Is There a Role for Positron Emission Tomography in Breast Cancer Staging?
Bibliographic record
Abstract
Positron emission tomography (PET) with fluorine-18 fluorodeoxyglucose (FDG) is a radiotracer imaging method that is used in the care of patients with cancer. We conducted a nonsystematic review of the literature regarding the applicability of this technique in patients with breast cancer, encompassing the impact of FDG-PET on surgical management, including axillary node staging and sentinel lymph node biopsy; the use of FDG-PET in the evaluation of the primary tumor; the role of FDG-PET in the evaluation of distant metastases both at diagnosis and in the investigation of suspected recurrence; and the ability of FDG-PET to predict treatment response. FDG-PET is not sufficiently sensitive to replace histologic surgical staging of the axilla. Although FDG avidity of the primary tumor has been shown to be an unfavorable indicator, there is insufficient information to recommend its routine use for this indication. FDG-PET is more sensitive than conventional imaging in the detection of metastatic or recurrent disease, but the impact of increased sensitivity on patient care and outcome has not been demonstrated. The data regarding prediction of treatment response are insufficient to reach any conclusion. There are a number of prospective, adequately powered clinical trials currently in progress that should provide more definitive answers regarding the role, if any, of this technique in the management of patients with breast cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".