Radiomics Methods to Differentiate Metastasis and Primary Lung Cancer of Breast Cancer Patients in PET/CT

Abstract

Purpose：Positron emission tomography (PET) with integrated computed tomography (PET/CT) is a whole-body imaging method providing information the entire body. When it was used in staging breast cancer patients, quite a few patients were found to have a second primary lung cancer(PLC), which was has few distinguishing features from breast cancer metastasis(MBC). Therefore, based on CT, LDCT and PET images, combined with pathological features, we established radiomics models to distinguish between MBC and PLC.Methods：We retrospectively collected CT, LDCT, and PET images, and pathology features of 100 breast cancer patients, including 60 metastases of breast cancer(MBC) and 40 primary lung cancers(PLC). The two radiologists manually drew a region of interest around the whole visible tumor in consensus. Python 3.8 and Pyradiomics toolkit are used to extract features from CT, LDCT, and PET. The linear discriminant analysis (LDA) classifier was used to build the radiomics model. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the classification performance.Results：Total 12, 13, and 9 features were selected from the CT, LDCT, and PET respectively. The model based on the LDCT and PET obtained the same highest AUC (0.9479). The combination with CT and pathology features showed a highest AUC of 0.9583 with a sensitivity of 1.000 and a specificity of 0.8333. Conclusion：Overall, the results are encouraging that radiomics models based on CT, LDCT and PET can differentiate between MBC and PLC pathological features could significantly improve the AUC and ACC of CT model.