Detection of Architectural Distortion in Prior Mammograms Using Statistical Measures of Angular Spread
Bibliographic record
Abstract
Architectural distortion, a distortion of the architecture of breast parenchyma without being accompanied by increased density or a mass, is a mammographic sign of breast cancer. Architectural distortion is an important finding in the detection of early stages of breast cancer. However, subtlety and variability in appearance, and similarity in presentation to normal breast tissue patterns overlapped in the projected mammographic image impose challenges in the detection of architectural distortion. Architectural distortion is the most commonly missed abnormality in false-negative (FN) screening cases. Several studies have indicated that architectural distortion accounts for 12% to 45% of breast cancer cases overlooked or misinterpreted in screening mammography. In terms of treatment of patients affected by breast cancer, only localized and nonmetastasized cancers are considered to be treatable and curable. In order to increase the possibility of survival, the detection of breast cancer at its early stages is of highest importance. The use of computer-aided diagnosis (CAD) techniques by a radiologist could be as effective as double reading, and provide efficient and effective means of reducing errors and help in increasing sensitivity in the detection of breast cancer. Numerous CAD techniques and systems have been proposed and developed to improve the sensitivity and accuracy of the detection of breast cancer. Several CAD techniques are found to be effective in detecting masses and calcifications; unfortunately, the same systems have demonstrated poor performance in the detection of subtle or indirect signs of possible malignancy, such as architectural distortion. Several studies have indicated that a substantial portion of prior mammograms of cases of screen-detected cancer or interval-cancer cases could contain subtle or minimal signs of abnormality. Such signs of abnormality include hard-to-detect features or patterns that could indicate breast cancer at stages prior to the formation of a mass or tumor. Architectural distortion could appear at the initial stages of the formation of a breast mass or tumor, and has been found to be associated with breast malignancy in one-half to two-thirds of the cases in which it is present. Increasing the sensitivity and accuracy in the detection of architectural distortion could lead to improvement in the prognosis of patients affected by breast cancer and help in increasing the associated survival rate.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".