[MRI manifestations of 40 cases with the hepatic epithelioid hemangioendothelioma classification based on the morphology and size].

Objective: To explore the MRI characteristics of the hepatic epithelioid hemangioendothelioma (HEHE) classification according to morphology and size. Methods: The clinical, pathological, and MRI imaging data of 40 cases with HEHE confirmed pathologically from December 2009 to September 2021 were retrospectively analyzed. A paired sample t-test was used for comparison between the two groups. Results: There were 40 cases (5 solitary, 24 multifocal, 9 local fusion, and 2 diffuse fusion) and 214 lesions (163 nodules, 31 masses, and 20 fusion foci). The most common features of lesions were subcapsular growth and capsular depression. The signal intensity of lesions ≤1cm was usually uniform with whole or ring enhancement. Nodules and mass-like lesions ≥1cm on a T1-weighted image had slightly reduced signal intensity or manifested as a halo sign. Target signs on a T2-weighted image were characterized by: target or centripetal enhancement; fusion-type lesions; irregular growth and hepatic capsular retraction, with ring or target-like enhancement in the early stage of fusion and patchy irregular enhancement in the late stage; blood vessels traversing or accompanied by malformed blood vessels; focal bleeding; an increasing proportion of extrahepatic metastases and abnormal liver function with the type of classified manifestation; primarily portal vein branches traversing; and reduced overall intralesional bleeding rate (17%). Lollipop signs were presented in 19 cases, with a high expression rate in mass-type lesions (42%). The fusion lesions were expressed, but the morphological manifestation was atypical. The diffusion-weighted imaging mostly showed high signal or target-like high signal. An average apparent diffusion coefficient of lesions was (1.56±0.36) ×10(-3)mm(2)/s, which was statistically significantly different compared with that of adjacent normal liver parenchyma (t=8.28, P<0.001). Conclusion: The MRI manifestations for the HEHE classification are closely related to the morphology and size of the lesions and have certain differences and characteristics that are helpful for the diagnosis of the disease when combined with clinical and laboratory examinations.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology.

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT: Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION: The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.

A novel liver zonation phenotype-associated molecular classification of hepatocellular carcinoma.

Background: Liver zonation is a unique phenomenon in which the liver exhibits distinct functions among hepatocytes along the radial axis of the lobule. This phenomenon can cause the sectionalized initiation of several liver diseases, including hepatocellular carcinoma (HCC). However, few studies have explored the zonation features of HCC. Methods: Four single-cell RNA sequencing datasets were used to identify hepatocyte-specific zonation markers. Integrative analysis was then performed with a training RNA-seq cohort (616 HCC samples) and an external validating microarray cohort (285 HCC samples) from the International Cancer Genome Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, and EMBL's European Bioinformatics Institute for clustering using non-negative matrix factorization consensus clustering based on zonation genes. Afterward, we evaluated the prognostic value, clinical characteristics, transcriptome and mutation features, immune infiltration, and immunotherapy response of the HCC subclasses. Results: A total of 94 human hepatocyte-specific zonation markers (39 central markers and 55 portal markers) were identified for the first time. Subsequently, three subgroups of HCC, namely Cluster1, Cluster2, and Cluster3 were identified. Cluster1 exhibited a non-zonational-like signature with the worst prognosis. Cluster2 was intensively associated with a central-like signature and exhibited low immune infiltration and sensitivity toward immune blockade therapy. Cluster3 was intensively correlated with a portal-like signature with the best prognosis. Finally, we identified candidate therapeutic targets and agents for Cluster1 HCC samples. Conclusion: The current study established a novel HCC classification based on liver zonation signature. By classifying HCC into three clusters with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, this study provided new perspectives on the heterogeneity of HCC and shed new light on delivering precision medicine for HCC patients.

