Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy

Jia Li; Yunhui Ma; Chunyu Yang; Ganbin Qiu; Jingmu Chen; Xiaoliang Tan; Yue Zhao

doi:10.3389/fonc.2024.1277698

Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy

Front Oncol. 2024 Feb 23:14:1277698. doi: 10.3389/fonc.2024.1277698. eCollection 2024.

Authors

Jia Li^#¹, Yunhui Ma^#¹, Chunyu Yang², Ganbin Qiu³, Jingmu Chen⁴, Xiaoliang Tan⁴, Yue Zhao⁴

Affiliations

¹ Department of Oncology, Central People's Hospital of Zhanjiang, Zhanjiang, China.
² Department of Radiology, The First School of Clinical Medicine, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
³ Imaging Department of Zhaoqing Medical College, Zhaoqing, China.
⁴ Department of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, China.

^# Contributed equally.

Abstract

Objectives: This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.

Methods: We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated.

Results: Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.

Conclusion: The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.

Keywords: early recurrence; hepatocellular carcinoma; magnetic resonance imaging; nomogram; radiomics analysis.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Science and Technology innovation of Zhaoqing City, Guangdong Province, China under Grant (No. 2019N008). This work was supported by Medical Scientific Research Foundation of Guangdong Province, China under Grant (No. B2020225).