PET/CT and MRI in Evaluating Cervical Cancer
Bibliographic record
Abstract
Positron emission tomography (PET)/computed tomography (CT) and magnetic resonance (MR) imaging are two most important imaging tools for evaluating cervical cancer in clinic. They have improved the accuracy of tumor staging and prognosis predicting in a large part. PET/CT is superior for lymph node (LN) status and metastasis to other imaging modalities. And it could differ among tumor types and grades according to maximum standardized uptake value (SUVmax). MRI is not sensitive to LN metastasis, but it shares the advantage of therapeutic response and recurrence evaluation with PET/CT. Recently, emerging functional imaging modality Diffusion-weighted imaging (DWI) has been showing its superiority on evaluation of cervical carcinoma as well. This article describes both advantages and limitations of MR imaging and PET/CT in evaluating cervical cancer, and reviews the current role of imaging techniques mentioned above.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".