Association Rule Mining-Based Radiomics in Breast Cancer Diagnosis

Document Type : Original Paper

Authors

1 Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

2 Department of Radiology and Biomedical Imaging, Yale School of Medicine, PO Box 208048, New Haven, CT, 06520-8048, USA.

3 Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

4 Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Abstract

Introduction: Breast cancer is the most common cancer among women worldwide. Early detection of breast cancer reduces mortality and morbidity. Acquiring or identifying valuable information in the form of rules is the key to an accurate diagnosis and differentiation between benign and malignant breast cancers. Our goal is to find the hidden but beneficial knowledge in the form of rules from datasets. In this paper, we use association rule mining algorithms to obtain information in the form of data rules for differential diagnosis between benign and malignant breast masses based on radiomic features, extracted from mammography images.
Material and Methods: In this study, 703 patients with both benign and malignant tumors were selected from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) database. The embedded method was employed to select the radiomic features of the image and uncover the hidden patterns of data through the Apriori algorithm.
Results: The association rules were generated from separated rules for benign and malignancy classes. The important features of the benign class include mass margins, horizontal long-run emphasis, 135drfraction, WavEnHLs3, vertical short-run emphasis, Teta1, 45dgr run-length with no uniformity, Teta2, and differential entropy2. However, the important features of the malignant class include assessment, correlation4, Teta1, WavEnLHs3, contrast5, vertical short-run emphasis, and differential entropy2.
Conclusion: It can be concluded that the proposed method has been successful in determining specific features for tumor prediction.

Keywords

Main Subjects


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