Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 231-245 |
| Number of pages | 15 |
| Journal | World Journal of Hepatology |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 25 2018 |
Keywords
- Bio-imaging
- Biobank
- Fatty liver
- Genomics
- Liver biopsy
- Liver cancer
- Magnetic resonance
- Non-alcoholic fatty liver disease
- Non-alcoholic steatohepatitis
- Radiomics
- Ultrasound
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Digital liver biopsy: Bio-imaging of fatty liver for translational and clinical research. / Mancini, M.; Summers, P.; Faita, F.; Brunetto, M.R.; Callea, F.; De Nicola, A.; Di Lascio, N.; Farinati, F.; Gastaldelli, A.; Gridelli, B.; Mirabelli, P.; Neri, E.; Salvadori, P.A.; Rebelos, E.; Tiribelli, C.; Valenti, L.; Salvatore, M.; Bonino, F.
In: World Journal of Hepatology, Vol. 10, No. 2, 25.04.2018, p. 231-245.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Digital liver biopsy: Bio-imaging of fatty liver for translational and clinical research
AU - Mancini, M.
AU - Summers, P.
AU - Faita, F.
AU - Brunetto, M.R.
AU - Callea, F.
AU - De Nicola, A.
AU - Di Lascio, N.
AU - Farinati, F.
AU - Gastaldelli, A.
AU - Gridelli, B.
AU - Mirabelli, P.
AU - Neri, E.
AU - Salvadori, P.A.
AU - Rebelos, E.
AU - Tiribelli, C.
AU - Valenti, L.
AU - Salvatore, M.
AU - Bonino, F.
N1 - Cited By :1 Export Date: 25 January 2019 Correspondence Address: Bonino, F.; Institute for Health, University of Pittsburgh Medical Center (UPMC), Lungarno Bruno Buozzi 13, Italy; email: ferruccio.bonino@unipi.it References: Morgagni, G.B., Founders of Modern Medicine: Giovanni Battista Morgagni. (1682-1771) (1903) Med Library Hist J, 1, pp. 270-277. , PMID: 18340813; Menghini, G., One-second needle biopsy of the l iver (1958) Gastroenterology, 35, pp. 190-199. , PMID: 13562404; Ratziu, V., Charlotte, F., Heurtier, A., Gombert, S., Giral, P., Bruckert, E., Grimaldi, A., Poynard, T., Sampling variability of liver biopsy in nonalcoholic fatty liver disease (2005) Gastroenterology, 128, pp. 1898-1906. , PMID: 15940625; Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S.A., Schabath, M.B., Forster, K., Gillies, R.J., Radiomics: The process and the challenges (2012) Magn Reson Imaging, 30, pp. 1234-1248. , PMID: 22898692; Parmar, C., Rios Velazquez, E., Leijenaar, R., Jermoumi, M., Carvalho, S., Mak, R.H., Mitra, S., Aerts, H.J., Robust Radiomics feature quantification using semiautomatic volumetric segmentation (2014) PLoS One, 9, p. e102107. , PMID: 25025374; Echegaray, S., Nair, V., Kadoch, M., Leung, A., Rubin, D., Gevaert, O., Napel, S., A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer (2016) Tomography, 2, pp. 283-294. , PMID: 28612050; Madabhushi, A., Agner, S., Basavanhally, A., Doyle, S., Lee, G., Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data (2011) Comput Med Imaging Graph, 35, pp. 506-514. , PMID: 21333490; Gu, Y., Kumar, V., Hall, L.O., Goldgof, D.B., Li, C.Y., Korn, R., Bendtsen, C., Gillies, R.J., Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach (2013) Pattern Recognit, 46, pp. 692-702. , PMID: 23459617; Parmar, C., Grossmann, P., Bussink, J., Lambin, P., Aerts, H.J., Machine Learning methods for Quantitative Radiomic Biomarkers (2015) Sci Rep, 5, p. 13087. , PMID: 26278466; Horsch, K., Giger, M.L., Venta, L.A., Vyborny, C.J., Automatic segmentation of breast lesions on ultrasound (2001) Med Phys, 28, pp. 1652-1659. , PMID: 11548934; Hermoye, L., Laamari-azjal, I., Cao, Z., Annet, L., Lerut, J., Dawant, B.M., Van Beers, B.E., Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods (2005) Radiology, 234, pp. 171-178. , PMID: 15564393; Zhang, X., Tian, J., Xiang, D., Li, X., Deng, K., Interactive liver tumor segmentation from ct scans using support vector classification with watershed (2011) Conf Proc IEEE Eng Med Biol Soc, 2011, pp. 6005-6008. , PMID: 22255708; Itakura, H., Achrol, A.S., Mitchell, L.A., Loya, J.J., Liu, T., Westbroek, E.M., Feroze, A.H., Gevaert, O., Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities (2015) Sci Transl Med, 7. , 303ra138 PMID: 26333934; Gevaert, O., Xu, J., Hoang, C.D., Leung, A.N., Xu, Y., Quon, A., Rubin, D.L., Plevritis, S.K., Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results (2012) Radiology, 264, pp. 387-396. , PMID: 22723499; Parmar, C., Leijenaar, R.T., Grossmann, P., Rios