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

Nomogram Estimating Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma From Preoperative Gadoxetate Disodium-Enhanced MRI

BACKGROUND

Vessels encapsulating tumor clusters (VETC) pattern is a novel microvascular pattern associated with poor outcomes of hepatocellular carcinoma (HCC). Preoperative estimation of VETC has potential to improve treatment decisions.

PURPOSE

To develop and validate a nomogram based on gadoxetate disodium-enhanced MRI for estimating VETC in HCC and to evaluate whether the estimations are associated with recurrence after hepatic resection.

STUDY TYPE

Retrospective.

POPULATION

A total of 320 patients with HCC and histopathologic VETC pattern assessment from three centers (development cohort:validation cohort = 173:147).

FIELD STRENGTH/SEQUENCE

A3.0  T/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, and 3D T1-weighted gradient-echo sequences.

ASSESSMENT

A set of previously reported VETC- and/or prognosis-correlated qualitative and quantitative imaging features were assessed. Clinical and imaging variables were compared based on histopathologic VETC status to investigate factors indicating VETC pattern. A regression-based nomogram was then constructed using the significant factors for VETC pattern. The nomogram-estimated VETC stratification was assessed for its association with recurrence.

STATISTICAL TESTS

Fisher exact test, t-test or Mann-Whitney test, logistic regression analyses, Harrell's concordance index (C-index), nomogram, Kaplan-Meier curves and log-rank tests. P value < 0.05 was considered statistically significant.

RESULTS

Pathological VETC pattern presence was identified in 156 patients (development cohort:validation cohort = 83:73). Tumor size, presence of heterogeneous enhancement with septations or with irregular ring-like structures, and necrosis were significant factors for estimating VETC pattern. The nomogram incorporating these indicators showed good discrimination with a C-index of 0.870 (development cohort) and 0.862 (validation cohort). Significant differences in recurrence rates between the nomogram-estimated high-risk VETC group and low-risk VETC group were found (2-year recurrence rates, 50.7% vs. 30.3% and 49.6% vs. 31.8% in the development and validation cohorts, respectively).

DATA CONCLUSION

The nomogram integrating gadoxetate disodium-enhanced MRI features was associated with VETC pattern preoperatively and with postoperative recurrence in patients with HCC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Prognostic impact of vessels encapsulating tumor clusters and macrotrabecular patterns in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) shows a high mortality rate. A macrotrabecular (MT) pattern and vessels encapsulating tumor clusters (VETC) pattern have been reported as aggressive histological patterns in HCC. However, their cut-off values have been contentious.

METHOD

Nine hundred eighty-five cases of previously diagnosed HCC were enrolled. The percentage areas of the MT and/or VETC pattern with ≥ 5% at every 10% increment were assessed. Clinicopathological analysis including patients' prognosis was conducted.

RESULT

One hundred fifty-eight and eighty-four cases were accompanied by 5-49% and ≥ 50% MT components, respectively. Two hundred six and twenty-nine cases had 5-49% and ≥ 50% VETC components, respectively. Cases with these histological patterns in common had aggressive characteristics and worse prognosis compared to cases with none of these patterns. The presence of 5-49% VETC pattern was independent worse prognostic factor in overall survival (P = 0.046). HCCs with the MT pattern and the VETC pattern were significantly accompanied by the VETC pattern and the MT pattern (P < 0.001), respectively.

CONCLUSION

As even 5% of the MT pattern and/or VETC pattern affected the prognosis of patients with HCC, the amount of these pattern should be described in pathological reports. This information could be useful in expecting patients' prognosis and providing proper post-operative treatments.

Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning

Background

Postoperative recurrence impedes the curability of early-stage hepatocellular carcinoma (E-HCC). We aimed to establish a novel recurrence-related pathological prognosticator with artificial intelligence, and investigate the relationship between pathological features and the local immunological microenvironment.

Methods

A total of 576 whole-slide images (WSIs) were collected from 547 patients with E-HCC in the Zhongshan cohort, which was randomly divided into a training cohort and a validation cohort. The external validation cohort comprised 147 Tumor Node Metastasis (TNM) stage I patients from The Cancer Genome Atlas (TCGA) database. Six types of HCC tissues were identified by a weakly supervised convolutional neural network. A recurrence-related histological score (HS) was constructed and validated. The correlation between immune microenvironment and HS was evaluated through extensive immunohistochemical data.

Results

The overall classification accuracy of HCC tissues was 94.17%. The C-indexes of HS in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that the HS (HR= 4.05, 95% CI: 3.40-4.84) was an independent predictor for recurrence-free survival. Patients in HS high-risk group had elevated preoperative alpha-fetoprotein levels, poorer tumor differentiation and a higher proportion of microvascular invasion. The immunohistochemistry data linked the HS to local immune cell infiltration. HS was positively correlated with the expression level of peritumoral CD14+ cells (p= 0.013), and negatively with the intratumoral CD8+ cells (p&lt; 0.001).

Conclusions

The study established a novel histological score that predicted short-term and long-term recurrence for E-HCCs using deep learning, which could facilitate clinical decision making in recurrence prediction and management.

Nomogram for the Preoperative Prediction of the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma

Background

The macrotrabecular-massive subtype of hepatocellular carcinoma (MTM-HCC) is an aggressive histological type and results in poor prognosis. We developed a nomogram model based on laboratory results to predict the presence of MTM-HCC.

Methods

A total of 357 HCC patients who underwent radical surgery between January 2015 and December 2020 at Ningbo Medical Center Lihuili Hospital were grouped according to histological type. After propensity score matching (PSM), 267 patients were divided into MTM-HCC (n = 76) and non-MTM-HCC (n = 191) groups. A LASSO regression analysis model was used to select predictive factors. Finally, a nomogram for predicting the presence of MTM-HCC was established. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities.

Results

The 1-, 3-, and 5-year disease-free survival (DFS) and overall survival (OS) rates for MTM-HCC were 60.0%, 36.0%, 32.4% and 92.1%, 68.7%, 52.2%, respectively. Survival analysis indicated that the probabilities of achieving DFS and OS were significantly worse in the MTM-HCC group than in the non-MTM-HCC group (P < 0.05). The nomogram model that included AST levels, PT and AFP levels achieved a better C-index of 0.723 (95% CI: 0.659-0.787). DCA revealed that the nomogram model could lead to net benefits and exhibited a wider range of threshold probabilities in the prediction of MTM-HCC.

Conclusion

The nomogram model included AST, PT and AFP could achieve an optimal performance in the preoperative prediction of MTM-HCC.

