A Novel Prognostic Score Based on Artificial Intelligence in Hepatocellular Carcinoma: A Long-Term Follow-Up Analysis

Abstract

ObjectiveT cell immunity plays an important role in anti-tumor effects and immunosuppression often leads to the development and relapse of cancer. This study aimed to investigate the effect of T cell numbers on the long-term prognosis of patients with hepatocellular carcinoma (HCC) and construct an artificial neural network (ANN) model to evaluate its prognostic value.MethodsWe enrolled 3,427 patients with HCC at Beijing Ditan Hospital, Capital Medical University, and randomly divided them into two groups of 1,861 and 809 patients as the training and validation sets, respectively. Cox regression analysis was used to screen for independent risk factors of survival in patients with HCC. These factors were used to build an ANN model using Python. Concordance index, calibration curve, and decision curve analysis were used to evaluate the model performance.ResultsThe 1-year, 3-year, 5-year, and 10-year cumulative overall survival (OS) rates were 66.9%, 45.7%, 34.9%, and 22.6%, respectively. Cox multivariate regression analysis showed that age, white blood cell count, creatinine, total bilirubin, γ-GGT, LDH, tumor size ≥ 5 cm, tumor number ≥ 2, portal vein tumor thrombus, and AFP ≥ 400 ng/ml were independent risk factors for long-term survival in HCC. Antiviral therapy, albumin, T cell, and CD8 T cell counts were independent protective factors. An ANN model was developed for long-term survival. The areas under the receiver operating characteristic (ROC) curve of 1-year, 3-year, and 5-year OS rates by ANNs were 0.838, 0.833, and 0.843, respectively, which were higher than those of the Barcelona Clinic Liver Cancer (BCLC), tumor node metastasis (TNM), Okuda, Chinese University Prognostic Index (CUPI), Cancer of the Liver Italian Program (CLIP), Japan Integrated Staging (JIS), and albumin–bilirubin (ALBI) models (P < 0.0001). According to the ANN model scores, all patients were divided into high-, middle-, and low-risk groups. Compared with low-risk patients, the hazard ratios of 5-year OS of the high-risk group were 8.11 (95% CI: 7.0-9.4) and 6.13 (95% CI: 4.28-8.79) (P<0.0001) in the training and validation sets, respectively.ConclusionHigh levels of circulating T cells and CD8 + T cells in peripheral blood may benefit the long-term survival of patients with HCC. The ANN model has a good individual prediction performance, which can be used to assess the prognosis of HCC and lay the foundation for the implementation of precision treatment in the future.

Development and Validation of a Novel Prognostic Nomogram Based on Platelet and CD8+T Cell Counts in Hepatocellular Carcinoma Patients with Portal Vein Tumor Thrombosis

“… 22–24 The independent prognostic value of γ-GGT has also been demonstrated in previous studies. 25 , 26 Due to its independent influence on prognosis, treatment modalities are often used to construct prognostic tools for HCC patients. 27 In multiple cancer-related studies, the impact of MPV on prognosis is not uniform.…”

Development of a nomogram to predict the risk of hepatocellular carcinoma in patients with hepatitis B-related cirrhosis on antivirals

“…The immune system plays an important role in immune surveillance and controls tumor growth effects ( 39 ). Furthermore, high levels of circulating and CD8+T cells in patients with HCC have good long-term outcomes ( 40 ). Overall, we established a novel risk nomogram for HCC development among patients with hepatitis-B-related cirrhosis treated with antivirals based on NLR, which is readily obtained from routine blood examinations.…”

Evaluation of Multiple Liver Cancer Scoring Systems

“…It can be used to deal with nonlinear, complex, and uncertain problems, such as classification, regression, clustering, prediction, and so on. [82] Liver cancer is a highly prevalent malignant tumor, whose prognosis is affected by many factors, such as tumor size, number, grade, invasion, metastasis, liver function, serum markers, and so on. Traditional statistical methods such as logistic regression or Cox regression may not fully utilize the complex relationship between these factors, while the ANN model can better capture the impact of these factors on liver cancer prognosis by its adaptive learning ability and nonlinear mapping ability.…”