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

Cai ZQ, Si SB, Chen C, Zhao Y, Ma YY, Wang L, Geng ZM - PLoS ONE (2015)

Bottom Line: This paper combines the Bayesian network (BN) with importance measures to identify key factors that have significant effects on survival time.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, College of Medicine, Xi'an 710061, Shaanxi, China; Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: Using the BN and importance measures, PVTT was identified as the most significant predictor of survival time for patients with HCC after hepatectomy.

No MeSH data available.


Related in: MedlinePlus

ROC curve of a survival time of >10 months.
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pone.0120805.g003: ROC curve of a survival time of >10 months.

Mentions: Because the BN determined a classification according to probability, the probability could be taken as the discrimination threshold. A patient was classified as having a long survival time (>10 months) when the probability was more than the threshold; otherwise, the patient was classified as having a short survival time (≤10 months). The threshold was set at 0.6127 in the present study so that the model achieved the highest accuracy. For patients with long survival times, the TPR of the model was 83.22% and the FPR was 48.67% at the threshold (Fig. 3). As the threshold varied from 0 to 1, the corresponding FPR and TPR formed the ROC curve (Fig. 3). Thus, we obtained a higher TPR with a given FPR, meaning that we obtained higher prediction accuracy with lower risk.


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

Cai ZQ, Si SB, Chen C, Zhao Y, Ma YY, Wang L, Geng ZM - PLoS ONE (2015)

ROC curve of a survival time of >10 months.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4380349&req=5

pone.0120805.g003: ROC curve of a survival time of >10 months.
Mentions: Because the BN determined a classification according to probability, the probability could be taken as the discrimination threshold. A patient was classified as having a long survival time (>10 months) when the probability was more than the threshold; otherwise, the patient was classified as having a short survival time (≤10 months). The threshold was set at 0.6127 in the present study so that the model achieved the highest accuracy. For patients with long survival times, the TPR of the model was 83.22% and the FPR was 48.67% at the threshold (Fig. 3). As the threshold varied from 0 to 1, the corresponding FPR and TPR formed the ROC curve (Fig. 3). Thus, we obtained a higher TPR with a given FPR, meaning that we obtained higher prediction accuracy with lower risk.

Bottom Line: This paper combines the Bayesian network (BN) with importance measures to identify key factors that have significant effects on survival time.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, College of Medicine, Xi'an 710061, Shaanxi, China; Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: Using the BN and importance measures, PVTT was identified as the most significant predictor of survival time for patients with HCC after hepatectomy.

No MeSH data available.


Related in: MedlinePlus