Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Acad Radiol. 2017 Sep;24(9):1139-1147. doi: 10.1016/j.acra.2017.03.013. Epub 2017 May 11.

Abstract

Rationale and objectives: This study aimed to determine whether mammographic features assessed by radiologists and using computer algorithms are prognostic of occult invasive disease for patients showing ductal carcinoma in situ (DCIS) only in core biopsy.

Materials and methods: In this retrospective study, we analyzed data from 99 subjects with DCIS (74 pure DCIS, 25 DCIS with occult invasion). We developed a computer-vision algorithm capable of extracting 113 features from magnification views in mammograms and combining these features to predict whether a DCIS case will be upstaged to invasive cancer at the time of definitive surgery. In comparison, we also built predictive models based on physician-interpreted features, which included histologic features extracted from biopsy reports and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists. The generalization performance was assessed using leave-one-out cross validation with the receiver operating characteristic curve analysis.

Results: Using the computer-extracted mammographic features, the multivariate classifier was able to distinguish DCIS with occult invasion from pure DCIS, with an area under the curve for receiver operating characteristic equal to 0.70 (95% confidence interval: 0.59-0.81). The physician-interpreted features including histologic features and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists showed mixed results, and only one radiologist's subjective assessment was predictive, with an area under the curve for receiver operating characteristic equal to 0.68 (95% confidence interval: 0.57-0.81).

Conclusions: Predicting upstaging for DCIS based upon mammograms is challenging, and there exists significant interobserver variability among radiologists. However, the proposed computer-extracted mammographic features are promising for the prediction of occult invasion in DCIS.

Keywords: Breast cancer; CAD; digital mammogram; ductal carcinoma in situ; microcalcification.

MeSH terms

  • Algorithms
  • Area Under Curve
  • Breast Neoplasms / diagnostic imaging*
  • Breast Neoplasms / pathology
  • Carcinoma, Ductal, Breast / diagnostic imaging*
  • Carcinoma, Ductal, Breast / pathology
  • Carcinoma, Intraductal, Noninfiltrating / diagnostic imaging*
  • Carcinoma, Intraductal, Noninfiltrating / pathology
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Mammography*
  • Middle Aged
  • Neoplasm Invasiveness
  • Neoplasm Staging
  • Neoplasms, Unknown Primary / diagnostic imaging*
  • Neoplasms, Unknown Primary / pathology
  • Observer Variation
  • Predictive Value of Tests
  • ROC Curve
  • Retrospective Studies