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.

[1]  P. Lee,et al.  Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals , 2021, Cancer Imaging.

[2]  Robert M. Marks,et al.  LI-RADS Past, Present, and Future, From the AJR Special Series on Radiology Reporting and Data Systems. , 2020, AJR. American journal of roentgenology.

[3]  K. Carriere,et al.  Interobserver Variability and Diagnostic Performance of Gadoxetic Acid-enhanced MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma. , 2020, Radiology.

[4]  Myeong-Jin Kim,et al.  Gadoxetic acid-enhanced MRI of macrotrabecular-massive hepatocellular carcinoma and its prognostic implications. , 2020, Journal of hepatology.

[5]  M. Ronot,et al.  Similar performance of liver stiffness measurement and liver surface nodularity for the detection of portal hypertension in patients with hepatocellular carcinoma , 2020, JHEP reports : innovation in hepatology.

[6]  Kaiyu Wang,et al.  Prediction Model for Intermediate‐Stage Hepatocellular Carcinoma Response to Transarterial Chemoembolization , 2020, Journal of magnetic resonance imaging : JMRI.

[7]  A. Luciani,et al.  Multiphase Liver MRI for Identifying the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma. , 2020, Radiology.

[8]  M. Ronot,et al.  Relevance of liver surface nodularity for preoperative risk assessment in patients with resectable hepatocellular carcinoma , 2020, The British journal of surgery.

[9]  B. Taouli,et al.  MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma , 2020, European Radiology.

[10]  M. Ronot,et al.  Imaging of liver tumours: What’s new? , 2020, Liver international : official journal of the International Association for the Study of the Liver.

[11]  Huan Liu,et al.  MRI‐Based Radiomics: Associations With the Recurrence‐Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization , 2019, Journal of magnetic resonance imaging : JMRI.

[12]  Manal M. Hassan,et al.  A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. , 2019, Radiology. Artificial intelligence.

[13]  L. Schwartz,et al.  Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules , 2019, European Radiology.

[14]  Ho Sung Kim,et al.  Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives , 2019, Korean journal of radiology.

[15]  Jing Zhang,et al.  Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. , 2019, Journal of hepatology.

[16]  Hui Zhang,et al.  Development of a prognostic score for recommended TACE candidates with hepatocellular carcinoma: A multicentre observational study. , 2019, Journal of hepatology.

[17]  K. Ngiam,et al.  Big data and machine learning algorithms for health-care delivery. , 2019, The Lancet. Oncology.

[18]  Kathryn J Fowler,et al.  Accuracy of the Liver Imaging Reporting and Data System in Computed Tomography and Magnetic Resonance Image Analysis of Hepatocellular Carcinoma or Overall Malignancy-A Systematic Review. , 2019, Gastroenterology.

[19]  Myeong-Jin Kim,et al.  Evaluation of Early Response to Treatment of Hepatocellular Carcinoma with Yttrium-90 Radioembolization Using Quantitative Computed Tomography Analysis , 2019, Korean journal of radiology.

[20]  Raymond Y Huang,et al.  Artificial intelligence in cancer imaging: Clinical challenges and applications , 2019, CA: a cancer journal for clinicians.

[21]  Xin Li,et al.  Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI , 2019, European Radiology.

[22]  M. Abecassis,et al.  Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases , 2018, Hepatology.

[23]  R. Yeung,et al.  Precision oncology in liver cancer. , 2018, Annals of translational medicine.

[24]  A. Luciani,et al.  Macrotrabecular‐massive hepatocellular carcinoma: A distinctive histological subtype with clinical relevance , 2018, Hepatology.

[25]  A. Luciani,et al.  Advanced Hepatocellular Carcinoma: Pretreatment Contrast-enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib. , 2018, Radiology.

[26]  M. Abecassis,et al.  AASLD guidelines for the treatment of hepatocellular carcinoma , 2018, Hepatology.

[27]  D. Sinn,et al.  Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. , 2017, Journal of hepatology.

[28]  Xiaoping Liu,et al.  Genomic and Epigenomic Heterogeneity of Hepatocellular Carcinoma. , 2017, Cancer research.

[29]  R. Lencioni,et al.  Lipiodol transarterial chemoembolization for hepatocellular carcinoma: A systematic review of efficacy and safety data , 2016, Hepatology.

[30]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[31]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[32]  L Pagliaro,et al.  Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver. , 2001, Journal of hepatology.

[33]  G. Tourassi Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.