Guidelines for the Diagnosis and Treatment of Hepatocellular Carcinoma (2019 Edition)

Liver Cancer, Journal Year: 2020, Volume and Issue: 9(6), P. 682 - 720

Published: Jan. 1, 2020

<b><i>Background:</i></b> Primary liver cancer, around 90% are hepatocellular carcinoma in China, is the fourth most common malignancy and second leading cause of tumor-related death, thereby posing a significant threat to life health Chinese people. <b><i>Summary:</i></b> Since publication <i>Guidelines for Diagnosis Treatment Liver Cancer (2017 Edition)</i> 2018, additional high-quality evidence has emerged with relevance diagnosis, staging, treatment cancer outside China that requires guidelines be updated. The new edition <i>(2019 was written by more than 70 experts field China. They reflect real-world situation regarding diagnosing treating recent years. <b><i>Key Messages:</i></b> Most importantly, were endorsed promulgated Bureau Medical Administration National Health Commission People’s Republic December 2019.

Language: Английский

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2019 Chinese clinical guidelines for the management of hepatocellular carcinoma: updates and insights

HepatoBiliary Surgery and Nutrition, Journal Year: 2020, Volume and Issue: 9(4), P. 452 - 463

Published: Aug. 1, 2020

Importance: Approximately half of newly-diagnosed hepatocellular carcinoma (HCC) cases in the world occur China, with hepatitis B virus (HBV) infection being predominant risk factor. Recently, guidelines for management Chinese HCC patients were updated. Objective: The past decade has witnessed a great improvement (HCC). This study reviews recommendations 2019 and makes comparison practices from Western world. Evidence Review: updated on surveillance, diagnosis, treatment algorithm summarized, comparisons among guidelines, European Association Study Liver (EASL) American Diseases (AASLD) made. Findings: Besides imaging pathological diagnoses, novel biomarkers like seven-micro-RNA panel are advocated early diagnoses therapeutic efficacy evaluation guidelines. China liver cancer (CNLC) staging system, proposed 2017 continues to be standard model patient classification, subsequent modifications updates made allocations. Compared Barcelona Clinic Cancer (BCLC) CNLC system employs resection, transplantation, transarterial chemoembolization (TACE) more progressed HCC. TACE combination other regional therapies ablation or systemic sorafenib also encouraged select China. treatments have evolved considerably since lenvatinib, regorafenib, carbozantinib, ramucirumab immune checkpoint inhibitors (ICIs)were first prescribed as first-line second-line agents. Conclusions Relevances: Novel biomarkers, operative techniques recommended guideline. More aggressive modalities suggested HBV-related

Language: Английский

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Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition)

Liver Cancer, Journal Year: 2023, Volume and Issue: 12(5), P. 405 - 444

Published: Jan. 1, 2023

Primary liver cancer, of which around 75-85% is hepatocellular carcinoma in China, the fourth most common malignancy and second leading cause tumor-related death, thereby posing a significant threat to life health Chinese people.Since publication Guidelines for Diagnosis Treatment Liver Cancer China June 2017, were updated by National Health Commission December 2019, additional high-quality evidence has emerged from researchers worldwide regarding diagnosis, staging, treatment that requires guidelines be again. The new edition (2022 Edition) was written more than 100 experts field cancer not only reflects real-world situation but also may reshape nationwide diagnosis cancer.The guideline aims encourage implementation evidence-based practice improve national average 5-year survival rate patients with as proposed "Health 2030 Blueprint."

Language: Английский

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An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas

Brain, Journal Year: 2021, Volume and Issue: 145(3), P. 1151 - 1161

Published: Sept. 10, 2021

Abstract Preoperative MRI is one of the most important clinical results for diagnosis and treatment glioma patients. The objective this study was to construct a stable validatable preoperative T2-weighted MRI-based radiomics model predicting survival gliomas. A total 652 patients across three independent cohorts were covered in including their images, RNA-seq data. Radiomic features (1731) extracted from images 167 gliomas (discovery cohort) collected Beijing Tiantan Hospital then used develop prediction through machine learning-based method. performance validated two 261 Cancer Genomae Atlas database (external validation 224 prospective (prospective cohort). data discovery external applied establish relationship between biological function key features, which further by single-cell sequencing immunohistochemical staining. 14 radiomic features-based constructed cohort, showed highly robust predictive power overall cohorts. associated with immune response, especially tumour macrophage infiltration. can stably predict assist preoperatively assessing extent infiltration tumours.

