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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".