Missed Breast Cancer: Effects of Subconscious Bias and Lesion Characteristics
Bibliographic record
Abstract
Medical errors are a substantial cause of morbidity and mortality and the third leading cause of death in the United States. Errors resulting in missed breast cancer are the most common reason for medical malpractice lawsuits against all physicians. Missed breast cancers are breast malignancies that are detectable at retrospective review of a previously obtained mammogram that was prospectively reported as showing negative, benign, or probably benign findings. Investigators in prior studies have found that up to 35% of both interval cancers and screen-detected cancers could be classified as missed. As such, in conjunction with having awareness of the most common misleading appearances of breast cancer, it is important to understand the cognitive processes and unconscious biases that can impact image interpretation, thereby helping to decrease the number of missed breast cancers. The various cognitive processes that lead to unconscious bias in breast imaging, such as satisfaction of search, inattention blindness, hindsight, anchoring, premature closing, and satisfaction of reporting, are outlined in this pictorial review of missed breast cancers. In addition, strategies for reducing the rates of these missed cancers are highlighted. The most commonly missed and misinterpreted lesions, including stable lesions, benign-appearing masses, one-view findings, developing asymmetries, subtle calcifications, and architectural distortion, also are reviewed. This information will help illustrate why and how breast cancers are missed and aid in the development of appropriate minimization strategies in breast imaging. ©RSNA, 2020
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".