Analysis of prognostic factors for survival after hepatectomy for hepatocellular carcinoma based on a bayesian network

• Published in: PloS one 2015-03, Vol.10 (3), p.e0120805-e0120805
• Main Authors: Cai, Zhi-Qiang, Si, Shu-Bin, Chen, Chen, Zhao, Yaling, Ma, Yong-Yi, Wang, Lin, Geng, Zhi-Min
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Analysis; Bayes Theorem; Bayesian analysis; Blood clots; Carcinoma, Hepatocellular - etiology; Carcinoma, Hepatocellular - mortality; Carcinoma, Hepatocellular - pathology; Carcinoma, Hepatocellular - surgery; Complications; Data mining; Dependent variables; Female; Gene expression; Hepatectomy; Hepatocellular carcinoma; Hospitals; Humans; Industrial engineering; Influence; International conferences; Knowledge management; Liver; Liver cancer; Liver Neoplasms - etiology; Liver Neoplasms - mortality; Liver Neoplasms - pathology; Liver Neoplasms - surgery; Male; Mathematical models; Mechanical engineering; Medical prognosis; Medicine; Metastases; Metastasis; Middle Aged; Model accuracy; Mortality; Neural networks; Patients; Portal vein; Prognosis; Random variables; ROC Curve; Surgery; Survival; Thromboembolism; Thrombosis; Time dependence; Tumors; Veins & arteries; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0120805

The prognosis of hepatocellular carcinoma (HCC) after hepatectomy involves many factors. Previous studies have evaluated the separate influences of single factors; few have considered the combined influence of various factors. This paper combines the Bayesian network (BN) with importance measures to identify key factors that have significant effects on survival time. A dataset of 299 patients with HCC after hepatectomy was studied to establish a BN using a tree-augmented naïve Bayes algorithm that could mine relationships between factors. The composite importance measure was applied to rank the impact of factors on survival time. 124 patients (>10 months) and 77 patients (≤10 months) were correctly classified. The accuracy of BN model was 67.2%. For patients with long survival time (>10 months), the true-positive rate of the model was 83.22% and the false-positive rate was 48.67%. According to the model, the preoperative alpha fetoprotein (AFP) level and postoperative performance of transcatheter arterial chemoembolization (TACE) were independent factors for survival of HCC patients. The grade of preoperative liver function reflected the tendency for postoperative complications. Intraoperative blood loss, tumor size, portal vein tumor thrombosis (PVTT), time of clamping the porta hepatis, tumor number, operative method, and metastasis were dependent variables in survival time prediction. PVTT was considered the most significant for the prognosis of survival time. Using the BN and importance measures, PVTT was identified as the most significant predictor of survival time for patients with HCC after hepatectomy.