Language: Английский

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Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm

European Radiology, Journal Year: 2021, Volume and Issue: 31(7), P. 4824 - 4838

Published: Jan. 14, 2021

Abstract Objectives To develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm. Methods Between March 2012 September 2019, 356 pathologically confirmed HCC cm who underwent gadoxetate disodium–enhanced MRI were retrospectively enrolled. MVI was graded as M0, M1, or M2 according to the number distribution of invaded vessels. Radiomics features extracted from DWI, arterial, portal venous, hepatobiliary phase images regions entire tumor, peritumoral area 10 mm, randomly selected liver tissue. Multivariate analysis identified independent predictors RFS, nomogram visualized ultimately predictive models. Results Elevated alpha-fetoprotein, total bilirubin radiomics values, enhancement, incomplete absent capsule enhancement risk factors MVI. The AUCs reached 0.920 (95% CI: 0.861–0.979) using random forest 0.879 0.820–0.938) logistic regression validation cohort ( n = 106). With 5-year RFS rate 68.4%, median MVI-positive (M2 M1) MVI-negative (M0) 30.5 (11.9 40.9) > 96.9 months p < 0.001), respectively. Age, histologic MVI, alkaline phosphatase, alanine aminotransferase independently predicted recurrence, yielding AUC 0.654 0.538–0.769, 99) cohort. Instead preoperatively by achieved comparable accuracy stratification prediction. Conclusions Preoperative is a potential biomarker Key Points • score predominant predictor which primary factor postoperative recurrence. either has obtained best so far. As an excellent substitute invasive (MVI-RF) outcome, reinforcing radiologic understanding angioinvasion progression.

Language: Английский

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Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

JHEP Reports, Journal Year: 2022, Volume and Issue: 4(4), P. 100443 - 100443

Published: Feb. 2, 2022

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum metabolic, infectious, autoimmune neoplastic diseases. Clinicians integrate qualitative quantitative information from multiple data sources to make diagnosis, prognosticate disease course, recommend treatment. In last 5 years, advances artificial intelligence (AI), particularly deep learning, have made it possible extract clinically relevant complex diverse clinical datasets. particular, histopathology radiology image contain diagnostic, prognostic predictive which AI can extract. Ultimately, such systems could be implemented as decision support tools. However, context hepatology, this requires further large-scale validation regulatory approval. Herein, we summarise state art with particular focus on data. We present roadmap for development novel biomarkers outline critical obstacles need overcome.

Language: Английский

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Frontiers in Oncology, Journal Year: 2022, Volume and Issue: 12

Published: Feb. 17, 2022

The high-throughput extraction of quantitative imaging features from medical images for the purpose radiomic analysis, i.e., radiomics in a broad sense, is rapidly developing and emerging research field that has been attracting increasing interest, particularly multimodality multi-omics studies. In this context, analysis multidimensional data plays an essential role assessing spatio-temporal characteristics different tissues organs their microenvironment. Herein, recent developments method, including manually defined features, acquisition preprocessing, lesion segmentation, feature extraction, selection dimension reduction, statistical model construction, are reviewed. addition, deep learning-based techniques automatic segmentation being analyzed to address limitations such as rigorous workflow, manual/semi-automatic annotation, inadequate criteria, multicenter validation. Furthermore, summary current state-of-the-art applications technology disease diagnosis, treatment response, prognosis prediction perspective radiology images, histopathology three-dimensional dose distribution data, oncology, presented. potential value diagnostic therapeutic strategies also further analyzed, first time, advances challenges associated with dosiomics radiotherapy summarized, highlighting latest progress radiomics. Finally, robust framework presented recommendations future development discussed, but not limited factors affect stability (medical big multitype expert knowledge medical), data-driven processes (reproducibility interpretability studies, alternatives various institutions, prospective researches clinical trials), thoughts on directions (the capability achieve open platform analysis).