Identification of hepatocellular carcinoma subtypes based on PcG-related genes and biological relevance with cancer cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is an extensive heterogeneous disease where epigenetic factors contribute to its pathogenesis. Polycomb group (PcG) proteins are a group of subunits constituting various macro-molecular machines to regulate the epigenetic landscape, which contributes to cancer phenotype and has the potential to develop a molecular classification of HCC. RESULTS: Here, based on multi-omics data analysis of DNA methylation, mRNA expression, and copy number of PcG-related genes, we established an epigenetic classification system of HCC, which divides the HCC patients into two subgroups with significantly different outcomes. Comparing these two epigenetic subgroups, we identified different metabolic features, which were related to epigenetic regulation of polycomb-repressive complex 1/2 (PRC1/2). Furthermore, we experimentally proved that inhibition of PcG complexes enhanced the lipid metabolism and reduced the capacity of HCC cells against glucose shortage. In addition, we validated the low chemotherapy sensitivity of HCC in Group A and found inhibition of PRC1/2 promoted HCC cells' sensitivity to oxaliplatin in vitro and in vivo. Finally, we found that aberrant upregulation of CBX2 in Group A and upregulation of CBX2 were associated with poor prognosis in HCC patients. Furthermore, we found that manipulation of CBX2 affected the levels of H3K27me3 and H2AK119ub. CONTRIBUTIONS: Our study provided a novel molecular classification system based on PcG-related genes data and experimentally validated the biological features of HCC in two subgroups. Our founding supported the polycomb complex targeting strategy to inhibit HCC progression where CBX2 could be a feasible therapeutic target.

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis.

INTRODUCTION: Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA. METHODS: This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients. RESULTS: In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA. CONCLUSION: We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.

Hepatic epithelioid hemangioendothelioma: A clinocopathological correlation.

Primary hepatic epithelioid hemangioendothelioma (HEHE) is a rare tumor with an incidence of <0.1 per 100,000. The clinical course is variable with variable outcomes. Due to its rarity, treatment protocols, prognostic and predictive factors are not well established underscoring the need for such a study. Pathologists' awareness of this entity, a meticulous morphologic examination coupled with immunohistochemistry can aid in accurate diagnosis.

Historical and Contemporary Management of Infantile Hepatic Hemangioma: A 30-year Single-center Experience.

OBJECTIVE: To describe outcome of infants with hemangioma(s) of the liver. SUMMARY OF BACKGROUND DATA: Infantile hepatic hemangiomas exhibit a diverse phenotype. We report our 30-year experience and describe optimal management based on precise radiological classification. METHODS: Retrospective review of 124 infants (66 female) 1986-2016. Categorical analysis with Chi2 and nonparametric comparison. Data expressed as median (range) and P < 0.05 considered significant. RESULTS: Lesions classified as focal (n = 70, 56%); multifocal (n = 47, 38%) or diffuse (n = 7, 6%) and of these 80(65%) were symptomatic (eg, cardiac failure n = 39, 31%; thrombocytopenia n = 12, 10%).Increased hepatic artery velocity was seen in 63 (56%). Median hepatic artery velocity was greatest in diffuse lesions [245 (175-376) cm/s vs focal 120 (34-242) cm/s vs multifocal 93 (36-313) cm/s; P = 0.0001]. Expectant management alone was followed in 55 (44%). Medical therapy was utilised in 57(46%) and sufficient for symptom control in 29/57 (51%). Propranolol therapy (from 2008) was sufficient for symptom control in 22/28 (79%). Surgery (hepatic artery ligation n = 26; resection n = 13; embolization n = 1) was required in 40 (32%). Median maximal lesion diameter was 3 (0.5-17.1) cm and greater in those requiring surgery (7 cm vs 4.9 cm; P = 0.04). The proportion requiring surgery decreased markedly in the propranolol era [pre-propranolol 25/48 (52%) vs post-propranolol 16/76 (21%) (P = 0.0003)]. Systematic follow-up with ultrasound to a median of 2.6 (0.02-16) years. CONCLUSIONS: A proportion of infantile hepatic hemangiomas remain asymptomatic permitting observation until resolution but the majority require complex multi-modal therapy. First-line pharmacotherapy with propranolol has reduced but not abolished the need for surgery.

