1. School of Basic Medicine, Jining Medical University, Jining Shandong 272067, China丨 2. School of Intelligent Medicine, China
Medical University, Liaoning Shenyang 110122, China丨 3. Department of Medical Imaging, Liaoning Cancer Hospital and Institute,
Cancer Hospital of China Medical University, Liaoning Shenyang 110042, China
Abstract：Objective To develop and validate a radiomics model for diagnosis of soft-tissue tumors based on multi-parametric
magnetic resonance imaging (MRI). Methods T1-CE and T1WI dual-sequence MRI date of 75 soft-tissue tumors patients obtained
from Liaoning Cancer Hospital and Institute China Medical University. K-means algorithm was used to divide the soft tissue tumor
date into enhanced intensity sub region and non-enhanced intensity sub region. The overall region of interest (ROI) and intratumor
segmentation regions were extracted to establish the model. The fusion model was established based on k-nearest neighbor
classifier model and drew the receiver operating characteristic (ROC) curve and correction curve, and calculated the area under curve
(AUC), specificity and sensitivity as the model evaluation index. The potential clinical value of the model was analyzed by decision
curve analysis (DCA). Results The enhanced intensity sub region of T1-CE and T1WI sequences were better than low brightness
subregion and overall ROI in the diagnosis of intra-tumor segmentation regions. The AUC values of our computer prediction model
were 0.865 (sensitivity=0.763, sensitivity=0.763) and 0.856 (specificity=0.867, sensitivity=0.800) in the training and test cohorts,
respectively. The calibration curve showed that the nomogram model had great prediction ability, and DCA analysis confirmed that
the model has good clinical value. Conclusion The dual-sequence MRI image date based on intra-tumor segmentation of soft tissue
tumors in this study can effectively assist the diagnosis of soft tissue tumors, and has certain clinical medical application value.
周晓娅1，尚圣捷2，王颖妮2，董越3，刘冠宇3，罗娅红3，蒋西然2. 基于瘤内异质性分割的软组织肿瘤MRI影像组学辅助诊断研究[J]. 中国医疗设备, 2021, 36(9): 86-90.
ZHOU Xiaoya1，SHANG Shengjie2，WANG Yingni2，DONG Yue3，LIU Guanyu3，LUO Yahong3，JIANG Xiran2. Study on MRI Radiomics on Tumor Heterogeneity Segmentation in Differentiating of Benign
and Malignant Soft-Tissue Tumors. China Medical Devices, 2021, 36(9): 86-90.
