Faculty Collaboration Database

A Logistic Regression Model for Noninvasive Prediction of AFP-Negative Hepatocellular Carcinoma. Technol Cancer Res Treat 2019 Jan 01;18:1533033819846632

Date

05/21/2019

Pubmed ID

31106685

Pubmed Central ID

PMC6535757

DOI

10.1177/1533033819846632

Scopus ID

2-s2.0-85066941729 (requires institutional sign-in at Scopus site)   16 Citations

Abstract

α-Fetoprotein is commonly used in the diagnosis of hepatocellular carcinoma. However, the diagnostic significance of α-fetoprotein has been questioned because a number of patients with hepatocellular carcinoma are α-fetoprotein negative. It is therefore necessary to develop novel noninvasive techniques for the early diagnosis of hepatocellular carcinoma, particularly when α-fetoprotein level is low or negative. The current study aimed to evaluate the diagnostic efficiency of hematological parameters to determine which can act as surrogate markers in α-fetoprotein-negative hepatocellular carcinoma. Therefore, a retrospective study was conducted on a training set recruited from Zhongnan Hospital of Wuhan University-including 171 α-fetoprotein-negative patients with hepatocellular carcinoma and 102 healthy individuals. The results show that mean values of mean platelet volume, red blood cell distribution width, mean platelet volume-PC ratio, neutrophils-lymphocytes ratio, and platelet count-lymphocytes ratio were significantly higher in patients with hepatocellular carcinoma in comparison to the healthy individuals. Most of these parameters showed moderate area under the curve in α-fetoprotein-negative patients with hepatocellular carcinoma, but their sensitivities or specificities were not satisfactory enough. So, we built a logistic regression model combining multiple hematological parameters. This model presented better diagnostic efficiency with area under the curve of 0.922, sensitivity of 83.0%, and specificity of 93.1%. In addition, the 4 validation sets from different hospitals were used to validate the model. They all showed good area under the curve with satisfactory sensitivities or specificities. These data indicate that the logistic regression model combining multiple hematological parameters has better diagnostic efficiency, and they might be helpful for the early diagnosis for α-fetoprotein-negative hepatocellular carcinoma.

Author List

Luo CL, Rong Y, Chen H, Zhang WW, Wu L, Wei D, Wei XQ, Mei LJ, Wang FB

Author

Ling Mei MD Associate Professor in the Medicine department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Biomarkers, Tumor
Carcinoma, Hepatocellular
Female
Humans
Liver Neoplasms
Logistic Models
Male
Neoplasm Staging
Prognosis
ROC Curve
Reproducibility of Results
Retrospective Studies
Workflow
alpha-Fetoproteins