Prospective Study of 2-[<sup>18</sup>F]Fluorodeoxyglucose Positron Emission Tomography in the Assessment of Regional Nodal Spread of Disease in Patients With Breast Cancer: An Ontario Clinical Oncology Group Study
Bibliographic record
Abstract
PURPOSE: 2-[(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) is potentially useful in assessing lymph nodes and detecting distant metastases in women with primary breast cancer. PATIENTS AND METHODS: Women diagnosed with operable breast cancer within 3 months underwent FDG-PET at one of five Ontario study centers followed by axillary lymph node assessment (ALNA) consisting of sentinel lymph node biopsy (SLNB) alone if sentinel lymph nodes (SLNs) were negative, SLNB with axillary lymph node dissection (ALND) if SLNB or PET was positive, or ALND alone if SLNs were not identified. RESULTS: Between January 2005 and March 2007, 325 analyzable women entered this study. Sentinel nodes were found for 312 (96%) of 325 women and were positive for tumor in 90 (29%) of 312. ALND was positive in seven additional women. Using ALNA as the gold standard, sensitivity for PET was 23.7% (95% CI, 15.9% to 33.6%), specificity was 99.6% (95% CI, 97.2% to 99.9%), positive predictive value was 95.8% (95% CI, 76.9% to 99.8%), negative predictive value was 75.4% (95% CI, 70.1% to 80.1%), and prevalence was 29.8% (95% CI, 25.0% to 35.2%). Using logistic regression, tumor size was predictive for prevalence of tumor in the axilla and for PET sensitivity. PET scan was suspicious for distant metastases in 13 patients; three (0.9%) were confirmed as metastatic disease and 10 (3.0%) were false positive. CONCLUSION: FDG-PET is not sufficiently sensitive to detect positive axillary lymph nodes, nor is it sufficiently specific to appropriately identify distant metastases. However, the very high positive predictive value (96%) suggests that PET when positive is indicative of disease in axillary nodes, which may influence surgical care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".