Children's Hepatic Tumors International Collaboration-Hepatoblastoma Stratification (CHIC-HS) System for Pediatric Patients with Hepatoblastoma: A Retrospective, Hospital-Based Cohort Study in South Korea.

PURPOSE: In 2017, the Children's Hepatic Tumors International Collaboration-Hepatoblastoma Stratification (CHIC-HS) system was introduced. We aimed to evaluate the accuracy of CHIC-HS System for the prediction of event-free survival (EFS) in Korean pediatric patients with hepatoblastoma. MATERIALS AND METHODS: This two-center retrospective study included consecutive Korean pediatric patients with histopathologically confirmed hepatoblastoma from March 1988 through September 2019. We compared EFS among four risk groups according to the CHIC-HS system. Discriminatory ability of CHIC-HS system was also evaluated using optimism-corrected C-statistics. Factors associated with EFS were explored using multivariable Cox regression analysis. RESULTS: We included 129 patients (mean age, 2.6±3.3 years; female:male, 63:66). The 5-year EFS rates in the very low, low, intermediate, and high-risk groups, according to the CHIC-HS system were 90.0%, 82.8%, 73.5%, and 51.3%, respectively. The CHIC-HS system aligned significantly well with EFS outcomes (p=0.004). The optimism-corrected C index of CHIC-HS was 0.644 (95% confidence interval [CI], 0.561 to 0.727). Age ≥ 8 (vs. age ≤ 2; hazard ratio [HR], 2.781; 95% CI, 1.187 to 6.512; p=0.018), PRE-Treatment EXTent of tumor (PRETEXT) stage IV (vs. PRETEXT I or II; HR, 2.774; 95% CI, 1.228 to 5.974; p=0.009), and presence of metastasis (HR, 2.886; 95% CI, 1.457 to 5.719; p=0.002), which are incorporated as the first three nodes in the CHIC-HS system, were independently associated with EFS. CONCLUSION: The CHIC-HS system aligned significantly well with EFS outcomes in Korean pediatric patients with hepatoblastoma. Age group, PRETEXT stage, and presence of metastasis were independently associated with EFS.

Molecular classification of hepatocellular carcinoma: prognostic importance and clinical applications.

Hepatocellular carcinoma (HCC) is a lethal human malignancy with a very low overall and long-term survival rate. Poor prognostic outcomes are predominantly associated with HCC due to a huge landscape of heterogeneity found in the deadliest disease. However, molecular subtyping of HCC has significantly improved the knowledge of the underlying mechanisms that contribute towards the heterogeneity and progression of the disease. In this review, we have extensively summarized the current information available about molecular classification of HCC. This review can be of great significance for providing the insight information needed for development of novel, efficient and personalized therapeutic options for the treatment of HCC patients globally.

BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update.

There have been major advances in the armamentarium for hepatocellular carcinoma (HCC) since the last official update of the Barcelona Clinic Liver Cancer prognosis and treatment strategy published in 2018. Whilst there have been advances in all areas, we will focus on those that have led to a change in strategy and we will discuss why, despite being encouraging, data for select interventions are still too immature for them to be incorporated into an evidence-based model for clinicians and researchers. Finally, we describe the critical insight and expert knowledge that are required to make clinical decisions for individual patients, considering all of the parameters that must be considered to deliver personalised clinical management.

Construction and Comprehensive Analysis of a Stratification System Based on AGTRAP in Patients with Hepatocellular Carcinoma.

