Multiphase Liver MRI for Identifying the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma

Abstract

H epatocellular carcinoma (HCC) accounts for the majority of primary liver cancers, which are the fourth leading cause of cancer-related deaths worldwide. The burden of HCC is increasing, mainly due to the rising prevalence of obesity and nonalcoholic fatty liver disease (1). Although surveillance programs have been implemented for at-risk patients (eg, patients with cirrhosis) to detect tumors at early stages and to allow for potentially curative treatment, such patients have high recurrence rates and heterogeneous recurrence-free survival (2). Indeed, HCC has strong genomic, molecular, and histologic heterogeneity (3,4). Several histologic subtypes of HCC associated with clinical and molecular features of different prognostic value have been described (5-7), among which the macrotrabecular-massive (MTM) HCC (MTM-HCC) subgroup has been recently identified (7,8). MTM-HCCs have been shown to be associated with poor survival, high serum a-fetoprotein (AFP) level, and histologic features of aggressiveness, including microvascular and macrovascular invasion and satellite nodules (8). This subtype has been newly included in the fifth edition of the World Health Organization Classification of Tumors (9).Identification of the aggressive HCC subtypes during pretherapeutic work-up may have strong prognostic and therapeutic implications (3). In patients with cirrhosis, the diagnosis of HCC may be performed noninvasively using multiphasic CT or dynamic contrast materialenhanced MRI using the Liver Imaging Reporting and Data System (LI-RADS), which provides standardization for HCC imaging acquisition and terminology and allows for accurate stratification of the probability of HCC and overall malignancy (2,10,11). As pathologic diagnosis is thus not systematically required, the identification

Preoperative CT for Characterization of Aggressive Macrotrabecular-Massive Subtype and Vessels That Encapsulate Tumor Clusters Pattern in Hepatocellular Carcinoma

“…Our results showed that intratumor necrosis was independently associated with MTM subtype and VETC pattern, in agreement with the findings reported by Mulé et al (11). However, the relatively narrow CIs of the estimated ORs in our study should be attributed to the higher prevalence of the two subtypes.…”

“…Currently, there are few studies illustrating the imaging characteristics of MTM subtype or VETC pattern in HCC. Mulé et al (11) reported that substantial necrosis at contrast-enhanced MRI was an independent predictor of MTM subtype and had high…”

Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

“…In this study, a metabolic gene prognostic model was constructed based on regression analysis to improve prognosis and guide the meticulous design of better treatments. Regression analysis including univariate COX regression analysis, least absolute shrinkage and selection operator (Lasso) regression analysis and multivariate COX regression analysis were widely used in the study of cancer prognosis such as hepatocellular carcinoma (Mule et al, 2020), lung adenocarcinoma (Zhu et al, 2020), bladder cancer (Jiang et al, 2020), and ovarian cancer (Ding et al, 2020). However, research into OSCC should be supplemented.…”

Delayed hospital admission and high-dose corticosteroids potentially prolong SARS-CoV-2 RNA detection duration of patients with COVID-19

“…For high-dimensional data, the least absolute shrinkage and selection operator (LASSO) method is more available to screen the predictive features from the primary data set compared with conventional regression analysis [ 9 – 12 ]. Tuning parameter (lambda) selection in the LASSO model uses 10-fold cross-validation [ 9 , 11 ]. By shrinking down to zero coefficient weights, LASSO regression analysis has the ability to eliminate exposures that are non-related to the outcome [ 9 , 11 ].…”

“…Tuning parameter (lambda) selection in the LASSO model uses 10-fold cross-validation [ 9 , 11 ]. By shrinking down to zero coefficient weights, LASSO regression analysis has the ability to eliminate exposures that are non-related to the outcome [ 9 , 11 ]. In the first step of our study, we identified the independent risk factors of long-term positive SARS-CoV-2 RNA detection by using LASSO logistic regression analysis and SARS-CoV-2 RNA detection duration by using LASSO Cox regression analysis.…”