Current Imaging Diagnosis of Hepatocellular Carcinoma

Simple Summary

The role of imaging in the management of hepatocellular carcinoma (HCC) has significantly evolved and expanded beyond the plain radiological confirmation of the tumor based on the typical appearance in a multiphase contrast-enhanced CT or MRI examination. The introduction of hepatobiliary contrast agents has enabled the diagnosis of hepatocarcinogenesis at earlier stages, while the application of ultrasound contrast agents has drastically upgraded the role of ultrasound in the diagnostic algorithms. Newer quantitative techniques assessing blood perfusion on CT and MRI not only allow earlier diagnosis and confident differentiation from other lesions, but they also provide biomarkers for the evaluation of treatment response. As distinct HCC subtypes are identified, their correlation with specific imaging features holds great promise for estimating tumor aggressiveness and prognosis. This review presents the current role of imaging and underlines its critical role in the successful management of patients with HCC.

Abstract

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer related death worldwide. Radiology has traditionally played a central role in HCC management, ranging from screening of high-risk patients to non-invasive diagnosis, as well as the evaluation of treatment response and post-treatment follow-up. From liver ultrasonography with or without contrast to dynamic multiple phased CT and dynamic MRI with diffusion protocols, great progress has been achieved in the last decade. Throughout the last few years, pathological, biological, genetic, and immune-chemical analyses have revealed several tumoral subtypes with diverse biological behavior, highlighting the need for the re-evaluation of established radiological methods. Considering these changes, novel methods that provide functional and quantitative parameters in addition to morphological information are increasingly incorporated into modern diagnostic protocols for HCC. In this way, differential diagnosis became even more challenging throughout the last few years. Use of liver specific contrast agents, as well as CT/MRI perfusion techniques, seem to not only allow earlier detection and more accurate characterization of HCC lesions, but also make it possible to predict response to treatment and survival. Nevertheless, several limitations and technical considerations still exist. This review will describe and discuss all these imaging modalities and their advances in the imaging of HCC lesions in cirrhotic and non-cirrhotic livers. Sensitivity and specificity rates, method limitations, and technical considerations will be discussed.

Variant Hepatocellular Carcinoma Subtypes According to the 2019 WHO Classification: An Imaging-Focused Review

The 2019 5th edition of the WHO classification of digestive system tumors estimates that up to 35% of hepatocellular carcinomas (HCCs) can be classified as one of eight subtypes defined by molecular characteristics: steatohepatitic, clear cell, macrotrabecular-massive, scirrhous, chromophobe, fibrolamellar, neutrophil-rich, and lymphocyte-rich HCCs. Due to their distinct cellular and architectural characteristics, these subtypes may not display arterial phase hyperenhancement and washout appearance, which are the classic MRI features of HCC, creating challenges in noninvasively diagnosing such lesions as HCC. Moreover, certain subtypes with atypical imaging features have a worse prognosis than other HCCs. A range of distinguishing imaging features may help raise suspicion that a liver lesion represents one of these HCC subtypes. In this review, we describe the MRI features that have been reported in association with various HCC subtypes according to the 2019 WHO classification, with attention given to the current understanding of these subtypes' pathologic and molecular bases and relevance to clinical practice. Imaging findings that differentiate the subtypes from benign liver lesions and non-HCC malignancies are highlighted. Familiarity with these sub-types and their imaging features may allow the radiologist to suggest their presence, though histologic analysis remains needed to establish the diagnosis.

MRI features of histologic subtypes of hepatocellular carcinoma: correlation with histologic, genetic, and molecular biologic classification

HCC is a heterogeneous group of tumors in terms of histology, genetic aberration, and protein expression. Advancements in imaging techniques have allowed imaging diagnosis to become a critical part of managing HCC in the clinical setting, even without pathologic diagnosis. With the identification of many HCC subtypes, there is increasing correlative evidence between imaging phenotypes and histologic, molecular, and genetic characteristics of various HCC subtypes. In this review, current knowledge of histologic heterogeneity of HCC correlated to features on gadolinium-enhanced dynamic liver MRI will be discussed. In addition, HCC subtype classification according to transcriptomic profiles will be outlined with descriptions of histologic, genetic, and molecular characteristics of some relatively well-established morphologic subtypes, namely the low proliferation class (steatohepatitic HCC and CTNNB1-mutated HCC) and the high proliferation class (macrotrabecular-massive HCC (MTM-HCC), scirrhous HCC, and CK19-positive HCC). Characteristics of sarcomatoid HCC and fibrolamellar HCC will also be discussed. Further research on radiological characteristics of HCC subtypes may ultimately enable non-invasive diagnosis and serve as a biomarker in predicting prognosis, molecular characteristics, and therapeutic response. In the era of precision medicine, a multidisciplinary effort to develop an integrated radiologic and clinical diagnostic system of various HCC subtypes is necessary. KEY POINTS: • HCC is a heterogeneous group of tumors in terms of histology, genetic aberration, and protein expression, which can be divided into many subtypes according to transcriptome profiles. • There is increasing evidence of a correlation between imaging phenotypes and histologic, genetic, and molecular biologic characteristics of various HCC subtypes. • Imaging characteristics may ultimately enable non-invasive diagnosis and subtype characterization, serving as a biomarker for predicting prognosis, molecular characteristics, and therapeutic response.

Morphomolecular Classification Update on Hepatocellular Adenoma, Hepatocellular Carcinoma, and Intrahepatic Cholangiocarcinoma

Hepatocellular adenomas (HCAs), hepatocellular carcinomas (HCCs), and intrahepatic cholangiocarcinomas (iCCAs) are a highly heterogeneous group of liver tumors with diverse pathomolecular features and prognoses. High-throughput gene sequencing techniques have allowed discovery of distinct genetic and molecular underpinnings of these tumors and identified distinct subtypes that demonstrate varied clinicobiologic behaviors, imaging findings, and complications. The combination of histopathologic findings and molecular profiling form the basis for the morphomolecular classification of liver tumors. Distinct HCA subtypes with characteristic imaging findings and complications include HNF1A-inactivated, inflammatory, β-catenin-activated, β-catenin-activated inflammatory, and sonic hedgehog HCAs. HCCs can be grouped into proliferative and nonproliferative subtypes. Proliferative HCCs include macrotrabecular-massive, TP53-mutated, scirrhous, clear cell, fibrolamellar, and sarcomatoid HCCs and combined HCC-cholangiocarcinoma. Steatohepatitic and β-catenin-mutated HCCs constitute the nonproliferative subtypes. iCCAs are classified as small-duct and large-duct types on the basis of the level of bile duct involvement, with significant differences in pathogenesis, molecular signatures, imaging findings, and biologic behaviors. Cross-sectional imaging modalities, including multiphase CT and multiparametric MRI, play an essential role in diagnosis, staging, treatment response assessment, and surveillance. Select imaging phenotypes can be correlated with genetic abnormalities, and identification of surrogate imaging markers may help avoid genetic testing. Improved understanding of morphomolecular features of liver tumors has opened new areas of research in the targeted therapeutics and management guidelines. The purpose of this article is to review imaging findings of select morphomolecular subtypes of HCAs, HCCs, and iCCAs and discuss therapeutic and prognostic implications. Online supplemental material is available for this article. ^©RSNA, 2022.

