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

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.

Figure 1

Flowchart of the proposed methodology.

Figure 2

Result of mammogram enhancement: (a) the original digital magnification view; (b) enhanced image by contrast-limited adaptive histogram equalization and dual-structural element based morphology approach; (c) final enhanced image after applying top-hat transform.

Figure 3

Example result of segmentation of individual MCs and detection of cluster boundary: (a) DCIS ROI mask delineated by radiologist; (b) segmented by the algorithm.

Figure 4

The AUC-ROC performance of individual computer vision features. The red line indicates chance behavior for AUC-ROC being 0.5. The feature groups are cluster level (black) and summary statistics of individual calcification features: mean (green), standard deviation (blue), minimum (yellow), and maximum (cyan).

Figure 5

ROC curves showing the classification performance using different features. The ROC curve for Radiologist-B’s subjective assessment achieved AUC-ROC of 0.55 (95% CI: 0.41–0.68) and showed no statistically significant difference from random chance and was thus not plotted.

Figure 6

The histogram of feature selection frequency using computer vision features and SFFS across the cross validation. These feature groups are cluster level (black) and summary statistics of individual calcification features: mean (green), standard deviation (blue), minimum (yellow), and maximum (cyan).