Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial

Objectives
Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to reduce the workload of radiologists in the context of dismissing normal breast scans and dismissing benign disease in women with extremely dense breasts. The aim of this study was to validate the potential of integrating CAT and CAD to reduce workload and workup on benign lesions in the second screening round of the DENSE trial, without missing cancer.

Methods
We included 2901 breast magnetic resonance imaging scans, obtained from 8 hospitals in the Netherlands. Computer-aided triaging and CAD were previously developed on data from the first screening round. Computer-aided triaging dismissed examinations without lesions. Magnetic resonance imaging examinations triaged to radiological reading were counted and subsequently processed by CAD. The number of benign lesions correctly classified by CAD was recorded. The false-positive fraction of the CAD was compared with that of unassisted radiological reading in the second screening round. Receiver operating characteristics (ROC) analysis was performed and the generalizability of CAT and CAD was assessed by comparing results from first and second screening rounds.

Results
Computer-aided triaging dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent CAD classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively (P = 0.001), than radiological reading alone. Computer-aided triaging had a smaller area under the ROC curve in the second screening round compared with the first, 0.83 versus 0.76 (P = 0.001), but this did not affect the negative predictive value at the 100% sensitivity operating threshold. Computer-aided diagnosis was not associated with significant differences in area under the ROC curve (0.857 vs 0.753, P = 0.08). At the operating thresholds, the specificities of CAT (39.7% vs 41.0%, P = 0.70) and CAD (41.0% vs 38.2%, P = 0.62) were successfully reproduced in the second round.

Conclusion
The combined application of CAT and CAD showed potential to reduce workload of radiologists and to reduce number of biopsies on benign lesions. Computer-aided triaging (CAT) correctly dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent computer-aided diagnosis (CAD) classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively (P = 0.001), than radiological reading alone.

Contrast-enhanced magnetic resonance imaging (MRI) may be used in combination with x-ray mammography to screen asymptomatic women for breast cancer. Supplemental MRI screening in women with extremely dense breasts improved the detection of cancer.1 Similar observations were reported for women at increased lifetime risk. Nonetheless, breast MRI screening has lower specificity compared with mammography1–3 and it invokes additional workload.

To reduce the workload of breast magnetic resonance (MR) radiologists, researchers have focused on automated lesion detection.4,5 One focused on identifying normal scans using computer-aided triaging (CAT).6 Computer-aided diagnosis (CAD) of dynamic contrast-enhanced MRI7,8 and multiparametric MRI1,9 was found to further increase specificity.10–14

A recently reported CAT—developed on data from 4783 MRI examinations from the first screening round of the DENSE trial—dismissed approximately 40% of normal breast examinations without dismissing malignant disease.6 In addition to CAT, CAD was developed on the same data to distinguish between 444 benign and 81 malignant lesions. It is yet unknown whether CAD is complementary to CAT to increase the positive predictive value (PPV) of MRI screening in women with extremely dense breasts while maintaining high negative predictive value (NPV) and minimizing the number of normal scans to be read by radiologists.

The aim of this study was to validate the potential of combining CAT with CAD in the second screening round of DENSE to minimize work load as well as minimizing the number of biopsies on benign lesions without dismissing malignant breast disease.