Macrotrabecular-Massive Hepatocellular Carcinoma: Light and Shadow in Current Knowledge

The subject of this narrative review is macrotrabecular-massive hepatocellular carcinoma (MTM‐HCC). Despite their rarity, these tumours are of general interest because of their epidemiological and clinical features and for representing a distinct model of the interaction between the angiogenetic system and neoplastic cells. The MTM‐HCC subtype is associated with various adverse biological and pathological parameters (the Alfa-foetoprotein (AFP) serum level, tumour size, vascular invasion, and satellite nodules) and is a key determinant of patient prognosis, with a strong and independent predictive value for early and overall tumour recurrence. Gene expression profiling has demonstrated that angiogenesis activation is a hallmark feature of MTM-HCC, with overexpression of both angiopoietin 2 (ANGPT2) and vascular endothelial growth factor A (VEGFA).

Evaluation of perfusion CT and dual-energy CT for predicting microvascular invasion of hepatocellular carcinoma

PURPOSE

Evaluation of perfusion CT and dual-energy CT (DECT) quantitative parameters for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) prior to surgery.

METHODS

This prospective single-center study included fifty-six patients (44 men; median age 67; range 31-84) who provided written informed consent. Inclusion criteria were (1) treatment-naïve patients with a diagnosis of HCC, (2) an indication for hepatic resection, and (3) available arterial DECT phase and perfusion CT (GE revolution HD-GSI). Iodine concentrations (IC), arterial density (AD), and 9 quantitative perfusion parameters for HCC were correlated to pathological results. Radiological parameters based principal component analysis (PCA), corroborated by unsupervised heatmap classification, was meant to deliver a model for predicting MVI in HCC. Survival analysis was performed using univariable log-rank test and multivariable Cox model, both censored at time of relapse.

RESULTS

58 HCC lesions were analyzed (median size 42.3 mm; range of 20-140). PCA showed that the radiological model was predictive of tumor grade (p = 0.01), intratumoral MVI (p = 0.004), peritumoral MVI (p = 0.04), MTM (macrotrabecular-massive) subtype (p = 0.02), and capsular invasion (p = 0.02) in HCC. Heatmap classification of HCC showed tumor heterogeneity, stratified into three main clusters according to the risk of relapse. Survival analysis confirmed that permeability surface-area product (PS) was the only significant independent parameter, among all quantitative tumoral CT parameters, for predicting a risk of relapse (Cox p value = 0.004).

CONCLUSION

A perfusion CT and DECT-based quantitative imaging profile can provide a diagnosis of histological MVI in HCC. PS is an independent parameter for relapse.

CLINICAL TRIALS

ClinicalTrials.gov: NCT03754192.

A gadoxetic acid-enhanced MRI-based multivariable model using LI-RADS v2018 and other imaging features for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma

PURPOSE

To identify imaging features of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) using LI-RADS v2018 and other imaging features and to develop a gadoxetic acid-enhanced MRI (EOB-MRI)-based model for pretreatment prediction of MTM-HCC.

MATERIALS AND METHODS

A total of 93 patients with pathologically proven HCC (39 MTM-HCC and 54 non-MTM-HCC) were retrospectively evaluated with EOB-MRI at 3 T. Imaging analysis according to LI-RADS v2018 was evaluated by two readers. Univariate and multivariate analyses were performed to determine independent predictors for MTM-HCC. Different logistic regression models were built based on MRI features, including model A (enhancing capsule, blood products in mass and ascites), model B (enhancing capsule and ascites), model C (blood products in mass and ascites), and model D (blood products in mass and enhancing capsule). Diagnostic performance was assessed by receiver operating characteristic (ROC) curves.

RESULTS

After multivariate analysis, absence of enhancing capsule (odds ratio = 0.102, p = 0.010), absence of blood products in mass (odds ratio = 0.073, p = 0.030), and with ascites (odds ratio = 55.677, p = 0.028) were identified as independent differential factors for the presence of MTM-HCC. Model A yielded a sensitivity, specificity, and AUC of 35.90% (21.20,52.80), 94.44% (84.60, 98.80), and 0.731 (0.629, 0.818). Model A achieved a comparable AUC than model D (0.731 vs. 0.699, p = 0.333), but a higher AUC than model B (0.731 vs. 0.644, p = 0.048) and model C (0.731 vs. 0.650, p = 0.005).

CONCLUSION

The EOB-MRI-based model is promising for noninvasively predicting MTM-HCC and may assist clinicians in pretreatment decisions.

Macrotrabecular-Massive Hepatocellular Carcinoma: What Should We Know?

Hepatocellular carcinoma is one of the most common malignancies globally. Recently, a newly identified histological subtype, designated as "macrotrabecular-massive hepatocellular carcinoma" (MTM-HCC), has been associated with an aggressive phenotype and has received extensive attention. MTM-HCC was a strong independent prognostic predictor of early and overall recurrence because it is closely related to tumor molecular subclass, gene mutation, carcinogenesis pathways, and immunohistochemical markers. In addition, preoperative imaging examination can potentially provide an essential clue for diagnosing MTM-HCC, intratumor necrosis or ischemia is an independent predictor for MTM-HCC on Gd-EOB-DTPA enhanced MRI or CT. Early diagnosis and appropriate treatment of MTM-HCC could prove beneficial for preventing early recurrence and could improve outcomes.

Patient Selection for Downstaging of Hepatocellular Carcinoma Prior to Liver Transplantation—Adjusting the Odds?

Background and Aims: Morphometric features such as the Milan criteria serve as standard criteria for liver transplantation (LT) in patients with hepatocellular carcinoma (HCC). Since it has been recognized that these criteria are too restrictive and do not adequately display the tumor biology, additional selection parameters are emerging.

Methods: Concise review of the current literature on patient selection for downstaging and LT for HCC outside the Milan criteria.

