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Objective was to estimate the 5-year cumulative risk of HCC in patients with HBC by utilizing an artificial neural network (ANN). Methods: We conducted this study with 1589 patients hospitalized at Beijing Ditan Hospital of Capital Medical University and People's Liberation Army Fifth Medical Center. The training cohort consisted of 913 subjects from Beijing Ditan Hospital of Capital Medical University, while the validation cohort comprised 676 subjects from People's Liberation Army Fifth Medical Center. Through univariate analysis, we identified factors that independently influenced the occurrence of HCC, which were then used to develop the ANN model. To evaluate the ANN model, we assessed its predictive accuracy, discriminative ability, and clinical net benefit using metrics such as the area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curves. Results: In total, we included nine independent risk factors in the development of the ANN model. Remarkably, the AUC of the ANN model was 0.880, significantly outperforming the AUC values of other existing models including mPAGE-B (0.719) (95% CI 0.670– 0.768), PAGE-B (0. 710) (95% CI 0.660– 0.759), FIB-4 (0.693) (95% CI 0.640– 0.745), and Toronto hepatoma risk index (THRI) (0.705) (95% CI 0.654– 0.756) (p< 0.001 for all). The ANN model effectively stratified patients into low, medium, and high-risk groups based on their 5-year In the training cohort, the positive predictive value (PPV) for low-risk patients was 26.2% (95% CI 25.0– 27.4), and the negative predictive value (NPV) was 98.7% (95% CI 95.2– 99.7). For high-risk patients, the PPV was 54.7% (95% CI 48.6– 60.7), and the NPV was 91.6% (95% CI 89.4– 93.4). These findings were validated in the independent validation cohort. Conclusion: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of the 5-year risk of HCC in patients with HBC. [ABSTRACT FROM AUTHOR]

Background: Individuals with hepatocellular carcinoma (HCC) are at risk of tumor recurrence after surgical resection, which affects their survival. The aim of the present study was to establish a model for predicting tumor progression in patients with HCC. Methods: To develop and validate the efficacy of a novel prognostic model, a retrospective cohort with HCC (n = 1005) at Beijing Ditan Hospital was enrolled from January 2008 and June 2017. Furthermore, a prospective cohort (n = 77) was recruited to validate the association between thyroid-stimulating hormone (TSH) levels and tumor progression in patients with HCC. Results: The model used in predicting the progression of HCC included four variables (namely, Barcelona Clinic Liver Cancer [BCLC] stage, presence of portal vein tumor thrombus, alpha-fetoprotein level, and TSH level). The AUROC of the 1-year progression-free survival (PFS) model was 0.755 and 0.753 in the deriving cohort and validation cohort, respectively, and these values were significantly higher than those of the Child–Pugh score, Model for End-stage Liver Disease (MELD), tumor–lymph node–metastasis (TNM) staging system, Okuda classification, and CLIP score. A simple assessment using a nomogram showed the 1-year PFS rate of patients with HCC. In the prospective cohort, the KM curve showed that the high TSH level group had a shorter PFS than the low TSH level (p = 0.001). Conclusion: The prognostic model of HCC progression was superior to other well-known classical tumor scoring systems. A high TSH level was correlated to poor outcome, particularly those with advanced HCC. [ABSTRACT FROM AUTHOR]