Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images.
10.7507/1001-5515.201803014
- Author:
Fei GAO
1
;
Bin YAN
2
;
Lei ZENG
2
;
Minghui WU
3
;
Hongna TAN
3
;
Jinjin HAI
2
;
Peigang NING
3
;
Dapeng SHI
3
Author Information
1. College of Information System Engineering, Information Engineering University, Zhengzhou 450001, P.R.China.gfflyfly@163.com.
2. College of Information System Engineering, Information Engineering University, Zhengzhou 450001, P.R.China.
3. Department of Radiology, Henan General Hospital, Zhengzhou 450002, P.R.China.
- Publication Type:Journal Article
- Keywords:
LASSO regression;
liver tumour;
magnetic resonance image;
pathological grading;
radiomics
- MeSH:
Carcinoma, Hepatocellular;
diagnostic imaging;
Humans;
Liver Neoplasms;
diagnostic imaging;
Magnetic Resonance Imaging;
Neoplasm Grading;
methods;
ROC Curve
- From:
Journal of Biomedical Engineering
2019;36(4):581-589
- CountryChina
- Language:Chinese
-
Abstract:
In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: = 125; validation dataset, = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