Results: The major task in patients outside the Milan criteria is the need for higher granularity with patient selection, since the benefit through LT is not uniform. The recent literature clearly shows that beneath tumor size and number, additional selection parameters are useful in the process of patient selection for and during downstaging. For initial patient selection, the alpha fetoprotein (AFP) level adds additional information to the size and number of HCC nodules concerning the chance of successful downstaging and LT. This effect is quantifiable using newer selection tools like the WE (West-Eastern) downstaging criteria or the Metroticket 2.0 criteria. Also an initial PET-scan and/or tumor biopsy can be helpful, especially in the high risk group of patients outside the University of California San Francisco (UCSF) criteria. After this entry selection, the clinical course during downstaging procedures concerning the tumor and the AFP response is of paramount importance and serves as an additional final selection tool.

Conclusion: Selection criteria for liver transplantation in HCC patients are becoming more and more sophisticated, but are still imperfect. The implementation of molecular knowledge will hopefully support a more specific risk prediction for HCC patients in the future, but do not provide a profound basis for clinical decision-making at present.

Reproducibility for Hepatocellular Carcinoma CT Radiomic Features: Influence of Delineation Variability Based on 3D-CT, 4D-CT and Multiple-Parameter MR Images

Purpose

Accurate lesion segmentation is a prerequisite for radiomic feature extraction. It helps to reduce the features variability so as to improve the reporting quality of radiomics study. In this research, we aimed to conduct a radiomic feature reproducibility test of inter-/intra-observer delineation variability in hepatocellular carcinoma using 3D-CT images, 4D-CT images and multiple-parameter MR images.

Materials and Methods

For this retrospective study, 19 HCC patients undergoing 3D-CT, 4D-CT and multiple-parameter MR scans were included in this study. The gross tumor volume (GTV) was independently delineated twice by two observers based on contrast-enhanced computed tomography (CECT), maximum intensity projection (MIP), LAVA-Flex, T2W FRFSE and DWI-EPI images. We also delineated the peritumoral region, which was defined as 0 to 5 mm radius surrounding the GTV. 107 radiomic features were automatically extracted from CECT images using 3D-Slicer software. Quartile coefficient of dispersion (QCD) and intraclass correlation coefficient (ICC) were applied to assess the variability of each radiomic feature. QCD<10% and ICC≥0.75 were considered small variations and excellent reliability. Finally, the principal component analysis (PCA) was used to test the feasibility of dimensionality reduction.

Results

For tumor tissues, the numbers of radiomic features with QCD<10% indicated no obvious inter-/intra-observer differences or discrepancies in 3D-CT, 4D-CT and multiple-parameter MR delineation. However, the number of radiomic features (mean 89) with ICC≥0.75 was the highest in the multiple-parameter MR group, followed by the 3DCT group (mean 77) and the MIP group (mean 73). The peritumor tissues also showed similar results. A total of 15 and 7 radiomic features presented excellent reproducibility and small variation in tumor and peritumoral tissues, respectively. Two robust features showed excellent reproducibility and small variation in tumor and peritumoral tissues. In addition, the values of the two features both represented statistically significant differences among tumor and peritumoral tissues (P<0.05). The PCA results indicated that the first seven principal components could preserve at least 90% of the variance of the original set of features.

Conclusion

Delineation on multiple-parameter MR images could help to improve the reproducibility of the HCC CT radiomic features and weaken the inter-/intra-observer influence.

Prognostic Nomogram and Therapeutic Option of Cancer-Specific Death in the Patients with Metachronous Second Primary Lung Cancer

With the increase of long-term primary lung cancer survivors, studies focused on metachronous second primary lung cancer (SPLC) have become very urgent. This study aimed to develop a prognostic nomogram and determine therapeutic options of cancer-specific death for patients with metachronous SPLC with and without the competing risk of other-specific death. Study population came from the SEER-18 database between 2006 and 2016. According to the clinical practice guideline of SPLC, the interval time of IPLC and metachronous SPLC was set to 4 years. We constructed nomograms with Lasso + Cox regression model and competing risk model to predict the prognosis and identify therapeutic options of metachronous SPLC patients with the assessment of model performance by the C-index, calibration plot, and decision curve analysis. In addition, two subgroup analyses stratified by histology and tumor size were used to better select therapeutic options for a certain population. 1300 patients with metachronous SPLC were incorporated in this study with 50.1% of the 5-year cumulative incidence in cancer-specific death. Compared with Lasso + Cox regression analysis, competing risk analysis harbored a higher C-index (0.811 vs. 0.76) and better net benefit in predicting cancer-specific death of metachronous SPLC. Two statistical analyses suggested that surgery alone was a preferentially therapeutic option of metachronous SPLC, whereas the effect of surgery + radiation in treating metachronous SPLC was similar to radiation alone. Subgroup analyses indicated that patients with metachronous SPLC were considered receiving different therapeutic options in different histology and tumor size but preferred to receive surgical treatment as the first choice. For primary lung cancer survivors, aggressive surgical treatment was the first-line selection of metachronous SPLC, followed by radiation alone, surgery + radiation, and no surgery + radiation.

The Landscape Of Alpha Fetoprotein In Hepatocellular Carcinoma: Where Are We?

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and has been acknowledged as a leading cause of death among cirrhosis patients. Difficulties in early diagnosis and heterogeneity are obstacles to effective treatment, especially for advanced HCC. Liver transplantation (LT) is considered the best therapy for HCC. Although many biomarkers are being proposed, alpha-fetoprotein (AFP), which was identified over 60 years ago, remains the most utilized. Recently, much hope has been placed in the immunogenicity of AFP to develop novel therapies, such as AFP vaccines and AFP-specific adoptive T-cell transfer (ACT). This review summarizes the performance of AFP as a biomarker for HCC diagnosis and prognosis, as well as its correlation with molecular classes. In addition, the role of AFP in LT is also described. Finally, we highlight the mechanism and application prospects of two immune therapies (AFP vaccine and ACT) for HCC. In general, our review points out the prevalence of AFP in HCC, accompanied by some controversies and novel directions for future research.

Imaging features of histological subtypes of hepatocellular carcinoma: Implication for LI-RADS

Background & Aims

The histopathological subtypes of hepatocellular carcinoma (HCC) are associated with distinct clinical features and prognoses. This study aims to report Liver Imaging Reporting and Data System (LI-RADS)-defined imaging features of different HCC subtypes in a cohort of resected tumours and to assess the influence of HCC subtypes on computed tomography (CT)/magnetic resonance imaging (MRI) LI-RADS categorisation in the subgroup of high-risk patients.

