Xiaoli Liu1, Yixin Hou1, Xinhui Wang1, Lihua Yu1, Xianbo Wang1, Li Jiang2, Zhiyun Yang3. 1. Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, China. 2. Department of Hepatobiliary Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, China. movdush@139.com. 3. Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, China. yangzhiyun2016@163.com.
Abstract
OBJECT: Disease progression is an important factor affecting the long-term survival in hepatocellular carcinoma (HCC). The progression-free survival (PFS) has been used as a surrogate endpoint for overall survival (OS) in many solid tumors. However, there were few models to predict the PFS in HCC patients. This study aimed to explore the prognostic factors that affect the PFS in HCC and establish an individualized prediction model. METHODS: We included 2890 patients with hepatitis B-related HCC hospitalized at Beijing Ditan Hospital, Capital Medical University and randomly divided into training and validation cohort. Cox multivariate regression was used to analyze independent risk factors affecting the 1-year PFS of HCC, and an artificial neural networks (ANNs) model was constructed. C-index, calibration curve, and decision curve analysis were used to evaluate the performance of the model. RESULTS: The median survival time was 26.2 m (95% CI: 24.08-28.32) and the 1-year PFS rate was 52.3% in whole study population. Cox multivariate regression showed smoking history, tumor number ≥ 2, tumor size ≥ 5 cm, portal vein tumor thrombus, WBC, NLR, γ-GGT, ALP, and AFP ≥ 400 ng/mL were risk factors for 1-year progression-free survival, while albumin and CD4 T cell counts were protective factors in HCC patients. A prediction model for 1-year PFS was constructed ( https://lixuan.me/annmodel/myg-v3/ ). The ANNs model's ability to predict 1-year PFS had an area under the receiver operating characteristic curve (AUROC) of 0.866 (95% CI 0.848-0.884) in HCC patients, which was higher than predicted by TNM, BCLC, Okuda, CLIP, CUPI, JIS, and ALBI scores (p < 0.0001). In addition, the ANNs model could also estimate the probability of 1-year OS and presented a higher AUROC value, 0.877 (95% CI 0.858-0.895), than those other models. All patients were divided into high-, medium-, and low-risk groups, according to the ANNs model scores. Compared with the hazard ratios (HRs) of PFS and OS in low-risk group, those in the high-risk group were 26.42 (95% CI 18.74-37.25; p < 0.0001) and 11.26 (95% CI 9.11-13.93; p < 0.0001), respectively. CONCLUSION: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of progression-free survival in HCC during clinical practice.
RCT Entities:
OBJECT: Disease progression is an important factor affecting the long-term survival in hepatocellular carcinoma (HCC). The progression-free survival (PFS) has been used as a surrogate endpoint for overall survival (OS) in many solid tumors. However, there were few models to predict the PFS in HCC patients. This study aimed to explore the prognostic factors that affect the PFS in HCC and establish an individualized prediction model. METHODS: We included 2890 patients with hepatitis B-related HCC hospitalized at Beijing Ditan Hospital, Capital Medical University and randomly divided into training and validation cohort. Cox multivariate regression was used to analyze independent risk factors affecting the 1-year PFS of HCC, and an artificial neural networks (ANNs) model was constructed. C-index, calibration curve, and decision curve analysis were used to evaluate the performance of the model. RESULTS: The median survival time was 26.2 m (95% CI: 24.08-28.32) and the 1-year PFS rate was 52.3% in whole study population. Cox multivariate regression showed smoking history, tumor number ≥ 2, tumor size ≥ 5 cm, portal vein tumor thrombus, WBC, NLR, γ-GGT, ALP, and AFP ≥ 400 ng/mL were risk factors for 1-year progression-free survival, while albumin and CD4 T cell counts were protective factors in HCC patients. A prediction model for 1-year PFS was constructed ( https://lixuan.me/annmodel/myg-v3/ ). The ANNs model's ability to predict 1-year PFS had an area under the receiver operating characteristic curve (AUROC) of 0.866 (95% CI 0.848-0.884) in HCC patients, which was higher than predicted by TNM, BCLC, Okuda, CLIP, CUPI, JIS, and ALBI scores (p < 0.0001). In addition, the ANNs model could also estimate the probability of 1-year OS and presented a higher AUROC value, 0.877 (95% CI 0.858-0.895), than those other models. All patients were divided into high-, medium-, and low-risk groups, according to the ANNs model scores. Compared with the hazard ratios (HRs) of PFS and OS in low-risk group, those in the high-risk group were 26.42 (95% CI 18.74-37.25; p < 0.0001) and 11.26 (95% CI 9.11-13.93; p < 0.0001), respectively. CONCLUSION: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of progression-free survival in HCC during clinical practice.
Entities:
Keywords:
Artificial neural networks; CD4; Circulating T cells; HCC; Hepatitis B virus; Immunology; Machine learning; Prognosis; Progression-free survival; Risk factor