A Logistic Regression Model for Noninvasive Prediction of AFP-Negative Hepatocellular Carcinoma.

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.

• Authors: Chang-Liang Luo, Yuan Rong, Hao Chen, Wu-Wen Zhang, Long Wu, Diao Wei, Xiu-Qi Wei, Lie-Jun Mei, Fu-Bing Wang
• Journal: Technology in cancer research & treatment (Technol Cancer Res Treat) Vol. 18 Pg. 1533033819846632 (01 01 2019) ISSN: 1533-0338 [Electronic] United States
• PMID: 31106685 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers, Tumor
• alpha-Fetoproteins

Topics

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