Methods

This retrospective institutional review board-approved study included patients with resected HCCs and available histopathological classification. Three radiologists independently reviewed preoperative CT and MRI exams. The readers evaluated the presence of imaging features according to LI-RADS v2018 definitions and provided a LI-RADS category in patients at high risk of HCC. Differences in LI-RADS features and categorisations were assessed for not otherwise specified (NOS-HCC), steatohepatitic (SH-HCC), and macrotrabecular-massive (MTM-HCC) types of HCCs.

Results

Two hundred and seventy-seven patients (median age 64.0 years, 215 [77.6%] men) were analysed, which involved 295 HCCs. There were 197 (66.7%) NOS-HCCs, 62 (21.0%) SH-HCCs, 23 (7.8%) MTM-HCCs, and 13 (4.5%) other rare subtypes. The following features were more frequent in MTM-HCC: elevated α-foetoprotein serum levels (p <0.001), tumour-in-vein (p <0.001 on CT, p ≤0.052 on MRI), presence of at least 1 LR-M feature (p ≤0.010 on CT), infiltrative appearance (p ≤0.032 on CT), necrosis or severe ischaemia (p ≤0.038 on CT), and larger size (p ≤0.006 on CT, p ≤0.011 on MRI). SH-HCC was associated with fat in mass (p <0.001 on CT, p ≤0.002 on MRI). The distribution of the LI-RADS major features and categories in high-risk patients did not significantly differ among the 3 main HCC subtypes.

Conclusions

The distribution of LI-RADS major features and categories is not different among the HCC subtypes. Nevertheless, careful analysis of tumour-in-vein, LR-M, and ancillary features as well as clinico-biological data can provide information for the non-invasive diagnosis of HCC subtypes.

Lay summary

In high-risk patients, the overall distribution of LI-RADS major features and categories is not different among the histological subtypes of hepatocellular carcinoma, but tumour-in-vein, presence of LR-M features, and ancillary features can provide information for the non-invasive diagnosis of hepatocellular carcinoma subtypes.

•

The distribution of the major features and categories of LI-RADS is not different among the HCC histological subtypes.

•

MTM-HCC was associated with TIV, ≥1 LR-M feature, infiltrative appearance, necrosis or severe ischaemia, and larger size.

•

Steatohepatitis-related HCC was associated with fat in mass on CT and on MRI.

Albumin-Bilirubin (ALBI) and Monocyte to Lymphocyte Ratio (MLR)-Based Nomogram Model to Predict Tumor Recurrence of AFP-Negative Hepatocellular Carcinoma

Purpose

In this study, we aimed to develop a novel liver function and inflammatory markers-based nomogram to predict recurrence-free survival (RFS) for AFP-negative (<20 ng/mL) HCC patients after curative resection.

Patients and Methods

A total of 166 pathologically confirmed AFP-negative HCC patients were included at the Ningbo Medical Center Lihuili Hospital. A LASSO regression analysis was used for data dimensionality reduction and element selection. Univariate and multivariate Cox regression analyses were performed to identify the independent risk factors relevant to RFS. Finally, clinical nomogram prediction model for RFS of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Receiver operating characteristic (ROC) and decision curve analysis (DCA) curve were used to validate the performance and clinical utility of the nomogram.

Results

Multivariate Cox regression analysis indicated that ALBI grade (hazard ratio, [HR] = 2.624, 95% confidence interval [CI]: 1.391-4.949, P = 0.003), INR (HR = 2.605, 95% CI: 1.061-6.396, P = 0.037), MLR (HR = 1.769, 95% CI: 1.073-2.915, P = 0.025) and MVI (HR = 4.726, 95% CI: 2.365-9.444, P < 0.001) were independent prognostic factors of RFS. Nomogram with independent factors was established and achieved a better concordance index of 0.753 (95% CI: 0.672-0.834) for predicting RFS. The ROC found that the area under curve (AUC) was consistent with the C-index and the sensitivity was 85.4%. The risk score calculated by nomogram could divide AFP-negative HCC patients into high-, moderate- and low-risk groups (P < 0.05). DCA analysis revealed that the nomogram could augment net benefits and exhibited a wider range of threshold probabilities by the risk stratification than the AJCC T and BCLC stage in the prediction of AFP-negative HCC recurrence.

Conclusion

The ALBI grade- and MLR-based nomogram prognostic model for RFS showed high predictive accuracy in AFP-negative HCC patients after surgical resection.

Comparing the CT and MRI findings for canine primary hepatocellular lesions

BACKGROUND

Triple-phase CT and gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) MRI have been used to differentiate hepatocellular carcinomas (HCCs) in dogs.

METHODS

This retrospective case series aimed to compare the CT findings with MRI findings of 20 canine hepatocellular lesions, including eight poorly/moderately-differentiated HCCs, eight well-differentiated HCCs and four hyperplasias. CT data were analysed, and the following parameters were noted: vessel enhancement, enhancement pattern in the equilibrium phase, maximal transverse diameter, the lowest enhancement, and the attenuation values of each hepatocellular lesion in the precontrast and triple-phase series, including the arterial phase, portal phase and equilibrium phase. MRI data were analysed, and the following parameters were noted: signal intensities of each hepatocellular lesion on T2-weighted images and T1-weighted images, and signal intensity ratio of the hepatocellular lesions in the hepatobiliary phase.

RESULTS

In 62.5% of poorly/moderately-differentiated HCC and 75% of well-differentiated HCC, presumptive necrosis was detected on CT and MRI. In the hepatobiliary phase on MRI, the median signal intensity ratio of poorly/moderately-differentiated HCC (0.54 [range: 0.3-0.71]) was significantly lower than that of well-differentiated HCC (0.75 [range: 0.6-0.96]) and hyperplasia (0.79 [range: 071-0.98]; p = 0.02 and p = 0.02, respectively).

CONCLUSION

Gd-EOB-DTPA-enhanced MRI may be a superior modality for differentiating hepatocellular origin lesions.

Pathologic, Molecular, and Prognostic Radiologic Features of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with variable biologic aggressiveness based on the tumor grade, presence or absence of vascular invasion, and pathologic and molecular classification. Knowledge and understanding of the prognostic implications of different pathologic and molecular phenotypes of HCC are emerging, with therapeutics that promise to provide improved outcomes in what otherwise remains a lethal cancer. Imaging has a central role in diagnosis of HCC. However, to date, the imaging algorithms do not incorporate prognostic features or subclassification of HCC according to its biologic aggressiveness. Emerging data suggest that some imaging features and further radiologic, pathologic, or radiologic-molecular phenotypes may allow prediction of the prognosis of patients with HCC. An invited commentary by Bashir is available online. Online supplemental material is available for this article. ^©RSNA, 2021.

