Lyu Shu-Yi, Zhang Yan, Zhang Mei-Wu, Zhang Bai-Song, Gao Li-Bo, Bai Lang-Tao, Wang Jue
Interventional Therapy Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China.
Ultrasonography Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China.
World J Clin Cases. 2022 Jan 14;10(2):518-527. doi: 10.12998/wjcc.v10.i2.518.
The incidence rate of breast cancer has exceeded that of lung cancer, and it has become the most malignant type of cancer in the world. BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.
To explore the diagnostic value of artificial intelligence (AI) automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.
A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital, University of Chinese Academy of Sciences. These nodules were classified by ultrasound doctors and the AI-SONIC breast system. The diagnostic values of conventional ultrasound, the AI automatic detection system, conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.
Among the 107 breast nodules, 61 were benign (57.01%), and 46 were malignant (42.99%). The pathology results were considered the gold standard; furthermore, the sensitivity, specificity, accuracy, Youden index, and positive and negative predictive values were 84.78%, 67.21%, 74.77%, 0.5199, 66.10% and 85.42% for conventional ultrasound BI-RADS classification diagnosis, 86.96%, 75.41%, 80.37%, 0.6237, 72.73%, and 88.46% for automatic AI detection, 80.43%, 90.16%, 85.98%, 0.7059, 86.05%, and 85.94% for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%, 67.21%, 78.50%, 0.6069, 68.25%, and 93.18% for adjusted BI-RADS classification, respectively. The biopsy rate, cancer detection rate and malignancy risk were 100%, 42.99% and 0% and 67.29%, 61.11%, and 1.87% before and after BI-RADS adjustment, respectively.
Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules. Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.
乳腺癌的发病率已超过肺癌，成为全球最常见的恶性肿瘤类型。BI-RADS 4类乳腺结节具有广泛的恶性风险，给临床决策带来挑战。
探讨人工智能（AI）自动检测系统对BI-RADS 4类乳腺结节的诊断价值，评估AI自动检测系统联合传统超声BI-RADS分类能否降低BI-RADS 4类乳腺结节的活检概率。
选取2019年6月至2020年7月在中国科学院大学宁波华美医院经病理确诊的107个BI-RADS类乳腺结节。由超声医生和AI-SONIC乳腺系统对这些结节进行分类。对传统超声、AI自动检测系统、传统超声联合AI自动检测系统及调整后的BI-RADS分类诊断的诊断价值进行统计学分析。
107个乳腺结节中，良性61个（57.01%），恶性46个（42.99%）。病理结果被视为金标准；此外，传统超声BI-RADS分类诊断的灵敏度、特异度、准确度、约登指数、阳性预测值和阴性预测值分别为84.78%、67.21%、74.77%、0.5199、66.10%和85.42%，AI自动检测分别为86.96%、75.41%、80.37%、0.6237、72.73%和88.46%，传统超声BI-RADS分类联合AI自动检测分别为80.43%、90.16%、85.98%、0.7059、86.05%和85.94%，调整后的BI-RADS分类分别为93.48%、67.21%、78.50%、0.6069、68.25%和93.18%。BI-RADS调整前后的活检率、癌症检出率和恶性风险分别为100%、42.99%、0%和67.29%、61.11%、1.87%。
AI自动检测在判断BI-RADS 4类乳腺结节的良恶性方面具有较高的准确性。传统超声BI-RADS分类联合AI自动检测可降低BI-RADS 4类乳腺结节的活检率。
