Background: To evaluate survival rates of hepatocellular carcinoma (HCC), the Chiang Mai Cancer Registry provided characteristics data of 6276 HCC patients diagnosed between 1998-2020 based on evolution of imaging diagnosis. Evolution can be separated into four cohorts, namely, cohort 1 (1990-2005) when we had ultrasound (US) and single-phase computed tomography (CT), cohort 2 (2006-2009) when one multi-phase CT and one magnetic resonance imaging (MRI) were added, cohort 3 (2010-2015) when MRI with LI-RADS was added, and finally, cohort 4 (2016-2020) when two upgraded MRIs with LI-RADS were added.
Methods: Cox proportional hazard models were used to determine the relation between death and risk factors including methods of imagining diagnosis, gender, age of diagnosis, tumor stages, history of smoking and alcohol-use, while Kaplan-Meier curves were used to calculate survival rates.
Results: The median age of diagnosis was 57.0 years (IQR: 50.0-65.0) and the median survival time was 5.8 months (IQR: 1.9-26.8) during the follow-up period. In the univariable analysis, all factors were all associated with a higher risk of death in HCC patients except age of diagnosis. In a multivariable analysis, elderly age at diagnosis, regional and metastatic stages and advanced methods of imagining diagnosis during cohorts 2 and 3 were independently associated with the risk of death in HCC patients. The survival rate of patients diagnosed during cohort 4 was significantly higher than the other cohorts.
Conclusion: As a significantly increasing survival rate of HCC patients in cohort 4, advanced methods of diagnostic imaging can be a part of the recommendation to diagnose HCC.
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http://dx.doi.org/10.1186/s12885-023-11429-6 | DOI Listing |
Advances in imaging techniques have evolved, allowing for early noninvasive diagnosis and improved management of high-risk patients with hepatocellular carcinoma (HCC). The hallmark imaging features of HCC on multiphasic cross-sectional imaging can be explained by the multistep process of hepatocarcinogenesis and is seen in 60% of cases. However, approximately 40% of cases do not abide by the classic imaging appearance and may pose a diagnostic challenge for radiologists.
View Article and Find Full Text PDFCell Death Differ
January 2025
Department of Hepatobiliary Surgery of the affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, China.
Lysine lactylation plays critical roles in various diseases, including cancer. Our previous study showed that lactylation of non-histone ABCF1 may be involved in hepatocellular carcinoma (HCC) progression. In this study, we evaluated the prognostic value of ABCF1-K430la in HCC using immunohistochemical staining and performed amino acid point mutations, multi-omics crossover, and biochemical experiments to investigate its biological role and underlying mechanism.
View Article and Find Full Text PDFSci Rep
January 2025
Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Cairo, 11884, Egypt.
Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality globally due to HCC late diagnosis and limited treatment options. MiRNAs (miRNAs) emerged as potential biomarkers for various diseases, including HCC. However, the value of miRNA-101 as a serum biomarker for HCV-induced HCC has not been fully investigated.
View Article and Find Full Text PDFNPJ Vaccines
January 2025
First Department of Hepatobiliary Surgery, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Hepatocellular carcinoma (HCC) is a highly prevalent malignancy with limited treatment efficacy despite advances in immune checkpoint blockade (ICB) therapy. The inherently weak immune responses in HCC necessitate novel strategies to improve anti-tumor immunity and synergize with ICB therapy. Kinesin family member 20A (KIF20A) is a tumor-associated antigen (TAA) overexpressed in HCC, and it could be a promising target for vaccine development.
View Article and Find Full Text PDFHPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States. Electronic address:
Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.
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