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Open Access Research article

Predicting invasive breast cancer versus DCIS in different age groups

Mehmet US Ayvaci1, Oguzhan Alagoz2, Jagpreet Chhatwal3, Alejandro Munoz del Rio4, Edward A Sickles5, Houssam Nassif6, Karla Kerlikowske7 and Elizabeth S Burnside24*

Author Affiliations

1 Information Systems and Operations Management, University of Texas at Dallas, 800 W Campbell Rd, SM 33, Richardson, TX 75080-3021, USA

2 Industrial & Systems Engineering, University of Wisconsin, 1513 University Avenue, Madison, WI 53706, USA

3 Department of Health Services Research, MD Anderson Cancer Center at University of Texas, 1400 Pressler Street, Unit 1444, Houston, TX 77098, USA

4 Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI 53792-3252, USA

5 Department of Radiology, University of California, San Francisco, CA 94143, USA

6 Department of Computer Science, University of Wisconsin, Madison, WI 53706, USA

7 Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA 94143, USA

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BMC Cancer 2014, 14:584  doi:10.1186/1471-2407-14-584

Published: 11 August 2014

Abstract

Background

Increasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.

Methods

We analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50–64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).

Results

The models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group—mass margins, and in the younger group—mass size were positive predictors of invasive cancer.

Conclusions

Clinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age.

Keywords:
Mammography; Logistic models; Breast neoplasms; Overdiagnosis; Biopsy; Aging