Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Jianxing Zhang; Xing Tao; Yanhui Jiang; Xiaoxi Wu; Dan Yan; Wen Xue; Shulian Zhuang; Ling Chen; Liangping Luo; Dong Ni

doi:10.3389/fonc.2022.938413

Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Front Oncol. 2022 Jul 4:12:938413. doi: 10.3389/fonc.2022.938413. eCollection 2022.

Authors

Jianxing Zhang^{1

2}, Xing Tao³, Yanhui Jiang³, Xiaoxi Wu², Dan Yan², Wen Xue², Shulian Zhuang², Ling Chen², Liangping Luo¹, Dong Ni³

Affiliations

¹ Department of Medical Imaging Center, The First Affiliated Hospital, Jinan University, Guangzhou, China.
² Department of Ultrasound, Remote Consultation Center of ABUS, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
³ Medical Ultrasound Image Computing Lab, Shenzhen University, Shenzhen, China.

Abstract

Objective: This study aimed to evaluate a convolution neural network algorithm for breast lesion detection with multi-center ABUS image data developed based on ABUS image and Yolo v5.

Methods: A total of 741 cases with 2,538 volume data of ABUS examinations were analyzed, which were recruited from 7 hospitals between October 2016 and December 2020. A total of 452 volume data of 413 cases were used as internal validation data, and 2,086 volume data from 328 cases were used as external validation data. There were 1,178 breast lesions in 413 patients (161 malignant and 1,017 benign) and 1,936 lesions in 328 patients (57 malignant and 1,879 benign). The efficiency and accuracy of the algorithm were analyzed in detecting lesions with different allowable false positive values and lesion sizes, and the differences were compared and analyzed, which included the various indicators in internal validation and external validation data.

Results: The study found that the algorithm had high sensitivity for all categories of lesions, even when using internal or external validation data. The overall detection rate of the algorithm was as high as 78.1 and 71.2% in the internal and external validation sets, respectively. The algorithm could detect more lesions with increasing nodule size (87.4% in ≥10 mm lesions but less than 50% in <10 mm). The detection rate of BI-RADS 4/5 lesions was higher than that of BI-RADS 3 or 2 (96.5% vs 79.7% vs 74.7% internal, 95.8% vs 74.7% vs 88.4% external). Furthermore, the detection performance was better for malignant nodules than benign (98.1% vs 74.9% internal, 98.2% vs 70.4% external).

Conclusions: This algorithm showed good detection efficiency in the internal and external validation sets, especially for category 4/5 lesions and malignant lesions. However, there are still some deficiencies in detecting category 2 and 3 lesions and lesions smaller than 10 mm.

Keywords: automatic breast ultrasound (ABUS); breast cancer; convolution neural network; detection; validation data.