Nuclear Medicine in the Imaging and Management of Breast Cancer
Bibliographic record
Abstract
Breast cancer is the most common cancer in women worldwide. Mammography is the main imaging modality used for the detection of breast cancer. Other modalities, including those encountered in nuclear medicine, provide added value in breast cancer imaging. 18F-fluorodeoxyglucose (18F-FDG) PET/CT imaging is a combined functional and anatomic modality used in the evaluation of cancer. This modality plays an expanding role in detection and staging of breast cancer and evaluation of recurrence. Bone scan imaging is readily available, and has played a valuable role in the management of breast cancer, specifically in the evaluation of osseous metastases. Sentinel node lymphoscintigraphy is based on the concept that the sentinel node is the first lymph node to potentially harbor breast metastasis. This technique relies on lymphoscintigraphic mapping with colloids such as Technetium-99m (99mTc)-filtered sulfur colloid, and its use can prevent the need for axillary nodal dissection in women with negative sentinel node disease. Scintimammography, which uses the radioactive tracer 99mTc Sestamibi, can be useful in patients with very dense breasts, those with palpable abnormalities not apparent on other imaging modalities, and to assess for multifocal breast cancer. This article provides an evidence-based discussion and a variety of case examples, stressing the important role of nuclear medicine imaging in the management of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".