHODS: We analysed patients from the FDG-PET/CT group of a randomized trial that compared a screening strategy based on FDG-PET/CT with a limited screening strategy for occult malignancy detection in patients with unprovoked VTE. FDG-PET/CT was interpreted as positive for cancer, as negative or as equivocal. Patients were considered as having cancer on the basis of screening results, or of any test performed during a two-years follow-up period. We ran two sets of analysis, considering patients with equivocal FDG-PET/CT as positive, then as negative for malignancy. RESULTS: Between March 2009, and August 2012, 172 patients were included. FDG-PET/CT was interpreted as positive for malignancy in 10 patients (5.8%), as equivocal in 23 patients (13.4%) and as negative in 139 patients (80.8%). Malignancy was diagnosed in 7/10 (70.0%), 2/23 (8.7%) and 1/139 (0.7%) patients, respectively. Grouping positive and equivocal results, sensitivity and specificity were 90% (95%CI 60% to 98%) and 85% (95%CI 79% to 90%), respectively. Grouping negative and equivocal results, sensitivity and specificity were 70% (95%CI 40% to 89%) and 98% (95%CI 95% to 99%), respectively. CONCLUSION: FDG-PET/CT showed good accuracy for occult cancer screening in patients with unprovoked VTE. Remaining challenges include the need to define specific interpretation criteria in this dedicated population.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".