BACKGROUND: With the development of sequencing technology, several signatures have been reported for the prediction of prognosis in patients with hepatocellular carcinoma (HCC). However, the above signatures are characterized by cumbersome application. Therefore, the study is aimed at screening out a robust stratification system based on only one gene to guide treatment. METHODS: Firstly, we used the limma package for performing differential expression analysis on 374 HCC samples, followed by Cox regression analysis on overall survival (OS) and disease-free interval (PFI). Subsequently, hub prognostic genes were found at the intersection of the above three groups. In addition, the topological degree inside the PPI network was used to screen for a unique hub gene. The rms package was used to construct two visual stratification systems for OS and PFI, and Kaplan-Meier analysis was utilized to investigate survival differences in clinical subgroups. The ssGSEA algorithm was then used to reveal the relationship between the hub gene and immune cells, immunological function, and checkpoints. In addition, we also used function annotation to explore into putative biological functions. Finally, for preliminary validation, the hub gene was knocked down in the HCC cell line. RESULTS: We discovered 6 prognostic genes (SKA1, CDC20, AGTRAP, BIRC5, NEIL3, and CDC25C) for constructing a PPI network after investigating survival and differential expression genes. According to the topological degree, AGTRAP was chosen as the basis for the stratification system, and it was revealed to be a risk factor with an independent prognostic value in Kaplan-Meier analysis and Cox regression analysis (P < 0.05). In addition, we constructed two visualized nomograms based on AGTRAP. The novel stratification system had a robust predictive value for PFI and OS in ROC analysis and calibration curve (P < 0.05). Meanwhile, AGTRAP upregulation was associated with T staging, N staging, M staging, pathological stage, grade, and vascular invasion (P < 0.05). Notably, AGTRAP was overexpressed in tumor tissues in all pancancers with paired samples (P < 0.05). Furthermore, AGTRAP was associated with immune response and may change immune microenvironment in HCC (P < 0.05). Next, gene enrichment analysis suggested that AGTRAP may be involved in the biological process, such as cotranslational protein targeting to the membrane. Finally, we identified the oncogenic effect of AGTRAP by qRT-PCR, colony formation, western blot, and CCK-8 assay (P < 0.05). CONCLUSION: We provided robust evidences that a stratification system based on AGTRAP can guide survival prediction for HCC patients.

Rare variants of primary liver cancer: Fibrolamellar, combined, and sarcomatoid hepatocellular carcinomas.

Hepatocellular carcinoma (HCC) constitutes 80% of all primary liver cancers. Based on key developments in the understanding of its carcinogenesis and the advancement of treatment options, detailed algorithms and practice guidelines have been published to guide the clinical management of HCC. Furthermore, several subclasses of HCC have been described based on molecular profiles and linked to pathological characteristics, clinical features, and disease aggressiveness. Most recently, the combination of the checkpoint inhibitor atezolizumab plus bevacizumab has significantly increased treatment response in the first line systemic treatment of HCC. Unfortunately, rare HCC variants, in particular fibrolamellar liver cancer (FLC), combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA), and sarcomatoid hepatocellular carcinoma (sHCC), were excluded from phase III studies. Therefore, data for decision-making and treatment allocation for these distinct entities, representing 1-5% of all primary liver cancers, is scarce. Moreover, most of the knowledge available for these rare HCC variants is based on registry data and retrospective studies. In this position paper, we briefly summarize the current clinical knowledge regarding FLC, cHCC-CCA, and sHCC. Based on our summary, we propose future clinical research activities within the framework of the European Reference Network on Hepatological Diseases (ERN RARE-LIVER).

A farewell to Barcelona Clinic Liver Cancer (BCLC) classification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with expected increasing frequency in the next few decades. At early stages, HCC is curable, with most common therapeutic modalities to include surgical resection and liver transplantation. The Barcelona Clinic Liver Cancer (BCLC) Staging System is widely adopted tool to guide the therapeutic algorithms of patients with HCC. This classification is guiding the clinical practice for the last 2 decades. However, there are emerging data demonstrating that patients beyond the traditional criteria of operability, resectability or transplantability actually can benefit from surgical treatment, emphasizing the need of refinement or even change of current BCLC criteria.

An update on subtypes of hepatocellular carcinoma: From morphology to molecular.

