Radiomics in hepatocellular carcinoma: a quantitative review
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J. Marescaux | P. Savadjiev | B. Gallix | T. Wakabayashi | T. Baumert | V. Agnus | Farid Ouhmich | P. Pessaux | Antonio Saviano | E. Felli | Cristian González-Cabrera | Taiga Wakabayashi
[1] Stefan Klein,et al. Classification of malignant and benign liver tumors using a radiomics approach , 2018, Medical Imaging.
[2] Olivier Gevaert,et al. Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study , 2017, Journal of medical imaging.
[3] C. Pal,et al. Deep Learning: A Primer for Radiologists. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.
[4] M. van Glabbeke,et al. New guidelines to evaluate the response to treatment in solid tumors , 2000, Journal of the National Cancer Institute.
[5] Wei-Chung Hsu,et al. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy , 2017, BMC Cancer.
[6] Amber L. Simpson,et al. Cholangiocarcinoma: Correlation between Molecular Profiling and Imaging Phenotypes , 2015, PloS one.
[7] Philippe Lambin,et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures , 2017, The British journal of radiology.
[8] M. Dumont,et al. European Association for the Study of the Liver , 1971 .
[9] Olivier Gevaert,et al. Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma , 2015, Journal of medical imaging.
[10] Myeong-Jin Kim,et al. Prediction of microvascular invasion of hepatocellular carcinoma: Usefulness of peritumoral hypointensity seen on gadoxetate disodium‐enhanced hepatobiliary phase images , 2012, Journal of magnetic resonance imaging : JMRI.
[11] B. Taouli,et al. Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results , 2017, European Radiology.
[12] Stephen M. Moore,et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.
[13] Xiaobo Zhou,et al. Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data—a preliminary study , 2018, Physics in medicine and biology.
[14] V. Goh,et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.
[15] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[16] Chiun Hsu,et al. Tumor Heterogeneity in Hepatocellular Carcinoma: Facing the Challenges , 2016, Liver Cancer.
[17] Biopsy for liver cancer: How to balance research needs with evidence‐based clinical practice , 2015, Hepatology.
[18] G. Parker,et al. Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome , 2014, Clinical Cancer Research.
[19] H. Hasegawa,et al. Natural history of hepatocellular carcinoma and prognosis in relation to treatment study of 850 patients , 1985, Cancer.
[20] A. Rutman,et al. A Computed Tomography Radiogenomic Biomarker Predicts Microvascular Invasion and Clinical Outcomes in Hepatocellular Carcinoma , 2015, Hepatology.
[21] Jae H Park,et al. The Clinical Implications of Liver Resection Margin Size in Patients with Hepatocellular Carcinoma in Terms of Positron Emission Tomography Positivity , 2018, World Journal of Surgery.
[22] C. Reinhold,et al. Pancreatic adenocarcinoma: A simple CT score for predicting margin-positive resection in patients with resectable disease. , 2017, European journal of radiology.
[23] A. Satterfield,et al. TREATMENT , 1924, California and western medicine.
[24] Jaron J. R. Chong,et al. Demystification of AI-driven medical image interpretation: past, present and future , 2018, European Radiology.
[25] Charles Swanton,et al. Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine , 2014, Genome Biology.
[26] C. Liang,et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast‐enhanced MR images , 2017, Journal of magnetic resonance imaging : JMRI.
[27] N. Houssami,et al. Rapid review: radiomics and breast cancer , 2018, Breast Cancer Research and Treatment.
[28] B. Taouli,et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging , 2017, Scientific Reports.
[29] Bernardino,et al. Prospective validation of the CLIP score: A new prognostic system for patients with cirrhosis and hepatocellular carcinoma , 2000, Hepatology.
[30] M Van Glabbeke,et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. , 2000, Journal of the National Cancer Institute.
[31] Xavier Geets,et al. Radiomics applied to lung cancer: a review , 2016 .
[32] S. Johnston,et al. Imaging in oncology—over a century of advances , 2012, Nature Reviews Clinical Oncology.
[33] M. Kohli,et al. Response criteria in oncologic imaging: review of traditional and new criteria. , 2013, Radiographics : a review publication of the Radiological Society of North America, Inc.
[34] V. Alves,et al. Histological Grading of Hepatocellular Carcinoma—A Systematic Review of Literature , 2017, Front. Med..
