DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China.
- Department of Radiology, Huzhou Central Hospital, No. 1558 Third Ring North Road, Huzhou 313000, China.
- Department of Radiology, Jiangsu Cancer Hospital, No. 42 BaiziTing Road, Xuanwu District, Nanjing 210000, China.
- Department of Medical Imaging, The First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China.
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, No. 801 Zhihuai Road, Bengbu 233004, China.
- Clinical Institute of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China. Electronic address: [email protected].
- Department of Gynaecology and Obstetrics, The First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China. Electronic address: [email protected].
Abstract
To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer. A total of 290 breast cancer patients from two hospitals were retrospectively collected. A k-means clustering algorithm confirmed subregions of tumor. DL features of whole tumor and subregions were extracted from DCE-MRI images based on 3D ResNet18 pre-trained model. The logistic regression model was constructed after dimension reduction. Model performance was assessed using the area under the curve (AUC), and clinical value was demonstrated through decision curve analysis (DCA). The k-means clustering method clustered the tumor into two subregions (habitat 1 and habitat 2) based on voxel values. Both the habitat 1 model (validation set: AUC = 0.771, 95 %CI: 0.642-0.900 and external test set: AUC = 0.794, 95 %CI: 0.696-0.891) and the habitat 2 model (AUC = 0.734, 95 %CI: 0.605-0.862 and AUC = 0.756, 95 %CI: 0.646-0.866) showed better predictive capabilities for Ki-67 expression level than the whole tumor model (AUC = 0.686, 95 %CI: 0.550-0.823 and AUC = 0.680, 95 %CI: 0.555-0.804). The combined model based on the two subregions further enhanced the predictive capability (AUC = 0.808, 95 %CI: 0.696-0.921 and AUC = 0.842, 95 %CI: 0.758-0.926), and it demonstrated higher clinical value than other models in DCA. The deep learning model derived from subregion of tumor showed better performance for predicting Ki-67 expression level in breast cancer patients. Additionally, the model that integrated two subregions further enhanced the predictive performance.