Prediction of pathological grade of hepatocellular carcinoma based on enhanced CT radiomics
10.13929/j.issn.1003-3289.2020.07.026
- VernacularTitle: 基于增强CT放射组学预测肝细胞肝癌病理分级
- Author:
Peigang NING
1
Author Information
1. Department of Medical Imaging, Henan Provincial People's Hospital, School of Clinical Medicine, Henan University, People's Hospital of Zhengzhou University
- Publication Type:Journal Article
- Keywords:
Carcinoma, hepatocellular;
Diagnosis;
Pathology;
Radiomics;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2020;36(7):1051-1056
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the feasibility and value of preoperative prediction of pathological grade of hepatocellular carcinoma (HCC) based on enhanced CT radiomics. Methods: Imaging and clinical data of 429 HCC patients confirmed by surgical pathology were retrospectively analyzed. The patients were divided into training group (n=329) and test group (n=100), and their clinical characteristics were recorded. Radiology features of arterial-phase (AP) and portal venous-phase (VP) CT images were extracted, the least absolute shrinkage and selection operator method (LASSO) were used to reduce dimension and select the most valuable radiomics features. Then CT radiomics models were built base on AP features, VP features and AP+VP features, respectively. Radiological scores (rad-score) of 2 groups were calculated and then classified. According to surgical pathology results, the pathological grade of HCC was defined as high-grade and low-grade, and the optimal radiomics prediction model was selected through 10-fold cross-validation training. Finally clinical model and combined model (clinical features combined with radiomics) were constructed after screening clinical characteristics for predicting pathological grade of HCC. ROC curves of the above 3 models for predicting pathological grade of HCC in training group and test group were drawn, and their diagnostic efficacy were evaluated. Results: Combined radiomics model was the best among 3 models, and the rad-scores of high-grade and low-grade HCC were significantly different in both training group and test group (Z=8.58, 3.24, both P<0.05). In test group,no statistical difference of AUC of combined model(0.70),of radiomics model (0.69) nor clinical model (0.63) was detected for predicting pathological grading of HCC (all P>0.05). Conclusion: Radiomics features based on enhanced CT images can be used to preoperative predict pathological grade of HCC, providing reference for diagnosis and treatment of HCC.
