Hepatobiliary phase hypointensity predicts progression to hepatocellular carcinoma for intermediate-high risk observations, but not time to progression

2020 ◽  
Vol 128 ◽  
pp. 109018
Author(s):  
Federica Vernuccio ◽  
Roberto Cannella ◽  
Kingshuk Roy Choudhury ◽  
Mathias Meyer ◽  
Alessandro Furlan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Hepatobiliary Phase ◽  
Time To Progression

scholarly journals Importance of Microvascular Invasion Risk and Tumor Size on Recurrence and Survival of Hepatocellular Carcinoma After Anatomical Resection and Non-anatomical Resection

2021 ◽  
Vol 11 ◽  
Author(s):  
Haoyu Hu ◽  
Shuo Qi ◽  
Silue Zeng ◽  
Peng Zhang ◽  
Linyun He ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Predictive Model ◽  
Tumor Size ◽  
Microvascular Invasion ◽  
Low Risk ◽  
Hepatobiliary Phase ◽  
Imaging Features ◽  
Anatomical Resection ◽  
Anatomic Resection

Purpose: To establish a valid prediction model to prognose the occurrence of microvascular invasion (MVI), and to compare the efficacy of anatomic resection (AR) or non-anatomic resection (NAR) for hepatocellular carcinoma (HCC).Methods: Two hundred twenty-eight patients with HCC who underwent surgical treatment were enrolled. Their hematological indicators, MRI imaging features, and outcome data were acquired.Result: In the multivariable analysis, alpha-fetoprotein >15 ng/mL, neutrophil to lymphocyte ratio >3.8, corona enhancement, and peritumoral hypointensity on hepatobiliary phase were associated with MVI. According on these factors, the AUROC of the predictive model in the primary and validation cohorts was 0.884 (95% CI: 0.829, 0.938) and 0.899 (95% CI: 0.821, 0.967), respectively. Patients with high risk of MVI or those with low risk of MVI but tumor size >5 cm in the AR group were associated with a lower rate of recurrence and death than patients in the NAR group; however, when patients are in the state of low-risk MVI with tumor size >5 cm, there is no difference in the rate of recurrence and death between AR and NAR.Conclusion: Our predictive model for HCC with MVI is convenient and accurate. Patients with high-risk of MVI or low-risk of MVI but tumor size >5 cm executing AR is of great necessity.

Early Detection of Hepatocellular Carcinoma Requires a Combined Surveillance Protocol in High-Risk Individuals

Endoscopy ◽  
2006 ◽  
Vol 38 (11) ◽  
Author(s):  
A Qasim ◽  
T Tajjudin ◽  
B Zaman ◽  
D Maguire ◽  
J Geoghegan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Early Detection ◽  
Surveillance Protocol

scholarly journals MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Liver Cancer ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 94-106
Author(s):  
Seung Baek Hong ◽  
Sang Hyun Choi ◽  
So Yeon Kim ◽  
Ju Hyun Shim ◽  
Seung Soo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Meta Analysis ◽  
Diagnostic Odds Ratio ◽  
Microvascular Invasion ◽  
Hepatobiliary Phase ◽  
Phase Imaging ◽  
Arterial Enhancement ◽  
Mri Features ◽  
Peritumoral Enhancement

<b><i>Purpose:</i></b> Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value. <b><i>Methods:</i></b> Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features. <b><i>Results:</i></b> Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (&#x3e;5 cm) (DOR = 5.2, 95% CI [3.0–9.0]), rim arterial enhancement (4.2, 95% CI [1.7–10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8–6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4–15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2–4.4]), multifocality (7.1, 95% CI [2.6–19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5–9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4–94.8%] and 93.3% [74.5–98.5%], respectively). <b><i>Conclusions:</i></b> Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

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