Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

• Published in: BMC cancer Vol. 9; no. 1; p. 389
• Main Authors: Hao, Ke, Luk, John M, Lee, Nikki P Y, Mao, Mao, Zhang, Chunsheng, Ferguson, Mark D, Lamb, John, Dai, Hongyue, Ng, Irene O, Sham, Pak C, Poon, Ronnie T P
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03-11-2009; BioMed Central; BMC
• Subjects: Area Under Curve; Biomarkers; Cancer; Carcinoma, Hepatocellular - mortality; Carcinoma, Hepatocellular - pathology; Carcinoma, Hepatocellular - surgery; Care and treatment; Diagnosis; Disease-Free Survival; Excision (Surgery); Health aspects; Hepatoma; Hospitals; Humans; Kaplan-Meier Estimate; Liver cancer; Liver Neoplasms - mortality; Liver Neoplasms - pathology; Liver Neoplasms - surgery; Medical imaging; Neoplasm Staging; Pathology; Postoperative period; Prognosis; Retrospective Studies; ROC Curve; Smoking; Studies; Surgery; Treatment Outcome; United States; China; Hong Kong
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/1471-2407-9-389

Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters.
We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set
Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum alpha-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7).
This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment.