Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging

Quantitative Imaging in Medicine and Surgery

article

Meng Zhu¹  Yong Pi²  Zekun Jiang³  Yanyan Wu⁴  Hong Bu⁵  Ji Bao⁵  Yujuan Chen⁶  Lijun Zhao⁷  Yulan Peng¹ 

[1] Department of Ultrasound , West China Hospital of Sichuan University;Machine Intelligence Laboratory, College of Computer Science , Sichuan University;West China Biomedical Big Data Center , West China Hospital of Sichuan University;Department of Ultrasound , Sichuan Provincial People’s Hospital;Laboratory of Pathology, West China Hospital , Sichuan University;Department of Breast Surgery , West China Hospital of Sichuan University;Department of Ultrasound, Chengdu Women’s and Children’s Central Hospital, School of Medicine , University of Electronic Science and Technology of China

关键词: Breast;    ductal carcinoma in situ (DCIS);    microinvasion;    ultrasound image;    deep learning;   

DOI  :  10.21037/qims-22-46

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images. Methods: The clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data. Results: The characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER−; P=0.003), progesterone receptor negative (PR−; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765. Conclusions: Deep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.

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