The Value of FDG Positron Emission Tomography in the Management of Patients with Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing experience with positron emission tomography (PET) scanning in breast cancer patients is revealing a significant role for this imaging modality. This report summarizes the experience of 2-[F18]fluoro-2-deoxy-D-glucose (FDG) PET scanning in 165 breast cancer patients from the BC Cancer Agency, British Columbia, Canada, and reviews the literature on this topic. Using the database at PETSCAN Vancouver, we identified imaged patients with a diagnosis of breast cancer. We then conducted a retrospective review of these patients' BC Cancer Agency charts to extract demographic and follow-up information. Between November 2000 and March 2003 we identified 165 patients with histologically confirmed breast cancer who had undergone PET scanning, were registered at the BC Cancer Agency, and had follow-up information. The median patient age was 52 years. The sensitivity of PET in detecting axillary metastases was 28%, and the specificity was 86%. At diagnosis, 5% of patients were diagnosed with distant metastases. In patients undergoing PET scanning because of suspected recurrence, the sensitivity and specificity for detecting recurrence were 89% and 88%, respectively. Distant metastases were demonstrated in 30% of patients who were thought only to have local-regional recurrence. The results suggest that there are two clinical situations in which PET appears to be particularly valuable. The first is in the evaluation of patients who are suspected of having a tumor recurrence. The other is in identifying patients with multifocal or distant sites of malignancy who otherwise appear to have an isolated, potentially curable, local-regional recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it