Zhang Jianxing, Tao Xing, Jiang Yanhui, Wu Xiaoxi, Yan Dan, Xue Wen, Zhuang Shulian, Chen Ling, Luo Liangping, Ni Dong
Department of Medical Imaging Center, The First Affiliated Hospital, Jinan University, Guangzhou, China.
Department of Ultrasound, Remote Consultation Center of ABUS, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Oncol. 2022 Jul 4;12:938413. doi: 10.3389/fonc.2022.938413. eCollection 2022.
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.
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.
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).
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.
本研究旨在评估一种基于ABUS图像和Yolo v5开发的用于多中心ABUS图像数据乳腺病变检测的卷积神经网络算法。
分析了2016年10月至2020年12月期间从7家医院招募的741例ABUS检查的2538份容积数据。将413例患者的452份容积数据用作内部验证数据，328例患者的2086份容积数据用作外部验证数据。413例患者中有1178个乳腺病变（其中161个为恶性，1017个为良性），328例患者中有1936个病变（57个为恶性，1879个为良性）。分析了该算法在检测具有不同允许假阳性值和病变大小的病变时的效率和准确性，并对差异进行了比较和分析，包括内部验证和外部验证数据中的各项指标。
研究发现，即使使用内部或外部验证数据，该算法对所有类别的病变都具有较高的敏感性。该算法在内部和外部验证集中的总体检出率分别高达78.1%和71.2%。随着结节大小的增加，该算法能够检测到更多的病变（≥10mm病变的检出率为87.4%，但<10mm病变的检出率不到50%）。BI-RADS 4/5类病变的检出率高于BI-RADS 3或2类病变（内部分别为96.5%对79.7%对74.7%，外部分别为95.8%对74.7%对88.4%）。此外，恶性结节的检测性能优于良性结节（内部为98.1%对74.9%，外部为98.2%对70.4%）。
该算法在内部和外部验证集中均显示出良好的检测效率，尤其是对于4/5类病变和恶性病变。然而，在检测2类和3类病变以及小于10mm的病变方面仍存在一些不足。