Language: Английский

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Hepatocellular Carcinoma in 2021: An Exhaustive Update

Cureus, Journal Year: 2021, Volume and Issue: unknown

Published: Nov. 5, 2021

Primary liver cancer is a challenging global health concern with an estimated more than million persons to be affected annually by the year 2025. The commonest type hepatocellular carcinoma (HCC), which has been increasing in incidence world over, mostly due chronic viral hepatitis B infection. In last decade, paradigm changes etiology, understanding of molecular biology, and pathogenesis, including role gut microbiota; medical surgical treatments, outcome trends are notable. application omics-based technology helped us unlock immune landscape HCC, through novel targets for drug treatment such as immune-checkpoint inhibitors have identified. Novel tools surveillance diagnosis HCC include protein-, genomics-, composite algorithm-based clinical/biomarker panels. Magnetic resonance imaging-based techniques improved ancillary features that enhance classical criteria while positron emission tomography shown value prognostication. Identification microbiota causation progression opened areas therapeutic research. A select group patients still benefit from modified early interventional radiology treatments. Improvements radiotherapy protocols, identification parameters futility among radiological interventions, emergence first-line systemic therapies combination antiangiogenic seen change progression-free overall survival. current review aimed at providing exhaustive updates on biomarker diagnosis, imaging, recommended options HCC.

Language: Английский

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Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model

Radiology, Journal Year: 2023, Volume and Issue: 307(4)

Published: April 25, 2023

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials Methods Patients with pathologically proven HCC from May 2012 September 2020 were retrospectively included four medical centers. Radiomics features extracted tumors peritumor regions registration or subtraction images. In the training set, these used build five models via logistic regression after feature reduction. The tested using internal external test sets against pathologic reference standard calculate area under receiver operating characteristic curve (AUC). optimal AUC model clinical-radiologic characteristics combined hybrid model. log-rank was in outcome cohort (Kunming center) analyze early recurrence-free survival overall high versus low model-derived score. RNA sequencing data Cancer Image Archive gene expression analysis. Results A total 773 patients (median age, 59 years; IQR, 49–64 633 men) divided into set (n = 334), 142), 141), 121), analysis 35). AUCs models, respectively, 0.76 0.86 0.72 0.84 set. Early (P < .01) .007) can be categorized Differentially genes findings positive involved glucose metabolism. Conclusion showed best performance prediction MVI. © RSNA, 2023 Supplemental material is available this article. See also editorial by Summers issue.

Language: Английский

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CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma

Radiology, Journal Year: 2022, Volume and Issue: 307(1)

Published: Dec. 13, 2022

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT model predict the MTM investigate underlying immune infiltration patterns. Materials Methods This study included five retrospective data sets one prospective set from three academic medical centers between January 2015 December 2021. The preoperative liver contrast-enhanced studies 365 adult patients resected HCC were evaluated. Third Xiangya Hospital Central South University provided training internal test set, while Yueyang Hunan Cancer external sets. Radiomic features extracted used develop machine learning in performance was verified two outcomes cohort, including 58 advanced undergoing transarterial chemoembolization antiangiogenic therapy, evaluate predictive value for progression-free survival (PFS). Bulk RNA sequencing tumors 41 Genome Atlas (TCGA) single-cell seven prospectively enrolled participants radiomics-related Area under receiver operating characteristics curve calculated, Cox proportional regression performed identify predictors PFS. Results Among (mean age, 55 years ± 10 [SD]; 319 men) modeling, 122 (33%) confirmed have subtype. 11 radiomic showed good predicting subtype, AUCs 0.84, 0.80, 0.74 respectively. A low score relative median cohort independently PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). dysregulated humoral immunity involving B-cell immunoglobulin synthesis. Conclusion Accurate prediction macrotrabecular-massive achieved model, also defective immunity. Published CC BY 4.0 license. Supplemental material available this article. See editorial by Yoon Kim issue.

Language: Английский

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