Imaging Features at the Periphery: Hemodynamics, Pathophysiology, and Effect on LI-RADS Categorization

Liver lesions have different enhancement patterns at dynamic contrast-enhanced imaging. The Liver Imaging Reporting and Data System (LI-RADS) applies the enhancement kinetic of liver observations in its algorithms for imaging-based diagnosis of hepatocellular carcinoma (HCC) in at-risk populations. Therefore, careful analysis of the spatial and temporal features of these enhancement patterns is necessary to increase the accuracy of liver mass characterization. The authors focus on enhancement patterns that are found at or around the margins of liver observations-many of which are recognized and defined by LI-RADS, such as targetoid appearance, rim arterial phase hyperenhancement, peripheral washout, peripheral discontinuous nodular enhancement, enhancing capsule appearance, nonenhancing capsule appearance, corona enhancement, and periobservational arterioportal shunts-as well as peripheral and periobservational enhancement in the setting of posttreatment changes. Many of these are considered major or ancillary features of HCC, ancillary features of malignancy in general, features of non-HCC malignancy, features associated with benign entities, or features related to treatment response. Distinction between these different patterns of enhancement can help with achieving a more specific diagnosis of HCC and better assessment of response to local-regional therapy. ^©RSNA, 2021.

Magnetic Resonance Imaging of Nonhepatocellular Malignancies in Chronic Liver Disease

Hepatocellular carcinoma (HCC) is the most common liver malignancy associated with chronic liver disease. Nonhepatocellular malignancies may also arise in the setting of chronic liver disease. The imaging diagnosis of non-HCC malignancies may be challenging. Non-HCC malignancies in patients with chronic liver disease most commonly include intrahepatic cholangiocarcinoma and combined hepatocellular-cholangiocarcinoma, and less commonly hepatic lymphomas and metastases. On MR imaging, non-HCC malignancies often demonstrate a targetoid appearance, manifesting as rim arterial phase hyperenhancement, peripheral washout, central delayed enhancement, and peripheral restricted diffusion. When applying the Liver Imaging Reporting and Data System algorithm, observations with targetoid appearance are categorized as LR-M.

HCC: role of pre- and post-treatment tumor biology in driving adverse outcomes and rare responses to therapy

Liver cancer is the fastest-growing cause of cancer deaths in the United States and is a complex disease. The response of hepatocellular carcinoma (HCC) to treatment can be variable. Predicting response to determine the most effective therapy is an active area of research. Our understanding of underlying factors which drive response to therapy is continually increasing. As more therapies for the treatment of this disease evolve, it is crucial to identify and match the ideal therapy for a particular tumor and patient. The potential predicative imaging features of tumor behavior, while of research interest, have not been validated for clinical use and do not currently inform treatment planning. If further validated though, prognostic features may be used in the future to personalize treatment plans according to individual patients and tumors. Unexpected post-treatment responses such as potential tumor biology changes and abscopal effect which are important to be aware of. This review is intended for radiologists who routinely interpret post treatment HCC imaging and is designed to increase their cognizance about how HCC tumor biology drives response to therapy and explore rare responses to therapy.

Gadoxetate-enhanced MRI Features of Proliferative Hepatocellular Carcinoma Are Prognostic after Surgery

Background Hepatocellular carcinomas (HCCs) are heterogeneous neoplasms, and the prognosis varies based on the subtype. Two broad molecular classes of HCC have been proposed: a proliferative and a nonproliferative class. Purpose To evaluate the gadoxetate-enhanced MRI findings of the proliferative class HCC and its prognostic significance after surgery. Materials and Methods This retrospective cohort study evaluated patients with surgically resected treatment-naive single HCC (≤5 cm) who underwent hepatic resection from January 2010 through February 2013 and preoperative gadoxetate-enhanced MRI. A Cox proportional hazards model was used to determine the predictive factors for overall survival (OS), intrahepatic distant recurrence, and extrahepatic metastasis (EM). The mean follow-up period was 75.5 months ± 30.2 (standard deviation). Multivariable logistic regression was performed to determine factors associated with proliferative class HCC. Results A total of 158 patients (mean age, 57 years ± 11; 128 men and 30 women) were evaluated. Forty-two of the 158 HCCs (26.6%) were proliferative class HCCs (17 macrotrabecular-massive HCCs, 14 keratin 19-positive HCCs, 10 scirrhous HCCs, and one sarcomatoid HCC). The proliferative class was associated with worse OS (hazard ratio [HR], 3.1; 95% CI: 1.5, 6.0; P = .01) and higher rates of intrahepatic distant recurrence (HR, 1.83; 95% CI: 1.1, 2.9; P = .01) and EM (HR, 9.97; 95% CI: 3.2, 31.4; P < .001). Rim arterial phase hyperenhancement (APHE) at gadoxetate-enhanced MRI (odds ratio [OR], 6.35; 95% CI: 1.9, 21.7; P = .01) and high serum α-fetoprotein (>100 ng/mL) (OR, 4.18; 95% CI: 1.64, 10.7; P = .01) were independent predictors for proliferative HCC. The presence of rim APHE was associated with poor OS (HR, 2.4; 95% CI: 1.2, 4.9; P = .02) and higher rates of EM (HR, 7.4; 95% CI: 2.5, 21.7; P < .01). Conclusion The proliferative class of hepatocellular carcinoma (HCC) is an independent factor for poor overall survival with increased rates of intrahepatic and extrahepatic metastasis. Rim arterial phase hyperenhancement at gadoxetate-enhanced MRI may help to identify proliferative class HCC and predict poor overall survival and an increased incidence of extrahepatic metastasis. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Krinsky and Shanbhogue in this issue.

A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive form of hepatocellular carcinoma and is associated with poor survival outcomes.

AIMS

This study aimed to develop a radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of MTM-HCC.

METHODS

This study enrolled 88 patients with histologically confirmed HCC, including 32 MTM-HCCs and 56 Non-MTM-HCCs. The clinical and gadobenate dimeglumine (Gd)-enhanced MRI features were retrospectively reviewed by two abdominal radiologists. The regions of interest (ROIs) on the largest cross-sectional image and two adjacent images of the tumor, from which radiomics features were extracted via MaZda software and a radiomics score (Rad-score) was calculated via Python software. Combined with the Rad-score and independent imaging factors, a radiomics nomogram was constructed using R software. Nomogram performance was estimated with calibration curve.

