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.
Introduction
Ductal carcinoma in situ (DCIS) is a preinvasive tumor confined within the ducts of the mammary glands and lies along the breast cancer continuum between atypical ductal hyperplasia and invasive ductal carcinoma. The incidence of DCIS has increased substantially since the introduction of mammographic screening, with over 60,000 women in the United States diagnosed with DCIS every year, representing approximately 20% of all new breast neoplasm diagnoses . However, despite the increased incidence of DCIS, there has not been a concomitant decrease in invasive breast cancer . Since the risk of progression from DCIS to invasive cancer is unclear, with estimates ranging from 14% to 53% , there is a growing debate about overdiagnosis and consequent overtreatment of DCIS. Furthermore, among DCIS-only cases diagnosed at core biopsy, approximately 26% will be shown to contain invasive ductal carcinoma at surgical excision . This upstaging, specifically from DCIS diagnosed at core biopsy to invasive ductal carcinoma at excision, has important consequences for patient management.
Many studies have sought to predict the occult invasion in DCIS. Different factors or markers, including immunohistochemical biomarkers, histologic features, and mammographic or sonographic findings, have been described and associated with outcomes in DCIS . However, none of these factors have been accepted as a definitive predictor of this upstaging or are sufficiently reliable for clinical use. Overall, it still remains a difficult task and unmet need to accurately predict occult invasive disease in DCIS.
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Materials and Methods
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Subject Selection
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Detection of Individual MCs and Clusters
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Feature Extraction
Computer-vision Features
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Table 1
Computer-vision features extracted from individual MCs and cluster
Category Feature Description Individual MCs Shape MC perimeter (p) Length of MC contour MC area (Amc) Number of pixels for an MC × pixel area (µm 2 ) MC circularity 4πAmc/ p 2 , a measure of “roundness” MC eccentricity Another measure of “roundness” MC major axis Length of the major axis of the MC region MC minor axis Length of the minor axis of the MC region MC Hu moments *7 Descriptive weighted averages of intensities Topology MC distance2centroid Distance to the cluster centroid MC distance2closest Distance to the nearest MC neighbor MC degree Number of edges incident to MC MC normalized degree Sum of normalized weights of the MC degree Texture MC background *2 Mean and SD of background pixel intensities MC foreground *2 Mean and SD of MC’s pixel intensities MC GLCM * 4 Measures computed from GLCMs Cluster Shape MCC area (Ac) Area of cluster MCC eccentricity Eccentricity of the cluster Topology MCC number (n) Number of MCs in the cluster MCC density 2 E / n ( n − 1): E is number of graph edges MCC coverage Sum(Amc/Ac) Texture Cluster background *2 Mean and SD of background pixel intensities in the cluster Cluster foreground *2 Mean and SD of MC’s pixel intensities in the cluster Cluster GLCM * 4 Measures computed from GLCMs for the whole cluster
GLCM, gray-level co-occurrence matrix; MC, microcalcification; MCC, microcalcification cluster; SD, standard deviation.
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Physician-interpreted Features
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Table 2
Comparison of histologic and mammographic features of the DCIS and invasive groups
Feature DCIS (74) Invasion (25)P Value Age 59.8 58.2P ≤ 0.5330 Histologic Nuclear grade (1–3) 2.51 2.58P ≤ 0.6044 1 6 3 2 27 6 3 41 16 Subtype of DCIS_P_ ≤ 0.5250 Comedo 36 14 Noncomedo 38 11 Mammographic Radiologist A Radiologist B Size of lesion Area (mm 2 ) 210.3 369.0P ≤ 0.2257 182.5 442.9P ≤ 0.0305 \* Major axis (mm) 16.7 24.8P ≤ 0.0496 \* 17.3 25.4P ≤ 0.0345 \* Morphology of calcifications_P_ ≤ 0.0704P ≤ 0.8690 Low risk (typically benign) 0 1 0 0 Medium risk (amorphous or coarse heterogeneous) 41 7 16 5 High risk (fine pleomorphic or fine linear) 33 17 58 20 Distribution of calcifications_P_ ≤ 0.6653P ≤ 0.2056 Regional 2 0 11 3 Segmental 5 3 3 4 Linear 2 1 15 7 Clustered 65 21 45 11 BI-RADS level of suspicion_P_ ≤ 0.0247 \* P ≤ 0.0321 \* 4a 40 7 1 1 4b 17 8 35 6 4c 14 8 23 7 5 3 2 15 11 Radiologist’s score of being invasive 14.5 21.0P ≤ 0.0052 \* 32.2 35.4P ≤ 0.4965
BI-RADS, Breast Imaging Reporting and Data System; DCIS, ductal carcinoma in situ .
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Building and Evaluating Predictive Models
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Results
Univariate Performance
Computer-vision Features
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Physician-interpreted Features
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Multivariate Performance
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Discussion
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Conclusions
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