| Title | Application of deep learning in the detection of breast lesions with four different breast densities |
| Authors | Li, Hongmei Ye, Jing Liu, Hao Wang, Yichuan Shi, Binbin Chen, Juan Kong, Aiping Xu, Qing Cai, Junhui |
| Affiliation | Subei Peoples Hosp Jiangsu Prov, Dept Radiol, Yangzhou, Jiangsu, Peoples R China Yizhun Med AI, Beijing, Peoples R China Peking Univ, Sch Elect Engn & Comp Sci, Beijing, Peoples R China |
| Keywords | DIGITAL MAMMOGRAPHY DIAGNOSTIC-ACCURACY FILM MAMMOGRAPHY CANCER PERFORMANCE RISK |
| Issue Date | Jun-2021 |
| Publisher | CANCER MEDICINE |
| Abstract | Objective This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction. Methods A total of 608 mammograms were collected from Northern Jiangsu People's Hospital in Yangzhou City. The data from this province have not been used in the training or evaluation data set. The model consists of three submodules, lesion detection (Mask-rcnn), lesion registration between craniocaudal view and mediolateral oblique view, malignancy prediction network (ResNet). The data set used to train the model was obtained from nine institutions across six cities. For normal cases, there were no annotations. Here, we adopted the free-response receiver operating characteristic (FROC) curve as the indicator to evaluate the detection performance of all cancers and triple-negative breast cancer (TNBC). The FROC curves are also shown for mass/distortion/asymmetry and typical benign calcification in two kinds of populations with four types of breast density. Results The sensitivity to mass/distortion/asymmetry for the four types of breast (A, B, C, D) are 0.94, 0.92, 0.89, and 0.72, respectively, when false positive per image is 0.25, while these values are 1.00, 0.95, 0.92, and 0.90, respectively, for the amorphous calcification lesions. The sensitivity for the cancer is 0.85 at the same false-positive rate. The TNBC accounts for about 10%-20% of all breast cancers and is more aggressive with poor prognosis than other breast cancers. Herein, we also evaluated performance on the TNBC cases. Our results show that Yizhun AI could detect 75% TNBC lesions at the same false-positive level mentioned above. Conclusion The Yizhun AI model used in our work has good diagnostic efficiency for different types of breast, even for the extremely dense breast. It has a guiding role in the clinical diagnosis of breast cancer. The performance of Yizhun AI on mass/distortion/asymmetry is affected by breast density significantly compared to that on amorphous calcification. |
| URI | http://hdl.handle.net/20.500.11897/615562 |
| ISSN | 2045-7634 |
| DOI | 10.1002/cam4.4042 |
| Indexed | SCI(E) |
| Appears in Collections: | 信息科学技术学院 |
