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

Publication ,  Journal Article
Shi, B; Grimm, LJ; Mazurowski, MA; Baker, JA; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY
Published in: Acad Radiol
September 2017

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.

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Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

September 2017

Volume

24

Issue

9

Start / End Page

1139 / 1147

Location

United States

Related Subject Headings

  • Retrospective Studies
  • ROC Curve
  • Predictive Value of Tests
  • Observer Variation
  • Nuclear Medicine & Medical Imaging
  • Neoplasms, Unknown Primary
  • Neoplasm Staging
  • Neoplasm Invasiveness
  • Middle Aged
  • Mammography
 

Citation

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Shi, B., Grimm, L. J., Mazurowski, M. A., Baker, J. A., Marks, J. R., King, L. M., … Lo, J. Y. (2017). Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol, 24(9), 1139–1147. https://doi.org/10.1016/j.acra.2017.03.013
Shi, Bibo, Lars J. Grimm, Maciej A. Mazurowski, Jay A. Baker, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, E Shelley Hwang, and Joseph Y. Lo. “Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?Acad Radiol 24, no. 9 (September 2017): 1139–47. https://doi.org/10.1016/j.acra.2017.03.013.
Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, et al. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol. 2017 Sep;24(9):1139–47.
Shi, Bibo, et al. “Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?Acad Radiol, vol. 24, no. 9, Sept. 2017, pp. 1139–47. Pubmed, doi:10.1016/j.acra.2017.03.013.
Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol. 2017 Sep;24(9):1139–1147.
Journal cover image

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

September 2017

Volume

24

Issue

9

Start / End Page

1139 / 1147

Location

United States

Related Subject Headings

  • Retrospective Studies
  • ROC Curve
  • Predictive Value of Tests
  • Observer Variation
  • Nuclear Medicine & Medical Imaging
  • Neoplasms, Unknown Primary
  • Neoplasm Staging
  • Neoplasm Invasiveness
  • Middle Aged
  • Mammography