Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences - Guo Y; Liao J; Li S; Shang Y; Wang Y; Wu Q; Wu Y; Wang M; Yan F; Tan H (Breast Cancer: Targets and Therapy)
Yaxin Guo,1,* Jun Liao,1,* Shunian Li,1 Yiyan Shang,2 Yunxia Wang,2 Qingxia Wu,3 Yaping Wu,1 Meiyun Wang,1 Fengshan Yan,1 Hongna Tan1 1Department of Radiology, People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital, Zhengzhou, People’s Republic of China; 2Department of Radiology, People’s Hospital of Henan University, Zhengzhou, People’s Republic of China; 3Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence (Beijing) Co., Ltd, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongna Tan; Fengshan Yan, Department of Radiology, People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital, Weiwu Road, Zhengzhou City, 450003, People’s Republic of China, Tel +86-13526766042 ; +86-18637134815, Email natan2000@126.com; mountyan857@163.comBackground: Histological grade is an acknowledged prognostic factor for breast cancer, essential for determining clinical treatment strategies and prognosis assessment. Our study aims to establish intra- and peritumoral radiomics models using T2WI and DWI MR sequences for predicting the histological grade of breast cancer.Methods: 700 breast cancer cases who had MRI scans before surgery were included. The intratumoral region (ITR) of interest was manually delineated, while the peritumoral region (PTR-3 mm) was automatically obtained by expanding the ITR by 3 mm. Radiomics features were extracted using the intra- and peritumoral images from T2WI and DWI sequences on breast MRI. Then, the key features with the strongest predictivity of histological grade were selected. Finally, 9 predictive radiomics models were established based on T2WI-ITR, T2WI-3mmPTR, DWI-ITR, DWI-3mmPTR, T2WI-ITR + 3mmPTR, DWI-ITR + 3mmPTR, (T2WI + DWI)-ITR, (T2WI + DWI)-3mmPTR and (T2WI + DWI)-ITR + 3mmPTR.Results: The (T2WI + DWI)-ITR + 3mmPTR contained 13 DWI features which included a shape feature, a texture feature, and 11 filtered features, as well as 10 T2WI features, all of which were filtered features. Among the 9 models, the combined models showed better performance than the single models in both the training and test sets, especially for the (T2WI + DWI)-ITR + 3mmPTR radiomics model. The (T2WI + DWI)-ITR + 3mmPTR radiomics model achieved a sensitivity, specificity, accuracy, and AUC of 80.4%, 72.4%, 75.0%, and 0.860 in the training set, and 68.9%, 70.5%, 70.0%, and 0.781 in the test set. Decision curve analysis (DCA) showed that the (T2WI + DWI)-ITR + 3mmPTR model had the greatest net clinical benefit compared to the other models.Conclusion: The intra- and peritumoral radiomics methodologies using T2WI and DWI MR sequences could be utilized to assess histological grade for breast cancer, particularly with the (T2WI + DWI)-ITR + 3mmPTR radiomics model demonstrating significant potential for clinical application.Keywords: breast cancer, histological grade, MRI, T2WI, DWI, radiomics