AUTHOR=Wang Guangsong , Shi Dafa , Guo Qiu , Zhang Haoran , Wang Siyuan , Ren Ke 

TITLE=Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.843436

DOI=10.3389/fonc.2022.843436

ISSN=2234-943X

ABSTRACT=Objectives: To build radiomics model of BI-RADS category 4 and 5 mammographic masses extracted from digital mammography (DM) for mammographic masses characterization by using a sensitivity threshold similar to that of biopsy. 
Materials and Methods: This retrospective study included 288 female patients (age, 51.91± 11.06) who had BI-RADS category 4 or 5 mammographic masses with an indication for biopsy. The patients were divided into two temporal set (training set, 82 malignancies and 110 benignities; independent test set, 48 malignancies and 48 benignities). A total of 188 radiomic features were extracted from mammographic masses on the combination of craniocaudal (CC) position images and mediolateral oblique (MLO) position images. For training set, Pearson’s correlation and the least absolute shrinkage and selection operator (LASSO) were used to select non- redundant radiomics features and useful radiomics features, respectively, and support vector machine (SVM) was applied to construct a radiomics model. The receiver operating characteristic curve (ROC) analysis was used to evaluate the classification performance of the radiomics model and to determine a threshold value with a sensitivity higher than 98% to predict the mammographic masses malignancy. For independent test set, identical threshold value was used to validate the classification performance of the radiomics model. The stability of the radiomics model was evaluated by using a 5-fold cross-validation method, and two breast radiologists assessed the diagnostic agreement of the radiomics model. 
Results: In training set, the radiomics model obtained an AUC of 0.934 (95% CI, 0.898-0.971), a sensitivity of 98.8% (81/82), a threshold of 0.22, and a specificity of 60% (66/110). In test set, the radiomics model obtained an AUC of 0.901 (95% CI, 0.835-0.961), a sensitivity of 95.8% (46/48), and a specificity of 66.7% (32/48). The radiomics model had relatively stable sensitivities in 5-fold cross validation (training set, 97.39%±3.9%; test set, 98.7%±4%).
Conclusion: The radiomics method based on DM may help reduce the temporarily unnecessary invasive biopsies for benign mammographic masses over-classified in BI-RADS category 4 and 5, while providing similar diagnostic performance for malignant mammographic masses as biopsies.