Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches

  • Jiao Li
  • Yan Song
  • Shuoyu Xu
  • Jinhua Wang
  • Huabin Huang
  • Weimei Ma
  • Xinhua Jiang
  • Yaopan Wu
  • Hongming Cai
  • Li LiEmail author
Original Article



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.


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.


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.


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.


Mammography Microcalcification Ductal carcinoma in situ Radiomics Machine learning 



This study was funded by the Science and Technology Planning Project of Guangdong Province, China (Nos. 2016B090918066, 201807010057), the Science and Technology Program of Guangzhou, China (No. 201704020060), the Health and Medical Collaborative Innovation Project of Guangzhou City (No. 201604020003), the National Natural Science Foundation of China (No. 61372141), Science and Technology Planning Project of Guangdong Province (No. 2016A010101013), and the Fundamental Research Fund for the Central Universities (No. 2017ZD051).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

Informed consent

Informed consent was obtained from all individuals included in the study.

Supplementary material

11548_2018_1900_MOESM1_ESM.pdf (68 kb)
Supplementary material 1 (PDF 68 kb)


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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Department of Medical Imaging, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South ChinaGuangzhouPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  3. 3.Department of Medical Imaging CenterShenzhen Hospital of Southern Medical UniversityShenzhenPeople’s Republic of China

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