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Cancer Prev Res (Phila). 2016 Feb;9(2):172-9. doi: 10.1158/1940-6207.CAPR-15-0186. Epub 2015 Dec 28.

The Doylestown Algorithm: A Test to Improve the Performance of AFP in the Detection of Hepatocellular Carcinoma.

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Drexel University College of Medicine, Philadelphia, Pennsylvania. 19102.
Fox Chase Cancer Center, Philadelphia, Pennsylvania.
Division of Digestive and Liver Diseases, University of Texas Southwestern, Dallas, Texas.
Department of Biostatistics, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Cancer Biomarkers Research Group, Division of Cancer Prevention, NCI, Bethesda, Maryland.
Drexel University School of Public Health, Philadelphia, Pennsylvania.
Thomas Jefferson University, Philadelphia, Pennsylvania.
The Baruch Blumberg Institute, Doylestown, Pennsylvania.
Drexel University College of Medicine, Philadelphia, Pennsylvania. 19102.


Biomarkers for the early diagnosis of hepatocellular carcinoma (HCC) are needed to decrease mortality from this cancer. However, as new biomarkers have been slow to be brought to clinical practice, we have developed a diagnostic algorithm that utilizes commonly used clinical measurements in those at risk of developing HCC. Briefly, as α-fetoprotein (AFP) is routinely used, an algorithm that incorporated AFP values along with four other clinical factors was developed. Discovery analysis was performed on electronic data from patients who had liver disease (cirrhosis) alone or HCC in the background of cirrhosis. The discovery set consisted of 360 patients from two independent locations. A logistic regression algorithm was developed that incorporated log-transformed AFP values with age, gender, alkaline phosphatase, and alanine aminotransferase levels. We define this as the Doylestown algorithm. In the discovery set, the Doylestown algorithm improved the overall performance of AFP by 10%. In subsequent external validation in over 2,700 patients from three independent sites, the Doylestown algorithm improved detection of HCC as compared with AFP alone by 4% to 20%. In addition, at a fixed specificity of 95%, the Doylestown algorithm improved the detection of HCC as compared with AFP alone by 2% to 20%. In conclusion, the Doylestown algorithm consolidates clinical laboratory values, with age and gender, which are each individually associated with HCC risk, into a single value that can be used for HCC risk assessment. As such, it should be applicable and useful to the medical community that manages those at risk for developing HCC.

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