Additional Value of 2-[18F]FDG PET/CT Comparing to MRI in Treatment Approach of Anal Cancer Patients
Bibliographic record
Abstract
Accurate staging and treatment planning are imperative for precise management in Anal Cancer (ACa) patients. We aimed to evaluate the additive and prognostic value of pre-treatment 2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) in the staging and management of ACa compared to magnetic resonance imaging (MRI). This retrospective study was conducted on 54 patients. Pre-treatment 2-[18F]FDG PET/CT studies and MRI reports were compared considering the primary tumor, pelvic lymph nodes, and metastatic lesions. The impact of 2-[18F]FDG PET/CT in the management and its prognostic value, using maximum standardized uptake value (SUVmax), were assessed. Discordant findings were found in 46.3% of patients (5 in T; 1 in T and N; 18 in N; and 1 in M stage). 2-[18F]FDG PET/CT resulted in up-staging in 9.26% and down-staging in 3.7% of patients. Perirectal lymph nodes were metabolically inactive in 12.9% of patients. Moreover, 2-[18F]FDG PET/CT resulted in management change in 24.1% of patients. Finally, SUVmax provided no prognostic value. 2-[18F]FDG PET/CT altered staging and management in a sizable number of patients in this study, and supports a need for a change in guidelines for it to be used as a routine complementary test in the initial management of ACa.
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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 | 0.004 |
| 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".