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.
Carcinoma, Hepatocellular/pathology* ; Humans ; Liver Neoplasms/pathology* ; Predictive Value of Tests ; Retrospective Studies ; Tomography, X-Ray Computed/methods*

To illustrate the literature distribution, research power distribution, and research hotspots in the radiomics research by using knowledge mapping analysis, and to provide reference for relevant researchers.
 Methods: Bibliographies from literature regarding radiomics in Web of Science database were downloaded. BICOM 2.0.1 and SATI 3.2 were used to clean and caculate the frequency of publication year, journal, author, key word, and research institution. CiteSpace V4.4.R1 was used to build the knowledge map of scientific research collaboration network between countries/regions.Ucinet 6 was used to build the knowledge map of scientific research collaboration network between core authors and institutions. gCLUTO 1.0 was applied to construct high-frequency keywords bi-clustering map.
 Results: A total of 700 literature was screened. Since 2012 the number of publications has been growing rapidly year by year. The United States, China, and Netherlands were leaders in this field. There were 5 major scientific research institution cooperative groups and 10 major author cooperative groups. Eight research hotspots were clustered by using high-frequency key word bi-clustering analysis.
 Conclusion: Radiomics is a new field and develops very fast. More and more countries, research institutions, and researchers with multidisciplinary background are going to participate in this filed. New terminology and new methods are going to appear in the field.
China ; Cluster Analysis