Jiao Li1, Yan Song2, Shuoyu Xu1, Jinhua Wang3, Huabin Huang1, Weimei Ma1, Xinhua Jiang1, Yaopan Wu1, Hongming Cai2, Li Li4. 1. Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China. 2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, People's Republic of China. 3. Department of Medical Imaging Center, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People's Republic of China. 4. Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China. li2@mail.sysu.edu.cn.
Abstract
PURPOSE: We aimed to investigate the feasibility of predicting invasion carcinoma from ductal carcinoma in situ (DCIS) lesions diagnosed by preoperative core needle biopsy using radiomics signatures, clinical imaging characteristics, and breast imaging reporting and data system (BI-RADS) descriptors on mammography. METHODS: Retrospectively, we enrolled 362 DCIS patients diagnosed by core needle biopsy, 110 (30.4%) of which had invasive carcinoma confirmed by operation and pathology. We analyzed the images identified suspicious calcification from 250 subjects (161 pure DCIS and 89 DCIS with invasion). A total of 569 calcification radiomics signatures were extracted from microcalcification for each patient. We included a group of routine clinical imaging characteristics and BI-RADS descriptors for comparison purpose. Five feature selection and seven classification methods were evaluated in terms of their prediction performance. We compared the area under the receiver operating characteristic curve (AUC) averaged from tenfold cross-validation of different feature sets to identify the best combination of feature selection and classification methods. RESULTS: Optimal feature selection and classification methods were identified after evaluating various combinations of feature sets. The best performance was achieved using both radiomics and clinical imaging characteristics (AUC = 0.72) performing better than BI-RADS descriptors or radiomics, but was no significant difference with clinical imaging characteristics (AUC = 0.66). The most significant features found were morphology signatures, first-order statistics, asymmetry/mass prevalence, and nuclear grade. CONCLUSIONS: We found that the prediction model established using microcalcifications radiomics signatures and clinical imaging characteristics has the potential to identify an understaging of invasive breast cancer.
PURPOSE: We aimed to investigate the feasibility of predicting invasion carcinoma from ductal carcinoma in situ (DCIS) lesions diagnosed by preoperative core needle biopsy using radiomics signatures, clinical imaging characteristics, and breast imaging reporting and data system (BI-RADS) descriptors on mammography. METHODS: Retrospectively, we enrolled 362 DCIS patients diagnosed by core needle biopsy, 110 (30.4%) of which had invasive carcinoma confirmed by operation and pathology. We analyzed the images identified suspicious calcification from 250 subjects (161 pure DCIS and 89 DCIS with invasion). A total of 569 calcification radiomics signatures were extracted from microcalcification for each patient. We included a group of routine clinical imaging characteristics and BI-RADS descriptors for comparison purpose. Five feature selection and seven classification methods were evaluated in terms of their prediction performance. We compared the area under the receiver operating characteristic curve (AUC) averaged from tenfold cross-validation of different feature sets to identify the best combination of feature selection and classification methods. RESULTS: Optimal feature selection and classification methods were identified after evaluating various combinations of feature sets. The best performance was achieved using both radiomics and clinical imaging characteristics (AUC = 0.72) performing better than BI-RADS descriptors or radiomics, but was no significant difference with clinical imaging characteristics (AUC = 0.66). The most significant features found were morphology signatures, first-order statistics, asymmetry/mass prevalence, and nuclear grade. CONCLUSIONS: We found that the prediction model established using microcalcifications radiomics signatures and clinical imaging characteristics has the potential to identify an understaging of invasive breast cancer.
Entities:
Keywords:
Ductal carcinoma in situ; Machine learning; Mammography; Microcalcification; Radiomics
Authors: Ko Woon Park; Seon Woo Kim; Heewon Han; Minsu Park; Boo-Kyung Han; Eun Young Ko; Ji Soo Choi; Eun Yoon Cho; Soo Youn Cho; Eun Sook Ko Journal: NPJ Breast Cancer Date: 2022-01-14