Healthc Inform Res.  2014 Apr;20(2):152-156. 10.4258/hir.2014.20.2.152.

Development and Validation of Web-Based Nomograms to Predict Postoperative Invasive Component in Ductal Carcinoma in Situ at Needle Breast Biopsy

• Lee SC
• Chang MC

• KMID: 2284575
• DOI: http://doi.org/10.4258/hir.2014.20.2.152

Abstract

OBJECTIVES
Although sonography-guided core needle biopsy is a highly targeted method, there is a possibility of an invasive component after surgical excision of ductal carcinoma in situ (DCIS) of the breast. This study was performed to develop and validate nomograms to predict the postoperative invasive component in DCIS at core needle biopsy.
METHODS
Two nomograms were developed using the data of previous meta-analysis and multivariate analysis. Nomograms were validated externally using the data of the authors' affiliation. The accuracy was validated by the expected-to-observed ratio and the Hosmer-Lemeshow goodness-of-fit test. Discrimination was validated by the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis.
RESULTS
The nomogram using the meta-analysis study data was developed at http://dcis-m.surgery.kr.pe/, and the nomogram using the multivariate analysis study data was developed at http://dcis-k.surgery.kr.pe/. The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram using multivariate analysis data (p = 0.131) was better calibrated than that using meta-analysis data (p < 0.001). ROC curve analysis showed statistically significant power of discrimination in both nomograms (AUC = 0.776, 0.751).
CONCLUSIONS
Both nomograms showed statistically significant discriminatory power, but the nomogram using the data of multivariate analysis was simpler and more reliable. These would be useful for the prediction of invasive cancer and the need for sentinel node biopsy in DCIS at core needle biopsy.

Keyword

Nomograms; Internet; Breast; Biopsy

MeSH Terms

Biopsy*
Biopsy, Large-Core Needle
Breast*
Carcinoma, Intraductal, Noninfiltrating*
Discrimination (Psychology)
Internet
Multivariate Analysis
Needles*
Nomograms*
ROC Curve

Figure

• 

  Figure 1 Hosmer-Lemeshow goodness-of-fit test of nomogram from the meta-analysis data (p < 0.001). Expected number of invasive cancer patients, observed number of invasive cancer patients, and the corresponding number of total patients per decile are shown.

• 

  Figure 2 Hosmer-Lemeshow goodness-of-fit test of nomogram from the multivariate analysis data (p = 0.131). Expected number of invasive cancer patients, observed number of invasive cancer patients and the corresponding number of total patients per decile are shown.

• 

  Figure 3 Receiver operating characteristic curves of nomograms from the meta-analysis data and multivariate analysis data. The area under curve is 0.766 (p < 0.001) for the meta-analysis data and 0.751 (p = 0.001) for the multivariate analysis data. The curves show no differences (p = 0.614).

Reference

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