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Early recurrence risk stratification of hepatocellular carcinoma receiving preoperative radiotherapy: Incorporating clinical and imaging features across treatment phases
Rong Cong, Bo Chen, Sai-Nan Cheng, Yi-Rui Zhai, Fan Wu, Bing Feng, Le-Yao Wang, Zhao-Wei Chen, Yong-Jian Zhu, Xiao-Hong Ma, Xin-Ming Zhao
Rong Cong, Sai-Nan Cheng, Bing Feng, Le-Yao Wang, Zhao-Wei Chen, Yong-Jian Zhu, Xiao-Hong Ma, Xin-Ming Zhao, Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Bo Chen, Yi-Rui Zhai, Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Fan Wu, Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Zhao-Wei Chen, Department of Radiology, Chengdu First People’s Hospital, Chengdu 610000, Sichuan Province, China
Co-first authors: Rong Cong and Bo Chen.
Co-corresponding authors: Xiao-Hong Ma and Xin-Ming Zhao.
Author contributions: Cong R, Chen B, Ma XH, and Zhao XM contributed to the study conception and design; Cong R, Chen B, Cheng SN, Wu F, and Zhai YR were responsible for patient enrollment and data collection; Cong R, Cheng SN, Feng B, Wang LY, Chen ZW, Zhu YJ, Ma XH, and Zhao XM involved in imaging review, data analysis, and interpretation of data; Cong R and Ma XH involved in manuscript preparation; Chen B, and Zhao XM involved in critical revision of the manuscript; Cong R and Chen B contributed equally to this manuscript as co-first authors; Ma XH and Zhao XM contributed equally to this manuscript as co-corresponding authors. All authors reviewed and approved the final manuscript.
Supported by Beijing Hope Run Special Fund of Cancer Foundation of China, No. LC2022A14; and National High Level Hospital Clinical Research Funding, No. 80102022505.
Institutional review board statement: This study was approved by the Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. NCC2023C-936.
Informed consent statement: The need for patient consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: If reasonably necessary, the datasets used and analyzed in this study can be obtained from the corresponding author.
Corresponding author: Xin-Ming Zhao, Professor, Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
zhaoxinming@cicams.ac.cn
Received: December 30, 2025
Revised: January 28, 2026
Accepted: March 9, 2026
Published online: June 21, 2026
Processing time: 161 Days and 1.2 Hours
BACKGROUND
There are still large variations in survival among patients with hepatocellular carcinoma (HCC) who received preoperative radiotherapy (RT), necessitating early recurrence (ER) risk stratification for decision-making and more appropriate management of patients.
AIM
To develop prognostic models integrating magnetic resonance imaging features and clinico-therapeutic-pathological variables across the treatment phases for predicting postoperative ER of HCC patients receiving preoperative RT.
METHODS
Consecutive 81 patients who received RT-based therapy and subsequent hepatectomy for HCC between November 2014 and December 2023 were retrospectively included. Magnetic resonance imaging features and clinico-therapeutic-pathological variables associated with ER-free survival (RFS) were identified using the least absolute shrinkage and selection operator Cox regression analysis. Multiple prognostic models across treatment phases were constructed by multivariate Cox regression analysis. Model performance was assessed with C-index, area under curve, calibration curve and decision curve analysis.
RESULTS
The baseline-preoperative model incorporated three baseline variables (cirrhosis, gamma-glutamyl transpeptidase level, arterial phase hyperenhancement portion ≥ 50%) and two preoperative variables (alpha-fetoprotein level, preoperative complete response). The postoperative combined model was developed by adding microvascular invasion and excluding alpha-fetoprotein level. The baseline-preoperative and postoperative combined model achieved C-indexes of 0.779 (95% confidence interval: 0.714-0.844) and 0.786 (95% confidence interval: 0.717-0.855), respectively, outperforming prognosis models based on other treatment phases and major staging systems (all P < 0.05). Moreover, the two models enabled the effective stratification of early RFS, overall RFS, and overall survival.
CONCLUSION
The baseline-preoperative and postoperative combined model could offer a simple tool to predict early postoperative recurrence of HCC patients receiving preoperative RT, which may assist with personalized treatment and surveillance.
Core Tip: This study developed two models (baseline-preoperative and postoperative combined models) that integrated multimodal factors across multiple treatment stages in patients with hepatocellular carcinoma who received radiotherapy-based therapy combined with subsequent surgery. These models exhibited comparable and satisfactory performance in predicting early recurrence and allowed risk stratification of early recurrence-free survival, overall recurrence-free survival, and overall survival. Including readily accessible variables, the findings have potential clinical relevance, particularly for preoperative decision-making regarding the timing and personalized planning of surgery after radiotherapy, as well as postoperative management involving adjuvant therapy and close surveillance for high-risk patients.
