Application of the Doylestown algorithm for the early detection of hepatocellular carcinoma.

ABSTRACT:

Background

We previously developed a logistic regression algorithm that uses AFP, age, gender, ALK and ALT levels to improve the detection of hepatocellular carcinoma (HCC). In 3,158 patients from 5 independent sites, this algorithm, referred to as the "Doylestown" algorithm, increased the AUROC of AFP 4% to 12% and had equal benefit regardless of tumor size or the etiology of liver disease.

Aims

Analysis of the Doylestown algorithm using samples from individuals taken before their diagnosis of HCC.

Methods

Here, the algorithm was tested using samples at multiple time points from (a) patients with established chronic liver disease, without HCC (120 patients) and (b) 116 patients with HCC diagnosis (85 patients with early stage HCC and 31 patients with recurrent HCC), taken at the time of, and up to 12 months prior to cancer diagnosis.

Results

Among patients who developed HCC, comparing the Doylestown algorithm at a fixed cut-off to AFP at 20 ng/mL, the Doylestown algorithm increased the True Positive Rate (TPR) in identification of HCC from 36 to 50%, at a time point of 12 months prior to the conventional HCC detection. Similar results were obtained in those patients with recurrent HCC, where the Doylestown algorithm increased TPR in detection of HCC from 18% to 59%, at 12 months prior to detection of recurrence.

Conclusions

This algorithm significantly improves the prediction of HCC by AFP alone and may have value in the early detection of HCC.

SUBMITTER: Mehta AS 

PROVIDER: S-EPMC6118370 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature