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A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy: A Multicenter Study

27 Pages Posted: 16 Oct 2019

See all articles by Li-Yue Sun

Li-Yue Sun

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Qing Ouyang

Government of the People's Republic of China - General Hospital of Southern Theater Command

Yuan He

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Shuai Yang

Sun Yat-sen University (SYSU) - First Affiliated Hospital

Bing-Bing Li

Southern Medical University - Department of Pathology

Bin Chen

Southern Medical University - Department of Pathology

Fang Wang

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Hai-Yun Wang

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Zhong-Guo Zhou

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Ri Gong

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Jian-Yong Shao

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China; Sun Yat-sen University (SYSU) - Collaborative Innovation Center for Cancer Medicine; Sun Yat-sen University (SYSU) - Department of Molecular Diagnostics

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Abstract

Objective: There is no satisfactory indicator for monitoring recurrence after resection in hepatocellular carcinoma (HCC). For patients without metastasis after hepatectomy, an HCC monitor recurrence (HMR) model was designed and validated by a multicenter retrospective study.

Methods: A training cohort was recruited of 1179 patients with HCC without metastasis after hepatectomy from February 2012 to December 2015. An HMR model was proposed with an AdaBoost classifier algorithm; factors included patient age, TNM staging, tumor size, and pre/postoperative dynamic variations of alpha-fetoprotein (AFP). The diagnostic efficacy of the model was evaluated relative to that of the traditional AFP monitoring recurrence model based on the areas under the respective receiver operating characteristic curves (AUCs). Prediction of recurrence was evaluated relative to computed tomography/magnetic resonance imaging (CT/MRI). The model was validated via an internal cohort comprising 695 patients, and two external cohorts of 30 and 33 patients, respectively.

Results: In preoperative patients with positive or negative AFP, the AUCs of the HMR model (0.8708-0.9609) indicated better diagnostic efficacy compared with serum AFP (0.7348-0.8196). The HMR model predicted recurrence earlier than did CT/MRI, by 77.64 to 191.58 days.

Conclusion: The HMR model was more accurate than serum AFP for monitoring recurrence after hepatectomy of HCC, and can be used for real-time monitoring of the postoperative status of patients with HCC without metastasis. The HMR model should help individualize strategies for reexaminations, and guide adjuvant treatment schemes for patients with HCC.

Funding Statement: This work was supported by grants from the National Natural Science Foundation of China (grant no. 81602468 & 81800556).

Declaration of Interests: The authors declare no competing interests.

Ethics Approval Statement: The Ethics Committee of Sun Yat-Sen University Cancer Center (SYSUCC) approved this study. Every participant provided signed informed consent.

Keywords: hepatocellular carcinoma; model; hepatectomy; monitor; recurrence

Suggested Citation

Sun, Li-Yue and Ouyang, Qing and He, Yuan and Yang, Shuai and Li, Bing-Bing and Chen, Bin and Wang, Fang and Wang, Hai-Yun and Zhou, Zhong-Guo and Gong, Ri and Shao, Jian-Yong, A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy: A Multicenter Study (October 11, 2019). Available at SSRN: https://ssrn.com/abstract=3468366 or http://dx.doi.org/10.2139/ssrn.3468366

Li-Yue Sun

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Guangzhou, 510060
China

Qing Ouyang

Government of the People's Republic of China - General Hospital of Southern Theater Command

Guangzhou
China

Yuan He

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Guangzhou, 510060
China

Shuai Yang

Sun Yat-sen University (SYSU) - First Affiliated Hospital

135, Xingang Xi Road
Guangzhou, Guangdong 510275
China

Bing-Bing Li

Southern Medical University - Department of Pathology

Guangzhou, Guangdong Province
China

Bin Chen

Southern Medical University - Department of Pathology

Guangzhou, Guangdong Province
China

Fang Wang

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Guangzhou, 510060
China

Hai-Yun Wang

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Guangzhou, 510060
China

Zhong-Guo Zhou

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Guangzhou, 510060
China

Ri Gong

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China

Guangzhou, 510060
China

Jian-Yong Shao (Contact Author)

Sun Yat-sen University (SYSU) - State Key Laboratory of Oncology in South China ( email )

Guangzhou, 510060
China

Sun Yat-sen University (SYSU) - Collaborative Innovation Center for Cancer Medicine ( email )

Guangdong, 510060
China

Sun Yat-sen University (SYSU) - Department of Molecular Diagnostics ( email )

China