A radiomics method to classify microcalcification clusters in digital breast tomosynthesis. Issue 8 (19th May 2020)

• Record Type: Journal Article
• Authors: Peng, Yunsong; Wu, Shandong; Yuan, Gang; Wu, Zhongyi; Du, Qiang; Sun, Haotian; Yang, Xiaodong; Chen, Qian; Zheng, Jian
• Is Part Of: Medical physics. Volume 47:Issue 8(2020)
• Page Start: 3435
• Page End: 3446
• Publication Date: 2020-05-19
• Subjects: classification -- digital breast tomosynthesis -- microcalcification cluster -- radiomics -- random forest; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14216
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Digital breast tomosynthesis (DBT) is becoming increasingly used in clinical practice. In DBT, the microcalcification clusters may span across multiple slices, which makes it difficult for radiologists to directly assess these distributed clusters for diagnosis. We investigated a radiomics method to classify microcalcification clusters in DBT based on a semiautomatic segmentation process. Methods: We performed a retrospective study on a cohort of 275 patients (including 79 benign and 196 malignant cases) with a total of 550 DBT volumes. Our method consisted of three steps. The initial step was to semiautomatically segment the microcalcification clusters. Then, radiomics features were extracted from the initially segmented microcalcification clusters. Finally, the benign and malignant microcalcification clusters were differentiated by the random forest (RF) classifier using selected subset features. The radiomics models were evaluated both on view‐based and case‐based modes with features selected from different domains. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the classification performance. Results: Twenty‐six key features were selected from a total of 170 radiomics features and these features show promising classification performance. The highest AUC was 0.834 for view‐based mode and 0.868 for case‐based mode when using features selected from the 3D‐domain. The 2D‐domain radiomics features showed a statistically similar performance to the 3D features ( P > 0.05). Conclusion: Radiomics models can provide encouraging performance in classification between malignant and benign microcalcification clusters which are semiautomatically segmented in DBT.