18F-FDG PET/MRI for restaging esophageal cancer after neoadjuvant chemoradiotherapy
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate whether 18F-fluorodeoxyglucose ( 18 F-FDG) PET/MRI may potentially improve tumor detection after neoadjuvant chemoradiotherapy (nCRT) for esophageal cancer. METHODS: This was a prospective, single-center feasibility study. At 6-12 weeks after nCRT, patients underwent standard 18 F-FDG PET/computed tomography (CT) followed by PET/MRI, and completed a questionnaire to evaluate burden. Two teams of readers either assessed the 18 F-FDG PET/CT or the 18 F-FDG PET/MRI first; the other scan was assessed 1 month later. Maximum standardized uptake value corrected for lean body mass (SUL max ) and mean apparent diffusion coefficient (ADC mean ) were measured at the primary tumor location. Histopathology of the surgical resection specimen served as the reference standard for diagnostic accuracy calculations. When patients had a clinically complete response and continued active surveillance, response evaluations until 9 months after nCRT served as a proxy for ypT and ypN (i.e. 'ycT' and 'ycN'). RESULTS: In the 21 included patients [median age 70 (IQR 62-75), 16 males], disease recurrence was found in the primary tumor in 14 (67%) patients (of whom one ypM+, detected on both scans) and in locoregional lymph nodes in six patients (29%). Accuracy (team 1/team 2) to detect yp/ycT+ with 18 F-FDG PET/MRI vs. 18 F-FDG PET/CT was 38/57% vs. 76/61%. For ypN+, accuracy was 63/53% vs. 63/42%, resp. Neither SUL max (both scans) nor ADC mean were discriminatory for yp/ycT+ . Fourteen of 21 (67%) patients were willing to undergo a similar 18 F-FDG PET/MRI examination in the future. CONCLUSION: 18 F-FDG PET/MRI currently performs comparably to 18 F-FDG PET/CT. Improvements in the scanning protocol, increasing reader experience and performing serial scans might contribute to enhancing the accuracy of tumor detection after nCRT using 18 F-FDG PET/MRI. TRIAL REGISTRATION: Netherlands Trial Register NL9352.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".