To investigate the value of MammoWorks™ system based on deep learning technology in breast tumor detection.

We enrolled 448 patients with breast lesions at X-ray BI-RADS grade 5-6 in Shaanxi Provincial Tumor Hospital from January 2015 to April 2017. All patients underwent operation, with complete clinical and pathological data. Additionally, using a random number table, 215 healthy people who had physical examination in our hospital at the same period (X-ray BI-RADS grade 1) were randomly enrolled as control and all of them had no breast diseases in the two-year follow-up. The mammographic data of all subjects were analyzed by MammoWorks™ system. With the pathological results of patients and the 2-year follow-up results of healthy people as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio of MammoWorks™ system in the detection of breast tumors were analyzed, as well as the number of false-positive marks in one mammograph. The rate comparison was performed using χ² test and Fisher exact probability method. The number of false-positive marks in one mammograph was compared using nonparametric test (Kruskal-Wallis test) and Kappa test was used to evaluate the consistency of the results between different subgroups.

Totally 2 652 X-ray photographs from 663 females were analyzed. A total of 2 284 lesions were marked, including 929 true-positive and 1 355 false-positive. There were 333 cases of true-positive, 126 true-negative, 89 false-positive and 115 false-negative. The sensitivity of MammoWorks™ system was 74.3%(333/448), specificity 58.6%(126/215), positive predictive value 78.9%(333/422), negative predictive value 52.3%(126/241), accuracy 69.2%(459/663), positive likelihood ratio 1.80 and negative likelihood ratio 0.44. The number of false-positive marks in one mammograph was 0.50(0.00~0.75). The sensitivity of MammoWorks™ system showed a significant difference between craniocaudal (CC) view and mediolateral oblique (MLO) view of X-ray (Kappa=0.278, P<0.001). The detection efficiency of MammoWorks™ system presented no significant difference in patients with different age, BI-RADS grade, tumor location, pathological stage, pathological type and molecular type (χ²=3.341, 1.056, 7.103, 8.911, 5.170, 7.803, P>0.050), while the detection efficiency of MammoWorks™ system was significantly different in patients with different breast density, lesion type, and tumor diameter (χ²=7.985, 15.543, 18.652, P<0.050). The number of false-positive marks in one mammograph presented a significant difference in patients with different breast density (χ²=15.024, P<0.050).

Based on deep learning technology, the MammoWorks™ system is helpful in the auxiliary diagnosis of breast tumors, but its detection efficiency still needs to be improved.

Breast neoplasms; Artificial intelligence; Deep learning