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New Screening Tool Could Improve the Survival Rate of Patients with Hepatocellular Carcinoma from 20% to 90%

June 17, 2024

A breakthrough study published in The American Journal of Pathology describes a new machine-learning model that may improve accuracy in early diagnosis of hepatocellular carcinoma and monitoring the impact of treatment.

Early diagnosis of hepatocellular carcinoma (HCC)—one of the most fatal malignancies—is crucial to improve patient survival. In a breakthrough studyopens in new tab/window investigators report on the development of a serum fusion-gene machine-learning model. This important screening tool may increase the five-year survival rate of patients with HCC from 20% to 90% because of its improved accuracy in early diagnosis of HCC and monitoring the impact of treatment. The study appears in The American Journal of Pathologyopens in new tab/window, published by Elsevier. 

HCC is the most common form of liver cancer and accounts for around 90% of cases. Currently, the most common screening test for the HCC biomarker, serum alpha-fetal protein, is not always accurate, and up to 60% of liver cancers are only diagnosed in advanced stages, resulting in a survival rate of only around 20%. 

Lead investigator Jian-Hua Luo, MD, PhD, Department of Pathology, High Throughput Genome Center, and Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, explained: “Early diagnosis of liver cancer helps save lives. However, most liver cancers occur insidiously and without many symptoms. This makes early diagnosis challenging. What we need is a cost-effective, accurate, and convenient test to screen early-stage liver cancer in human populations. We wanted to explore if a machine-learning approach could be used to increase the accuracy of screening for HCC based on the status of the fusion genes."

In the search for a more effective and efficient diagnostic tool to predict non-HCC and HCC cases, investigators analyzed a panel of nine fusion transcripts in serum samples from 61 patients with HCC and 75 patients with non-HCC conditions using real-time quantitative reverse transcription PCR (RT-PCR). Seven of the nine fusions were frequently detected in HCC patients. The researchers generated machine-learning models based on serum fusion-gene levels to predict HCC in the training cohort, using the leave-one-out cross-validation approach.  

A four fusion gene logistic regression model produced an accuracy of 83% to 91% in predicting the occurrence of HCC. When combined with serum alpha-fetal protein, the two-fusion gene plus alpha-fetal protein logistic regression model produced 95% accuracy for all the cohorts. Furthermore, quantification of fusion gene transcripts in the serum samples accurately assessed the impact of the treatment and was able to monitor for the recurrence of the cancer. 

Caption: Investigators used serum fusion transcripts to assess the risk of hepatocellular carcinoma (HCC) and the impact of cancer treatment through machine learning (Credit: The American Journal of Pathology). 

Dr. Luo commented, “The fusion gene machine-learning model significantly improves the early detection rate of HCC over the serum alpha-fetal protein alone. It may serve as an important tool in screening for HCC and in monitoring the impact of HCC treatment. This test will find patients who are likely to have HCC.”

Dr. Luo concluded, “Early treatment of liver cancer has a 90% five-year survival rate, while late treatment has only 20%. The alternative to this test is to subject every individual with some risk of liver cancer to imaging analysis every six months, which is very costly and ineffective. In addition, when imaging results are ambiguous, this test will help to differentiate malignant versus benign lesions.”

Notes for editors 

The article is “Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment Through Machine Learning,” by Yan-Ping Yu, Silvia Liu, David Geller, and Jian-Hua Luo ( in new tab/window). It appears in The American Journal of Pathology, volume 194, issue 7 (July 2024), published by Elsevier

The article is openly available for 30 days at in new tab/window.  

Full text of the article is also available to credentialed journalists upon request. Contact Eileen Leahy at +1 732 406 1313 or [email protected]opens in new tab/window to request a PDF of the article or more information. To reach the study’s authors contact Jian-Hua Luo, MD, PhD, at [email protected]opens in new tab/window.  

This study was supported in part by National Cancer Institute grant 1R56CA229262-01, a grant from the Clinical Translational Science Institute of University of Pittsburgh, and National Institute of Diabetes, Digestive and Kidney Diseases grant P30-DK120531-01. 

About The American Journal of Pathology 

The American Journal of Pathologyopens in new tab/window, official journal of the American Society for Investigative Pathologyopens in new tab/window, published by Elsevier, seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches. https://ajp.amjpathol.orgopens in new tab/window 

About Elsevier

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Eileen Leahy


+1 732 406 1313

E-mail Eileen Leahy


Chhavi Chauhan, PhD

Director of Scientific Outreach

The American Journal of Pathology

+1 240 283 9724

E-mail Chhavi Chauhan, PhD