Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma.
10.11817/j.issn.1672-7347.2022.220027
- Author:
Huafei ZHAO
1
;
Zhichao FENG
2
;
Huiling LI
2
;
Shanhu YAO
2
;
Wei ZHENG
3
;
Pengfei RONG
4
Author Information
1. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013. 18875508536@163.com.
2. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013.
3. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013. zheng_wei@csu.edu.cn.
4. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013. rongpengfei66@163.com.
- Publication Type:Randomized Controlled Trial
- Keywords:
computed tomography;
hepatocellular carcinoma;
microvascular invasion;
radiomics;
region of interest
- MeSH:
Carcinoma, Hepatocellular/pathology*;
Humans;
Liver Neoplasms/pathology*;
Predictive Value of Tests;
Retrospective Studies;
Tomography, X-Ray Computed/methods*
- From:
Journal of Central South University(Medical Sciences)
2022;47(8):1049-1057
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC.
METHODS:Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve.
RESULTS:A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05).
CONCLUSIONS:The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.
