CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Yue Huang; Lingfeng Chen; Qingzhu Ding; Han Zhang; Yun Zhong; Xiang Zhang; Shangeng Weng

doi:10.3389/fonc.2024.1295575

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Front Oncol. 2024 Apr 16:14:1295575. doi: 10.3389/fonc.2024.1295575. eCollection 2024.

Authors

Yue Huang^#^{1

2}, Lingfeng Chen^#^{1

2}, Qingzhu Ding^{1

2}, Han Zhang^{1

2}, Yun Zhong^{1

2}, Xiang Zhang^{1

2}, Shangeng Weng^{1

2}

Affiliations

¹ Department of Hepatobiliary Pancreatic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
² Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.

^# Contributed equally.

Abstract

Objective: To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods: Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results: In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions: Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Keywords: CT; hepatocellular carcinoma; inflammatory biomarkers; pathological grade; radiomics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Backbone Talents Training Project of Fujian Health Department (Grant No. 2020GGB033) and the Natural Science Foundation of Fujian Province (Grant No. 2020J01980).