Breast Radiology
A study on CT radiomics approach to predict outcomes of simultaneously pulmonary nodules in breast cancer patients after treatment
Huang Yan, Wang Zhe, Xiao Qin, Sun Yiqun, Li Qin, Wang He, Gu Yajia
Published 2020-05-10
Cite as Chin J Radiol, 2020, 54(5): 474-478. DOI: 10.3760/cma.j.cn112149-20190726-00640
Abstract
ObjectiveTo evaluate the feasibility of CT radiomics method in predicting outcomes of simultaneous pulmonary nodules in breast cancer patients after treatment.
MethodsPatients with breast cancer confirmed by pathology and with simultaneous pulmonary nodules (diameter>5 mm, number≤5) detected by preoperative CT were retrospectively enrolled in this study. Eighty female patients were included (median age: 52, quartile range: 45, 61). The pulmonary nodules (median size: 6.0 mm, quartile range: 5.5, 7.2 mm) were classified into stable group (without change over 2 years) and change group according to follow-up CT findings. The change group was further divided into improved group and progressive group. Eventually, 54 cases were in the stable group, 26 cases were in the change group. One hundred and five texture features were extracted using the python-based pyradiomics package based on preoperative CT images. Stepwise regression was used to exclude features without significant difference in predicting changes of pulmonary nodules. Classifiers model and 5 fold cross validation method were used to obtain the highest performance in predicting outcomes of pulmonary nodules. Receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance of the model.
ResultsAfter features exclusion and selection, three radiomics features were used to establish classifiers between stable group and change group. It was showed that the linear discriminate analysis was the optimal model with the specificity, sensitivity, accuracy and area under the ROC curve (AUC) as 0.980, 0.460, 0.813 and 0.770 respectively. One radiomics feature was chosen to establish classifiers between improved group and progressive group. The coarse gaussian support vector machine (CGSVM) was the optimal model, with the specificity, sensitivity, accuracy and AUC as 0.540, 0.920, 0.713 and 0.880 respectively.
ConclusionsCT radiomics analysis has the potential to predict the outcomes of simultaneous indeterminate pulmonary nodules in breast cancer patients after treatment, and it may contribute to preoperative treatment and postoperative follow-up planning.
Key words:
Breast neoplasms; Pulmonary nodules; Tomography, X-ray computed
Contributor Information
Huang Yan
Shanghai Institute of Medical Imaging, Shanghai 200032, China
Wang Zhe
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
Xiao Qin
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Sun Yiqun
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Li Qin
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Wang He
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
Gu Yajia
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
