Updates on the Role of FDG-PET/CT in Gynecological Malignancies
Bibliographic record
Abstract
PET/CT has had an evolutionary role in Oncology. Gynecological malignancies have been increasing in incidence in the last decades. Delay in diagnosis and management have led to worsening prognosis among the patients. Lowering the threshold in suspecting these tumors, may significantly improve the patients’ overall survival. In this review we will address the role of FDG-PET/CT in diagnosing, staging, assessing the response to therapy and predicting survival in gynecological malignancies, namely endometrial, ovarian and cervical cancer. We will briefly compare the diagnosing ability of PET/MRI to PET/CT. We will address the interesting fact about simultaneously utilizing the Apparent Diffusion Coefficient (ADC) with the Standardized Uptake Value (SUV) in hybrid MRI imaging and we will also discuss about the role of PET/MRI in diagnosing primary and recurrent gynecological malignancies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".