The morphologic spectrum of hepatocellular carcinoma (HCC) is quite broad. While in about one-third of cases, the neoplasms can be categorized into meaningful subtypes based on morphology, a vast majority of these neoplasms are morphologically heterogeneous. With extensive tumor profiling, data has begun to emerge which can correlate specific morphologic features with underlying molecular signatures. A true morphologic subtype not only has reproducible H & E features, further supported by specific immunohistochemical or molecular signatures, but also has specific clinical implications and prognostic associations. Eight such morphologic subtypes are recognized by the 2019 WHO classification of tumors with a few more additional subtypes described in the literature. The goal of this review is to familiarize the reader with the morphologic subtypes and elaborate on the clinical and molecular associations of these neoplasms.

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

Factors associated with survival in dogs with a histopathological diagnosis of hepatocellular carcinoma: 94 cases (2007-2018).

Background: Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer in dogs. Despite this, relatively few reports of this disease exist pertaining to prognostic factors and outcome. Aim: To evaluate factors associated with survival in dogs with all subtypes of HCC diagnosed on histopathology. Methods: A retrospective single institutional study was carried out on 94 client-owned dogs with a histopathologic diagnosis of HCC between 2007 and 2018 obtained by biopsy (21/94) or attempted definitive resection (73/94). Signalment, preoperative features, surgical findings, and postoperative outcomes were recorded. Associations between survival to discharge data were collected and univariable logistical regression was carried out. Kaplan-Meier survival analysis was carried out to identify negative risk factors for long-term prognosis. Results: The median survival time (MST) for all patients was 707 days (95% CI = 551-842). MST was not significantly different (p > 0.05) between patients who had suspected versus incidentally diagnosed HCC (695 vs. 775 days), between complete versus incomplete surgical margins (668 vs. 834 days), or between patients with massive subtype versus nodular/diffuse subtype (707 vs. 747 days). Logistical regression identified an association with the excision of the right medial lobe and risk of perioperative death (OR = 9.2, CI 1.5-55.9, p = 0.016). An American Society of Anesthesiologists score ≥4, disease present within the quadrate lobe, and elevated blood urea nitrogen, potassium or gamma-glutamyltransferase were identified as negative prognosticators during multivariable Cox regression. Preoperative imaging (ultrasound or CT) agreed with the surgical location in 91% of the cases. Preoperative cytology was consistent with a diagnosis of HCC in 15/32 (46.9%) cases. Conclusion: Type of diagnosis (incidental vs presumed), completeness of excision, and subtype were not associated with MST in this study. Preoperative identification of tumors within the central division may be related to a less favorable outcome. Results of preoperative cytology were not highly sensitive for identifying a malignancy.

Proteomic Profiling of Hepatocellular Adenomas Paves the Way to Diagnostic and Prognostic Approaches.

BACKGROUND AND AIMS: Through an exploratory proteomic approach based on typical hepatocellular adenomas (HCAs), we previously identified a diagnostic biomarker for a distinctive subtype of HCA with high risk of bleeding, already validated on a multicenter cohort. We hypothesized that the whole protein expression deregulation profile could deliver much more informative data for tumor characterization. Therefore, we pursued our analysis with the characterization of HCA proteomic profiles, evaluating their correspondence with the established genotype/phenotype classification and assessing whether they could provide added diagnosis and prognosis values. APPROACH AND RESULTS: From a collection of 260 cases, we selected 52 typical cases of all different subgroups on which we built a reference HCA proteomics database. Combining laser microdissection and mass-spectrometry-based proteomic analysis, we compared the relative protein abundances between tumoral (T) and nontumoral (NT) liver tissues from each patient and we defined a specific proteomic profile of each of the HCA subgroups. Next, we built a matching algorithm comparing the proteomic profile extracted from a patient with our reference HCA database. Proteomic profiles allowed HCA classification and made diagnosis possible, even for complex cases with immunohistological or genomic analysis that did not lead to a formal conclusion. Despite a well-established pathomolecular classification, clinical practices have not substantially changed and the HCA management link to the assessment of the malignant transformation risk remains delicate for many surgeons. That is why we also identified and validated a proteomic profile that would directly evaluate malignant transformation risk regardless of HCA subtype. CONCLUSIONS: This work proposes a proteomic-based machine learning tool, operational on fixed biopsies, that can improve diagnosis and prognosis and therefore patient management for HCAs.