Diagnostic accuracy of commercial system for computer-assisted detection (CADx) as an adjunct to interpretation of mammograms.
Bibliographic record
Abstract
PURPOSE: To evaluate the diagnostic accuracy of the commercial computer-aided detection CADx system for the reading of mammograms. MATERIALS AND METHODS: The study assessed the Second Look system developed and marketed by CADx Medical Systems, Montreal, Canada. The diagnostic sensitivity was evaluated by means of a retrospective study on 98 consecutive cancers detected at screening by double independent reading. The specificity and the positive predictive value (PPV) for cancer of the CADx system were prospectively evaluated on a second group of 560 consecutive mammograms of asymptomatic women not included in screening program. The radiologist who was present during the test assessed the abnormal mammographic findings by one or more of the following diagnostic procedures: physical examination, additional mammographic detail views with or without magnification, ultrasonography, ultrasound- or mammography-guided fine needle aspiration cytology, and core-biopsy. The exams first underwent conventional reading and then a second reading carried out with the aid of the CADx system. RESULTS: The overall diagnostic sensitivity of the CADx system on the 98 screening cancers was 81.6%; in particular it was 89.3% for calcifications, 83.9% for masses and only 37.5% for architectural distortion. The CADx markings for each mammography were 4.7 on average. Identification of invasive carcinoma was independent from tumour size. In the second group of 560 mammograms, the CADx system marked all cases identified as positive by conventional reading and confirmed by biopsy (7/7), but did not permit the detection of any additional cancer. The CADx markings per exam were 4.2 on average, the specificity was 13.7% and the PPV was 0.55% versus 13.7% recall rate of conventional reading. CADx reading led to a 1.96% (11/560) increase of the women necessitating further diagnostic investigation. CONCLUSIONS: The results of our study show that the diagnostic sensitivity of the CADx system is lower than that obtained by double independent reading at screening. Used in association with conventional reading of mammograms of asymptomatic women the CADx system did not increase diagnostic sensitivity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".