Abstract

BackgroundSurgical 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.MethodsWe 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 setResultsOur 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 α-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).ConclusionThis 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.

Highlights

  • 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

  • Statistical Analysis We examined whether the clinicopathologic phenotypes that were recorded at the time of surgery might predict cancer prognosis

  • In a simple Cox model, the endpoints were found to be significantly associated with tumor size, serum AFP levels of alpha fetoprotein (AFP), total albumin concentration (ALBU), venous infiltration (VENINV), tumor stage, and the number of tumor nodule (NOTN)

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Summary

Introduction

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. Prognosis following surgery differs substantially and such large variation is mostly unexplained This variation becomes a hurdle in searching for effective and efficacious therapies and cancer management strategies. Our primary goal is to develop and validate models that use clinicopathologic parameters and common biomarkers observed at the time of surgery to predict the HCC prognosis. Such model must be flexible in accommodating addition factors (e.g. protein biomarkers, gene or microRNA expression signatures) when becoming available

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