Magnetic resonance spectroscopy and imaging in breast cancer prognosis and diagnosis
Bibliographic record
Abstract
Breast cancer (BC) has the highest occurrence and mortality of all cancers that affect women with more than one million new cases each year across the globe. BC accounts for about one-quarter of all cancer-related deaths. Even though breast cancer is an aggressive and fatal disease, early detection and treatment can result in increased survival in more than three-quarters of diagnosed patients. In general, traditional diagnostic methods, such as ultrasonography and mammography, considerably increase t survival rates due to early disease detection. Although these traditional methods are useful, new strategies for early detection of breast cancer would likely reduce breast cancer mortality rates. Additional diagnostic imaging modalities, such as Computer omography (CT), Positron Emission Tomography (PET), and other types of scintigraphy techniques, have been used to identify the primary source of the cancer in metastatic cases, but none of these techniques is yet in routine clinical use. Among other imaging methodologies, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy (MRS) and Nuclear Magnetic Resonance (NMR) approaches are powerful tools for uncovering cancer biomarkers. In this review, we consider the current capabilities of magnetic resonance techniques in breast cancer research and highlight some milestones that are necessary to move early detection of breast cancer using such approaches into mainstream health care modalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".