Copyright
©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Development of a nomogram for predicting liver transplantation prognosis in hepatocellular carcinoma
Li He, Wan-Sheng Ji, Hai-Long Jin, Wen-Jing Lu, Yuan-Yuan Zhang, Hua-Guang Wang, Yu-Yu Liu, Shuang Qiu, Meng Xu, Zi-Peng Lei, Qian Zheng, Xiao-Li Yang, Qing Zhang
Li He, Hai-Long Jin, Yu-Yu Liu, Shuang Qiu, Meng Xu, Zi-Peng Lei, Qian Zheng, Qing Zhang, Department of Organ Transplantation, The Third Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
Li He, School of Clinical Medicine, Shandong Second Medical University, Weifang 261053, Shandong Province, China
Wan-Sheng Ji, Clinical Research Center, The Affiliated Hospital of Shandong Second Medical University, Weifang 261053, Shandong Province, China
Wen-Jing Lu, Yuan-Yuan Zhang, Xiao-Li Yang, Department of Laboratory Medicine, The Third Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
Hua-Guang Wang, Physiatry Department, Naval Aviation University, Yantai 100071, Shandong Province, China
Co-first authors: Li He and Wan-Sheng Ji.
Co-corresponding authors: Xiao-Li Yang and Qing Zhang.
Author contributions: He L, Ji WS, and Jin HL designed and performed the research and wrote the paper; Lu WJ, Zhang YY, and Liu YY designed the research and supervised the report; Wang HG, Qiu S, Xu M, Lei ZP, and Zheng Q designed the research and contributed to the analysis; He L, Ji WS, Jin HL, Yang XL, and Zhang Q provided clinical advice; Yang XL and Zhang Q supervised the report; Yang XL and Zhang Q provided funding support and should be considered as co-corresponding authors.
Supported by the National Natural Science Foundation of China, No. 81372595 and No. 81972696.
Institutional review board statement: This study was reviewed and approved by our hospital’s Ethics Committee (Approval No. 2023-008).
Informed consent statement: Signed informed consent forms were provided by all patients.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Qing Zhang, MD, Chief Physician, Department of Organ Transplantation, The Third Medical Centre of Chinese PLA General Hospital, No. 69 Yongding Road, Haidian District, Beijing 100039, China. zqy6920@sina.com
Received: February 19, 2024
Revised: April 24, 2024
Accepted: May 13, 2024
Published online: June 7, 2024
Processing time: 104 Days and 20.4 Hours
BACKGROUND
At present, liver transplantation (LT) is one of the best treatments for hepatocellular carcinoma (HCC). Accurately predicting the survival status after LT can significantly improve the survival rate after LT, and ensure the best way to make rational use of liver organs.
AIM
To develop a model for predicting prognosis after LT in patients with HCC.
METHODS
Clinical data and follow-up information of 160 patients with HCC who underwent LT were collected and evaluated. The expression levels of alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, Golgi protein 73, cytokeratin-18 epitopes M30 and M65 were measured using a fully automated chemiluminescence analyzer. The best cutoff value of biomarkers was determined using the Youden index. Cox regression analysis was used to identify the independent risk factors. A forest model was constructed using the random forest method. We evaluated the accuracy of the nomogram using the area under the curve, using the calibration curve to assess consistency. A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomograms.
RESULTS
The total tumor diameter (TTD), vascular invasion (VI), AFP, and cytokeratin-18 epitopes M30 (CK18-M30) were identified as important risk factors for outcome after LT. The nomogram had a higher predictive accuracy than the Milan, University of California, San Francisco, and Hangzhou criteria. The calibration curve analyses indicated a good fit. The survival and recurrence-free survival (RFS) of high-risk groups were significantly lower than those of low- and middle-risk groups (P < 0.001). The DCA shows that the model has better clinical practicability.
CONCLUSION
The study developed a predictive nomogram based on TTD, VI, AFP, and CK18-M30 that could accurately predict overall survival and RFS after LT. It can screen for patients with better postoperative prognosis, and improve long-term survival for LT patients.
Core Tip: This is a retrospective study to research the influencing factors affecting the prognosis of liver cancer transplantation, including pathological factors, tumor morphology and biomarkers, exploring the prediction model of liver transplantation with high accuracy, thereby optimizing the allocation of liver transplant resources by striking a balance between maximizing the number of beneficiaries and reducing the risk of hepatocellular carcinoma recurrence. By establishing predictive models and implementing a stratification system, we hope to improve the overall efficacy and success rate of liver transplantation.