RESULTS

A total of eleven top weighted radiomics features were selected among five sequences of MR images. There was a significant difference in Rad-score between MTM-HCC and non-MTM-HCC patients (P < 0.001), where patients with MTM-HCC generally had higher Rad-scores (absolute value). After multivariate analysis, radiomics score (OR = 7.794, P < 0.001) and intratumor fat (OR = 9.963, P = 0.014) were determined as independent predictors associated with MTM-HCC. The area under the receiver operating characteristic (ROC) curve of the selected model was 0.813 (95% CI 0.714-0.912) and the optimal cutoff value was 0.60. The nomogram showed overall satisfactory prediction performance (AUC = 0.785 [95% CI 0.684-0.886]).

CONCLUSIONS

A contrast-enhanced MRI-based radiomics nomogram may be useful for preoperative prediction of MTM-HCC in primary HCC patients, allowing opportunity to improve the treatment course and patient outcomes.

HCC advances in diagnosis and prognosis: Digital and Imaging

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death worldwide. Understanding of the pathogenesis of HCC has significantly improved in the past few years due to advances in genetics, molecular biology and pathology. Several subtypes have been identified with different backgrounds and outcomes, leading to possible changes in disease management and challenging the role of imaging. Indeed, despite its pivotal role in the diagnostic workup, prognosis, and the decision-making process in patients with HCC, these recent developments are progressively redefining the role of imaging. First and most important, liver imaging is shifting from a purely qualitative to a quantitative paradigm, integrating quantitative imaging and radiomics in a digital era. Second, to improve patient management, imaging has gradually moved beyond tumor-centered assessment to include a broader evaluation of the liver and its function. This review describes and discusses these advances in the imaging for the diagnosis and prognosis of HCC.

A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion

OBJECTIVE

A radiomics nomogram for pretreatment prediction of TACE refractoriness was developed and validated for hepatocellular carcinoma (HCC) without extrahepatic metastasis or macrovascular invasion.

MATERIALS AND METHODS

This study included 80 patients with HCC without extrahepatic metastasis or macrovascular involvement treated with TACE between July 2016 and November 2018. The datasets were divided into a training set (80%) and a test set (20%) for feature selection and tenfold cross-validation. Forty radiomic features were extracted from arterial-phase computed tomography (CT) using the Local Image Features Extraction software. The Lasso regression model was used for radiomics signature selection. The Lasso regression model was used for radiomics signature selection and the selected signatures were validated using the Mann-Whitney U-test. The radiomics nomogram was developed based on a multivariate logistic regression model incorporating the Rad-score, CT imaging factors, and clinical factors, and it was validated.

RESULTS

The Rad-score, which consists of the Gray-Level Zone Length Matrix (GLZLM)-Long-Zone Low Gray-Level Emphasis (LZLGE) and GLZLM-Gray-Level Non-Uniformity (GLNU), T-stage, log α-fetoprotein (AFP), and bilobar distribution were significantly associated with TACE refractoriness (p < 0.05). Predictors in the radiomics nomogram were the Rad-score and T-stage (Rad-score + T-stage), Rad-score and bilobar distribution (Rad-score + bilobar distribution), or Rad-score and logAFP (Rad-score + logAFP). The multivariate logistic regression model showed a good predictive performance (Rad-score + T-stage, AUC, 0.95; Rad-score + bilobar distribution, AUC 0.91; and Rad-score + logAFP, AUC, 0.91).

CONCLUSION

The radiomics nomogram could be used for the pretreatment prediction of TACE refractoriness.

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

Background Macrotrabecular-massive (MTM) subtype and vessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. Purpose To estimate the potential of preoperative CT in the prediction of MTM subtype and VETC pattern. Materials and Methods Patients who underwent surgical resection or liver transplant and preoperative CT for HCC between January 2015 and June 2018 were retrospectively included in the primary cohort. CT imaging features were evaluated by two radiologists. Predictors associated with the MTM subtype or VETC pattern were determined by using logistic regression analyses and the performance was tested in a validation cohort. Prognostic factors associated with early recurrence after surgical resection were identified by using Cox regression analyses. Results The primary cohort included 170 patients (median age, 55 years; interquartile range, 48-63 years; 152 men). Serum α-fetoprotein level higher than 100 ng/mL (odds ratio [OR], 4.3; 95% CI: 2.1, 9.2; P < .001), intratumor necrosis (OR, 5.2; 95% CI: 2.5, 11.0; P < .001), and intratumor hemorrhage (OR, 5.4; 95% CI: 1.3, 23.3; P = .02) were independent predictors for MTM subtype, whereas tumor size greater than 5 cm (OR, 3.8; 95% CI: 1.7, 8.1; P = .001) and intratumor necrosis (OR, 2.1; 95% CI: 1.0, 4.4; P = .045) were independent predictors for VETC pattern. These features were used for the construction of ANH and SN scores (where A is α-fetoprotein level, N is necrosis, H is hemorrhage, and S is size), respectively, which showed comparable prediction performance in the primary and validation cohorts. Preoperative high ANH and high SN phenotype (hazard ratio, 1.9; 95% CI: 1.2, 3.0; P = .01) was independently associated with early recurrence after surgical resection. Conclusion Preoperative CT features could be used for the characterization of macrotrabecular-massive subtype and vessels that encapsulate tumor clusters pattern and were of prognostic significance for early recurrence in patients with hepatocellular carcinoma. Online supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue. Published under a CC BY 4.0 license.

Texture Analysis Based on Gd-EOB-DTPA-Enhanced MRI for Identifying Vessels Encapsulating Tumor Clusters (VETC)-Positive Hepatocellular Carcinoma

PURPOSE

To determine the potential findings associated with vessels encapsulating tumor clusters (VETC)-positive hepatocellular carcinoma (HCC), with particular emphasis on texture analysis based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI.

METHODS

Eighty-one patients with VETC-negative HCC and 52 patients with VETC-positive HCC who underwent Gd-EOB-DTPA-enhanced MRI before curative partial hepatectomy were retrospectively evaluated in our institution. MRI texture analysis was performed on arterial phase (AP) and hepatobiliary phase (HBP) images. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select texture features most useful for identifying VETC-positive HCC. Univariate and multivariate analyses were used to determine significant variables for identifying the VETC-positive HCC in clinical factors and the texture features of MRI. Receiver operating characteristic (ROC) analysis and DeLong test were performed to compare the identified performances of significant variables for identifying VETC-positive HCC.

