Application of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer

• Published in: BMC medical imaging Vol. 22; no. 1; pp. 1 - 166
• Main Authors: Ge, Shuai, Yixing, Yu, Jia, Dong, Ling, Yang
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 14.09.2022; BioMed Central; BMC
• Subjects: Accuracy; Biopsy; Breast cancer; Calcification; Chemotherapy; Confidence intervals; Correlation coefficient; Correlation coefficients; Discriminant analysis; Feature extraction; Feature selection; Image processing; Image segmentation; Mammography; Mathematical analysis; Medical imaging; Normal distribution; Patients; Physicians; Predictions; Radiomics; Software; Statistical analysis; Statistical significance; Training; Transportation equipment industry; Triple-negative breast cancer; Tumors; Japan
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-022-00875-6

Abstract Objective This study is aimed to explore the value of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer (TNBC). Materials and methods Initially, the clinical and X-ray data of patients (n = 319, age of 54 ± 14) with breast cancer (triple-negative—65, non-triple-negative—254) from the First Affiliated Hospital of Soochow University (n = 211, as a training set) and Suzhou Municipal Hospital (n = 108, as a verification set) from January 2018 to February 2021 are retrospectively analyzed. Comparing the mediolateral oblique (MLO) and cranial cauda (CC) mammography images, the mammography images with larger lesion areas are selected, and the image segmentation and radiomics feature extraction are then performed by the MaZda software. Further, the Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) are used to select three sets of feature subsets. Moreover, the score of each patient’s radiomics signature (Radscore) is calculated. Finally, the receiver operating characteristic curve (ROC) is analyzed to calculate the AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of TNBC. Results A significant difference in the mammography manifestation between the triple-negative and the non-triple-negative groups ( P  < 0.001) is observed. The (POE + ACC)-NDA method showed the highest accuracy of 88.39%. The Radscore of triple-negative and non-triple-negative groups in the training set includes − 0.678 (− 1.292, 0.088) and − 2.536 (− 3.496, − 1.324), respectively, with a statistically significant difference ( Z  = − 6.314, P  < 0.001). In contrast, the Radscore in the validation set includes − 0.750 (− 1.332, − 0.054) and − 2.223 (− 2.963, − 1.256), with a statistically significant difference ( Z  = − 4.669, P  < 0.001). In the training set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC include 0.821 (95% confidence interval 0.752–0.890), 74.4%, 82.5%, 72.5%, 41.2%, and 94.6%, respectively. In the validation set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC are of 0.809 (95% confidence interval 0.711–0.907), 80.6%, 72.0%, 80.7%, 55.5%, and 93.1%, respectively. Conclusion In summary, we firmly believe that this mammography-based radiomics signature could be useful in the preoperative prediction of TNBC due to its high value.