[35] Riccardo Lencioni,et al. Modified RECIST (mRECIST) Assessment for Hepatocellular Carcinoma , 2010, Seminars in liver disease.
[36] Jia Fan,et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients , 2018, BMC Cancer.
[37] A. Jemal,et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.
[38] M. Kuo,et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. , 2007, Journal of vascular and interventional radiology : JVIR.
[39] Christos Davatzikos,et al. Imaging genomics in cancer research: limitations and promises. , 2016, The British journal of radiology.
[40] Zaiyi Liu,et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma , 2017, Abdominal Radiology.
[41] Myeong-Jin Kim,et al. Liver imaging reporting and data system (LI-RADS) version 2014: understanding and application of the diagnostic algorithm , 2016, Clinical and molecular hepatology.
[42] A. Miller,et al. Reporting results of cancer treatment , 1981, Cancer.
[43] H. Hricak. Oncologic imaging: a guiding hand of personalized cancer care. , 2011, Radiology.
[44] Shaocheng Zhu,et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature , 2018, European Radiology.
[45] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[46] Elliot K Fishman,et al. Preliminary Data Using Computed Tomography Texture Analysis for the Classification of Hypervascular Liver Lesions: Generation of a Predictive Model on the Basis of Quantitative Spatial Frequency Measurements—A Work in Progress , 2015, Journal of computer assisted tomography.
[47] Bellia Mario,et al. Prospective validation of the CLIP score, a new prognostic system for cirrhotic patients with hepatocellular carcinoma (RE: Hepatology 2000, 32/3: 679-80) , 2000 .
[48] G. Gores,et al. Hepatocellular carcinoma: clinical frontiers and perspectives , 2014, Gut.
[49] Jing Zhang,et al. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. , 2018, Diagnostic and interventional radiology.
[50] Robert M. Marks,et al. Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review. , 2018, Radiology.
[51] Eun Sook Ko,et al. Radiomics and imaging genomics in precision medicine , 2017 .
[52] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[53] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[54] C. Liang,et al. Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma , 2016, Oncotarget.
[55] P. Schirmacher,et al. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. , 2018, Journal of hepatology.
[56] P. Savadjiev,et al. Image-based biomarkers for solid tumor quantification , 2019, European Radiology.
[57] Xiaoping Liu,et al. Genomic and Epigenomic Heterogeneity of Hepatocellular Carcinoma. , 2017, Cancer research.
[58] Ahmed Hosny,et al. Artificial intelligence in radiology , 2018, Nature Reviews Cancer.
[59] J. Bruix,et al. Prognosis of Hepatocellular Carcinoma: The BCLC Staging Classification , 1999, Seminars in liver disease.
[60] H. Aerts. The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. , 2016, JAMA oncology.
[61] Stefano Brocchi,et al. Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma? , 2016, Radiology.
[62] John O. Prior,et al. Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma , 2017, Oncotarget.
[63] T. Beyer,et al. Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning , 2017, The Journal of Nuclear Medicine.
[64] Neema Jamshidi,et al. Radiomics and radiogenomics of primary liver cancers , 2018, Clinical and molecular hepatology.
[65] Aya Kamaya,et al. 2017 Version of LI-RADS for CT and MR Imaging: An Update. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.
[66] F. Farinati,et al. How should patients with hepatocellular carcinoma be staged? , 2000, Cancer.
[67] Kathryn J Fowler,et al. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. , 2018, Radiology.
[68] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[69] D. Moher,et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.
[70] Chad A Holder,et al. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma , 2016, BMC Cancer.
[71] Hiroshi Tanaka,et al. Distinct clinicopathological phenotype of hepatocellular carcinoma with ethoxybenzyl-magnetic resonance imaging hyperintensity: association with gene expression signature. , 2015, American journal of surgery.
[72] Yanjie Zhu,et al. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: A retrospective pilot study. , 2017, European journal of radiology.
[73] D. Moher,et al. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement , 2009, BMJ : British Medical Journal.
[74] H. Hricak,et al. Background, current role, and potential applications of radiogenomics , 2018, Journal of magnetic resonance imaging : JMRI.
[75] O. Abe,et al. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. , 2018, Diagnostic and interventional imaging.
[76] Michael A Jacobs,et al. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI , 2017, npj Breast Cancer.
[77] Howard Y. Chang,et al. Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.
[78] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[79] L. Mariani,et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. , 2009, The Lancet. Oncology.