RESULTS

LASSO logistic regression selected 3 features in AP and HBP images, respectively. In multivariate analysis, the Log-sigma-4.0-mm-3D first-order Kurtosis derived from AP images (odds ratio [OR] = 4.128, P = 0.001) and the Wavelet-LHL-GLDM Dependence Non Uniformity Normalized derived from HBP images (OR = 2.280, P = 0.004) were independent significant variables associated with VETC-positive HCC. The combination of the two texture features for identifying VETC-positive HCC achieved an AUC value of 0.844 (95% confidence interval CI, 0.777, 0.910) with a sensitivity of 80.8% (95% CI, 70.1%, 91.5%) and specificity of 74.1% (95% CI, 64.5%, 83.6%).

CONCLUSION

Texture analysis based on Gd-EOB-DTPA-enhanced MRI can help identify VETC-positive HCC.

Pathologic and molecular features of hepatocellular carcinoma: An update

Morphological diversity and several new distinct pathologic subtypes of hepatocellular carcinoma (HCC) are now well-recognized. Recent advances in tumor genomics and transcriptomics have identified several recurrent somatic/genetic alterations that are closely related with histomorphological subtypes and have therefore, greatly improved our understanding of HCC pathogenesis. Pathologic subtyping allows for a diagnosis which is clinically helpful and can have important implication in patient prognostication as some of these subtypes are extremely aggressive with vascular invasion, early recurrence, and worst outcomes. Several targeted treatments are now being considered in HCC, and the reporting of subtypes may be quite useful for personalized therapeutic purpose. This manuscript reviews the recently identified histomorphological subtypes and molecular alterations in HCC.

Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid-enhanced magnetic resonance imaging

OBJECTIVES

To identify image features of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to determine its role in predicting MTM-HCC.

METHODS

Patients who underwent preoperative gadoxetic acid-enhanced MRI and with surgery proven HCC were retrospectively included. Imaging features were assessed according to Liver Imaging Reporting and Data System. Quantitative measurements were recorded. Clinical characteristics and imaging findings were compared between MTM-HCCs and non-MTM-HCCs. Predictive factors of MTM-HCC were screened with univariate analyses and then identified with multivariate logistic regression. A regression-based diagnostic model was constructed. ROC analyses were used to determine cutoff values, AUC, and corresponding 95% confidence interval (CI) of findings. The diagnostic performance was validated by 10-fold cross-validation.

RESULTS

One hundred and forty-one patients with 37 MTM-HCCs were included. Multivariate analyses identified high platelet count (≥ 163.5 × 10³/ul, odds ratio = 3.20; 95% CI: 1.29, 7.96; p = 0.012), low tumor-to-liver ADC ratio (≤ 1.05, odds ratio = 3.05; 95% CI, 1.23 - 7.55; p = 0.016), and necrosis or severe ischemia (odds ratio = 11.61; 95% CI, 3.99 - 33.76, p < 0.001) as independent predictors of MTM-HCC. Necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. The average AUCs were 0.81 (95% CI: 0.71, 0.90) for the regression-based diagnostic model, with a sensitivity of 57% and specificity of 92%.

CONCLUSIONS

Necrosis or severe ischemia was a sensitive imaging feature of MTM-HCC. Noninvasive prediction of this subtype can be achieved with good accuracy and excellent specificity when findings were combined.

KEY POINTS

• The macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) represents an aggressive subtype of HCC and is associated with poor prognosis. • Imaging features of necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. • A regression-based diagnostic model including high platelet count (≥ 163.5 × 10³/ul), low tumor-to-liver ADC ratio (≤ 1.05), and necrosis or severe ischemia can provide noninvasive assessment of MTM-HCC with good accuracy and high specificity.

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

Coronavirus disease 2019 (COVID-19) with the infection of SARS-CoV-2 has become a serious pandemic worldwide. However, only few studies focused on risk factors of prolonged SARS-CoV-2 RNA detection among patients with COVID-19. We included 206 adult patients with laboratory-confirmed COVID-19 from two hospitals between 23 Jan and 1 April 2020. Least absolute shrinkage and selection operator (LASSO) analysis was used to screen out independent risk factors of SARS-CoV-2 RNA detection. By multivariate binomial logistic regression analysis and Cox regression analysis, we further determined the associations between SARS-CoV-2 RNA detection and potential risk factors. All patients had two negative SARS-CoV-2 tests with 33 days of median duration of SARS-CoV-2 RNA detection (interquartile range: 25.2-39 days). LASSO and binomial logistic regression analyses suggested that delayed hospital admission (adjusted OR = 3.70, 95% CI: 1.82-7.50), hypokalemia, and subpleural lesion (adjusted OR = 4.32, 95% CI: 1.10-16.97) were associated with prolonged SARS-CoV-2 RNA detection. By LASSO and multivariate Cox regression analyses, we observed that delayed hospital admission, subpleural lesion, and high-dose corticosteroid use were independent risk factors of prolonged SARS-CoV-2 RNA detection. Early hospital admission shortened 5.73 days of mean duration of SARS-CoV-2 RNA detection than delayed hospital admission after adjusting confounding factors. Our study demonstrated that delayed hospital admission and subpleural lesion were associated with prolonged SARS-CoV-2 RNA detection among patients with COVID-19. The use of high-dose corticosteroids should be interpreted with extreme caution in treating COVID-19.

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

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P < 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P < 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.

Hepatocellular Carcinoma With Atypical Imaging Features: Review of the Morphologic Hepatocellular Carcinoma Subtypes With Radiology-Pathology Correlation

Hepatocellular carcinoma (HCC) is the fastest growing cause of cancer death in the United States with the incidence rate more than doubling in 20 years. HCC is unique since a noninvasive diagnosis can be achieved with imaging alone when specific clinical criteria and imaging characteristics are met, obviating the need for tissue sampling. However, HCC is a highly heterogeneous neoplasm. Atypical HCC subtypes vary significantly in their morphology, which can be attributed to specific histologic and molecular features, and can cause deviations from the classic imaging characteristics. The different morphologic subtypes of HCC frequently present a diagnostic challenge for radiologists and pathologists since their imaging and pathologic features can overlap with those of non-HCC malignancies. Identifying an atypical subtype can have important clinical implications. Liver transplant, albeit a scarce and limited resource, is the optimal treatment for conventional HCC, potentially curing both the tumor and the underlying pre-malignant condition. Some HCC subtypes as well as mimickers are associated with unacceptably high recurrence and poor outcome after transplant, and there remains limited data on the role and prognosis of liver transplantation for treatment of rare HCC subtypes. Other subtypes tend to recur later than classic HCC, potentially requiring a different follow-up scheme. This review will discuss the appearance of different HCC subtypes in relation to their histopathologic features. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.