Effect of multiple peritumoral regions of interest ranges based on computed tomography radiomics for the prediction of early recurrence of hepatocellular carcinoma after resection

Wendi Kang; Xiaomeng Cao; Jianwei Luo

doi:10.21037/qims-23-226

Effect of multiple peritumoral regions of interest ranges based on computed tomography radiomics for the prediction of early recurrence of hepatocellular carcinoma after resection

Quant Imaging Med Surg. 2023 Oct 1;13(10):6668-6682. doi: 10.21037/qims-23-226. Epub 2023 Sep 4.

Authors

Wendi Kang^{1

2

3}, Xiaomeng Cao⁴, Jianwei Luo¹

Affiliations

¹ Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
² Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, China.
⁴ Department of General Surgery, Gansu Provincial Hospital of TCM, Lanzhou, China.

Abstract

Background: Early recurrence (ER) of hepatocellular carcinoma (HCC) is defined as recurrence that occurs within two years after resection. Our study aimed to determine the optimal peritumoral regions of interest (ROI) range by comparing the effect of multiple peritumoral radiomics ROIs on predicting ER of HCC, and to develop and validate a combined clinical-radiomics prediction model.

Methods: A total of 160 HCC patients were randomly divided into a training cohort (n=112) and a validation cohort (n=48). The intratumoral original ROI was outlined based on enhanced computed tomography images and then used as the base to sequentially extend outward 1-5 mm to form peritumoral ROI. We developed a logistic regression model to predict ER of HCC. The efficacy of different ROI prediction models was compared to determine the optimal ROI. The combined model divided the patients into a high-risk group and low-risk group.

Results: Ninety-seven (60.6%) of the patients were ER; the remaining 63 (39.4%) were not ER. The area under the curve values and 95% confidence intervals for ROI 3 were 0.867 (0.802-0.933) and 0.807 (0.682-0.931) in the training and validation cohorts, respectively, and ROI 3 was identified as the optimal ROI. Multivariate logistic regression analysis determined microvascular invasion (MVI) (P=0.037) and alpha-fetoprotein (AFP) (P=0.013) to be independent risk factors for ER. The combined clinical-radiomic model containing the radiomics score, MVI, and AFP had the optimal predictive efficacy, with area under the curve values and 95% confidence intervals of 0.903 (0.848-0.957) and 0.830 (0.709-0.952) in the training and validation cohort, respectively. Subgroup analysis showed significantly ER predicted in the high-risk group than the low-risk group (P<0.001).

Conclusions: Peritumoral radiomics 3 mm range was determined as the optimal ROI in this study. The clinical-radiomics combined models can effectively stratify high- and low-risk patients for timely clinical treatment and decision making.

Keywords: Peritumoral radiomics; hepatocellular carcinoma (HCC); intratumoral radiomics; nomogram; risk stratification.