Background: Computer-aided diagnosis (CAD) system can provide reference to radiologists in breast mass classification. This study was to verify if a CAD model, based on perceptive features learned from quantitative BI-RADS descriptions, can help radiologists improve diagnosis performance for breast masses in mammography.
Methods: A retrospective multi-reader multi-case (MRMC) study was conducted to evaluate a CAD model established on perceptive features. Digital mammograms of 416 patients with breast masses were collected from 2014 to 2017, including 231 benign and 185 malignant masses. Altogether, 214 of them (109 benign, 105 malignant) were selected randomly to train the CAD model which consisted of perceptive feature extractor and classifier. The other 202 patients were used as the test set for evaluation from which 51 patients (29 benign and 22 malignant) were selected. Six radiologists were divided into three groups (junior, middle-senior, and senior).They evaluated 51 patients without and with support from the CAD model. BI-RADS category, benign or malignant diagnosis, probability of malignancy, and diagnosis time were recorded during two evaluation sessions.
Results: In the MRMC evaluation, the average AUC of six radiologists with CAD support was significantly higher than that without support (0.896 vs. 0.850, p=0.02). Both of average sensitivity and average specificity increased (p = 0.0253). More cases were assessed as BI-RADS 4 than BI-RADS 2 or 3. Five radiologists showed comparable diagnosis time per case with and without CAD support, and one radiologist showed a significant decrease when the CAD model was involved.
Conclusion: The CAD model could improve radiologists’ diagnostic performance for breast masses without improving the diagnosis time.