A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers
Various statistical models predict the probability of developing hepatocellular carcinoma (HCC) in patients with cirrhosis, with GALAD being one of the most extensively studied scores. Biomarkers like alpha-fetoprotein (AFP), AFP-L3, and des-g-carboxyprothrombin (DCP) are widely used alone or in con...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/7/1253 |
| _version_ | 1797608153517391872 |
|---|---|
| author | Călin Burciu Roxana Șirli Renata Bende Alexandru Popa Deiana Vuletici Bogdan Miuțescu Iulia Rațiu Alina Popescu Ioan Sporea Mirela Dănilă |
| author_facet | Călin Burciu Roxana Șirli Renata Bende Alexandru Popa Deiana Vuletici Bogdan Miuțescu Iulia Rațiu Alina Popescu Ioan Sporea Mirela Dănilă |
| author_sort | Călin Burciu |
| collection | DOAJ |
| description | Various statistical models predict the probability of developing hepatocellular carcinoma (HCC) in patients with cirrhosis, with GALAD being one of the most extensively studied scores. Biomarkers like alpha-fetoprotein (AFP), AFP-L3, and des-g-carboxyprothrombin (DCP) are widely used alone or in conjunction with ultrasound to screen for HCC. Our study aimed to compare the effectiveness of Cytokeratin 19 (CK19) and Glypican-3 (GPC3) as standalone biomarkers and in a statistical model to predict the likelihood of HCC. We conducted a monocentric prospective study involving 154 participants with previously diagnosed liver cirrhosis, divided into two groups: 95 patients with confirmed HCC based on clinical, biological, and imaging features and 59 patients without HCC. We measured the levels of AFP, AFP-L3, DCP, GPC3, and CK19 in both groups. We used univariate and multivariate statistical analyses to evaluate the ability of GPC3 and CK19 to predict the presence of HCC and incorporated them into a statistical model—the GALKA score—which was then compared to the GALAD score. AFP performed better than AFP-F3, DCP, GPC3, and CK19 in predicting the presence of HCC in our cohort. Additionally, GPC3 outperformed CK19. We used multivariate analysis to compute the GALKA score to predict the presence of HCC. Using these predictors, the following score was formulated: 0.005*AFP-L3 + 0.00069*AFP + 0.000066*GPC3 + 0.01*CK19 + 0.235*Serum Albumin—0.277. The optimal cutoff was >0.32 (AUROC = 0.98, sensitivity: 96.8%, specificity: 93%, positive predictive value—95.8%, negative predictive value—94.8%). The GALKA score had a similar predictive value to the GALAD score for the presence of HCC. In conclusion, AFP, AFP-L3, and DCP were the best biomarkers for predicting the likelihood of HCC. Our score performed well overall and was comparable to the GALAD score. |
| first_indexed | 2024-03-11T05:39:29Z |
| format | Article |
| id | doaj.art-0deff8a0a44b4a13a0b9888e287de104 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:39:29Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-0deff8a0a44b4a13a0b9888e287de1042023-11-17T16:29:59ZengMDPI AGDiagnostics2075-44182023-03-01137125310.3390/diagnostics13071253A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 BiomarkersCălin Burciu0Roxana Șirli1Renata Bende2Alexandru Popa3Deiana Vuletici4Bogdan Miuțescu5Iulia Rațiu6Alina Popescu7Ioan Sporea8Mirela Dănilă9Department of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaDepartment of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timișoara, RomaniaVarious statistical models predict the probability of developing hepatocellular carcinoma (HCC) in patients with cirrhosis, with GALAD being one of the most extensively studied scores. Biomarkers like alpha-fetoprotein (AFP), AFP-L3, and des-g-carboxyprothrombin (DCP) are widely used alone or in conjunction with ultrasound to screen for HCC. Our study aimed to compare the effectiveness of Cytokeratin 19 (CK19) and Glypican-3 (GPC3) as standalone biomarkers and in a statistical model to predict the likelihood of HCC. We conducted a monocentric prospective study involving 154 participants with previously diagnosed liver cirrhosis, divided into two groups: 95 patients with confirmed HCC based on clinical, biological, and imaging features and 59 patients without HCC. We measured the levels of AFP, AFP-L3, DCP, GPC3, and CK19 in both groups. We used univariate and multivariate statistical analyses to evaluate the ability of GPC3 and CK19 to predict the presence of HCC and incorporated them into a statistical model—the GALKA score—which was then compared to the GALAD score. AFP performed better than AFP-F3, DCP, GPC3, and CK19 in predicting the presence of HCC in our cohort. Additionally, GPC3 outperformed CK19. We used multivariate analysis to compute the GALKA score to predict the presence of HCC. Using these predictors, the following score was formulated: 0.005*AFP-L3 + 0.00069*AFP + 0.000066*GPC3 + 0.01*CK19 + 0.235*Serum Albumin—0.277. The optimal cutoff was >0.32 (AUROC = 0.98, sensitivity: 96.8%, specificity: 93%, positive predictive value—95.8%, negative predictive value—94.8%). The GALKA score had a similar predictive value to the GALAD score for the presence of HCC. In conclusion, AFP, AFP-L3, and DCP were the best biomarkers for predicting the likelihood of HCC. Our score performed well overall and was comparable to the GALAD score.https://www.mdpi.com/2075-4418/13/7/1253cytokeratin 19glypican-3hepatocellular carcinomastatistical model |
| spellingShingle | Călin Burciu Roxana Șirli Renata Bende Alexandru Popa Deiana Vuletici Bogdan Miuțescu Iulia Rațiu Alina Popescu Ioan Sporea Mirela Dănilă A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers Diagnostics cytokeratin 19 glypican-3 hepatocellular carcinoma statistical model |
| title | A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers |
| title_full | A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers |
| title_fullStr | A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers |
| title_full_unstemmed | A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers |
| title_short | A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers |
| title_sort | statistical approach to the diagnosis and prediction of hcc using ck19 and glypican 3 biomarkers |
| topic | cytokeratin 19 glypican-3 hepatocellular carcinoma statistical model |
| url | https://www.mdpi.com/2075-4418/13/7/1253 |
| work_keys_str_mv | AT calinburciu astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT roxanasirli astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT renatabende astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT alexandrupopa astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT deianavuletici astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT bogdanmiutescu astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT iuliaratiu astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT alinapopescu astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT ioansporea astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT mireladanila astatisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT calinburciu statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT roxanasirli statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT renatabende statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT alexandrupopa statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT deianavuletici statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT bogdanmiutescu statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT iuliaratiu statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT alinapopescu statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT ioansporea statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers AT mireladanila statisticalapproachtothediagnosisandpredictionofhccusingck19andglypican3biomarkers |