Research on Preoperative Prediction of Parametrial Tissue Invasion of Cervical Cancer Based
on T2WI Combined ADC Imaging
LIU Qinfeng1,4b，ZHANG Qianyu2，HUANG Jing3，GAO Tingting4a，WANG Tao5a,5b，ZHANG Enke1
1. Department of Medical Equipment, Shaanxi Provincial People’s Hospital, Xi’an Shaanxi 710068, China丨
2. School of Life Science and Technology, Xi’an Jiaotong University, Xi’an Shaanxi 710049, China丨
3. Department of Pain, 521 Hospital of Norinco Group, Xi’an Shaanxi 710065, China丨4. a. School of Life Science and Technology丨 b. School of Electronic Engineering, Xidian University, Xi’an Shaanxi 710071, China丨
5. a. Key Laboratory of Shaanxi Province for Craniofacial and Maxillofacial Precision Medicine Research丨 b. Department of Medical
Imaging, Hospital of Stomatology, Xi’an Jiaotong University, Xi’an Shaanxi 710004, China
Abstract：Objective To explore the predictive value of imaging omic model based on MRI T2WI combined with ADC for
preoperative prediction of parametrial invasion status of cervical cancer. Methods The preoperative multi-parameter MRI and
postoperative pathological data of 137 patients with cervical cancer (FIGO stage IB1-IIA, 2009 edition) who underwent radical
hysterectomy were retrospectively collected. These cases were randomly assigned to the training cohort (96 cases) and the validation
cohort (41 cases). The 3D Slicer software was used to segment the maximum horizontal tumor images (T2WI and ADC maps)
manually and delineate the ROI. A total of 2096 features were extracted from the ROIs of all cases. A total of 15 valid features
were screened out using Lasso algorithm and the radiomics signatures of the training cohort and the validation cohort were
established respectively. Finally, multivariate logistic regression model was established by combining those signatures with related
clinicopathological indicators, tested and validate the model performance and developed the corresponding Nomogram. Results The
AUC of classification performance of radiomics signatures was 0.935 (95%CI, 0.887~0.984) in the training cohort and 0.810 (95%CI,
0.675~0.946) in the validation cohort. The C-index of the multivariate logistic regression model was 0.926 (95%CI, 0.889~0.982) in
the training cohort, and 0.875 (95%CI, 0.826~0.913) in the validation cohort. The Hosmer-Lemeshow test showed that the correction
curve of the prediction model fits well with the ideal curve. Conclusion The preoperative prediction model based on the T2WI
combined with ADC maps has a good efficacy in predicting the parametrial invasion status of the patients with cervical cancer, which
can assist doctors to make preoperative individualized treatment decisions for patients.
刘沁峰1,4b，张千彧2，黄静3，高婷婷4a，王涛5a,5b，张恩科1. 基于T2WI联合ADC图的影像组学模型术前预测宫颈癌宫旁组织浸润状况的研究[J]. 中国医疗设备, 2021, 36(12): 90-93.
LIU Qinfeng1,4b，ZHANG Qianyu2，HUANG Jing3，GAO Tingting4a，WANG Tao5a,5b，ZHANG Enke1. Research on Preoperative Prediction of Parametrial Tissue Invasion of Cervical Cancer Based
on T2WI Combined ADC Imaging. China Medical Devices, 2021, 36(12): 90-93.
