Technology as a force for improved diagnosis and treatment of breast disease.
Bibliographic record
Abstract
Increasing numbers of women are seeking evaluation of screen-detected breast abnormalities, and more women with breast cancer are living with the consequences of treatment. Improved technologies have helped to individualize diagnostic evaluation and treatment, improve efficacy and minimize morbidity. This article highlights some of these technologies. Superior imaging techniques have improved breast cancer screening and show promise for intraoperative surgical guidance and postoperative specimen evaluation. Digital mammography improves the sensitivity of mammography for women younger than 50 years with dense breasts, and tomosynthesis may improve specificity. Magnetic resonance imaging provides sensitive delineation of the extent of the disease and superior screening for women with a greater than 25% lifetime risk of breast cancer Minimally invasive techniques have been developed for the assessment of intraductal lesions, biopsy of imaging abnormalities, staging of the axilla and breast radiotherapy. Ductoscopy facilitates intraductal biopsy and localization of lesions for excision, sentinel lymph node biopsy is becoming standard for axillary staging, and intraoperative radiotherapy has the potential to reduce treatment time and morbidity. Three-dimensional imaging allows correlation of final histology with preoperative imaging for superior margin assessment. Related techniques show promise for translation to the intraoperative setting for surgical guidance. New classifications of breast cancers based on gene expression, rather than morphology, describe subtypes with different prognoses and treatment implications, and new targeted therapies are emerging. Genetic fingerprints that predict treatment response and outcomes are being developed to assign targeted treatments to individual patients likely to benefit. Surgeons play a vital role in the successful integration of new technologies into practice.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".