Velazquez, E., Bussink, J., Rietveld, D., Rietbergen, M.M., Aerts, H.J., Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer (2015) Sci Rep, 5, p. 11044. , PMID: 26251068; Chan, H.P., Doi, K., Galhotra, S., Vyborny, C.J., MacMahon, H., Jokich, P.M., Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography (1987) Med Phys, 14, pp. 538-548. , PMID: 3626993; Lambin, P., Rios-velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G., Granton, P., Zegers, C.M., Aerts, H.J., Radiomics: extracting more information from medical images using advanced feature analysis (2012) Eur J Cancer, 48, pp. 441-446. , PMID: 22257792; Petta, S., Valenti, L., Bugianesi, E., Targher, G., Bellentani, S., Bonino, F., A "systems medicine" approach to the study of non-alcoholic fatty liver disease (2016) Dig Liver Dis, 48, pp. 333-342. , PMID: 26698409; AISF position paper on nonalcoholic fatty liver disease (NAFLD): Updates and future directions (2017) Dig Liver Dis, 49, pp. 471-483. , PMID: 28215516; Hamaguchi, M., Kojima, T., Itoh, Y., Harano, Y., Fujii, K., Nakajima, T., Kato, T., Okanoue, T., The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation (2007) Am J Gastroenterol, 102, pp. 2708-2715. , PMID: 17894848; Mirabelli, P., Incoronato, M.R., Coppola, L., Infante, T., Parente, C.A., Nicolai, E., Soricelli, A., Salvatore, M., Bioresource of Human Samples Associated with Functional and/or Morphological Bioimaging Results for the Study of Oncological, Cardiological, Neurological, and Metabolic Diseases (2017) Open Journal of Bioresources, 4, p. 2; Napel, S., Giger, M., Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine (2015) J Med Imaging, 2, p. 041001. , Bellingham, [PMID: 26839908]; Doi, K., Computer-aided diagnosis in medical imaging: historical review, current status and future potential (2007) Comput Med Imaging Graph, 31, pp. 198-211. , PMID: 17349778; Banerjee, R., Pavlides, M., Tunnicliffe, E.M., Piechnik, S.K., Sarania, N., Philips, R., Collier, J.D., Neubauer, S., Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease (2014) J Hepatol, 60, pp. 69-77. , PMID: 24036007; Cherry, S.R., In vivo molecular and genomic imaging: new challenges for imaging physics (2004) Phys Med Biol, 49, pp. R13-R48. , PMID: 15012005; Pavlides, M., Banerjee, R., Sellwood, J., Kelly, C.J., Robson, M.D., Booth, J.C., Collier, J., Barnes, E., Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease (2016) J Hepatol, 64, pp. 308-315. , PMID: 26471505; Iverson, S.J., Lang, S.L., Cooper, M.H., Comparison of the Bligh and Dyer and Folch methods for total lipid determination in a broad range of marine tissue (2001) Lipids, 36, pp. 1283-1287. , PMID: 11795862; Bohte, A.E., de Niet, A., Jansen, L., Bipat, S., Nederveen, A.J., Verheij, J., Terpstra, V., Stoker, J., Non-invasive evaluation of liver fibrosis: a comparison of ultrasound-based transient elastography and MR elastography in patients with viral hepatitis B and C (2014) Eur Radiol, 24, pp. 638-648. , PMID: 24158528; Abenavoli, L., Beaugrand, M., Transient elastography in non-alcoholic fatty liver disease (2012) Ann Hepatol, 11, pp. 172-178. , PMID: 22345333; Robic, M.A., Procopet, B., Métivier, S., Péron, J.M., Selves, J., Vinel, J.P., Bureau, C., Liver stiffness accurately predicts portal hypertension related complications in patients with chronic liver disease: a prospective study (2011) J Hepatol, 55, pp. 1017-1024. , PMID: 21354450; Cassinotto, C., Lapuyade, B., Mouries, A., Hiriart, J.B., Vergniol, J., Gaye, D., Castain, C., De Ledinghen, V., Non-invasive assessment of liver fibrosis with impulse elastography: comparison of Supersonic Shear Imaging with ARFI and FibroScan® (2014) J Hepatol, 61, pp. 550-557. , PMID: 24815876; Asrani, S.K., Talwalkar, J.A., Kamath, P.S., Shah, V.H., Saracino, G., Jennings, L., Gross, J.B., Ehman, R.L., Role of magnetic resonance elastography in compensated and decompensated liver disease (2014) J Hepatol, 60, pp. 934-939. , PMID: 24362072; Coco, B., Oliveri, F., Maina, A.M., Ciccorossi, P., Sacco, R., Colombatto, P., Bonino, F., Brunetto, M.R., Transient elastography: a new surrogate marker of liver fibrosis influenced by major changes of transaminases (2007) J Viral Hepat, 14, pp. 360-369. , PMID: 17439526; Bonino, F., Arena, U., Brunetto, M.R., Coco, B., Fraquelli, M., Oliveri, F., Pinzani, M., Vizzuti, F., Liver stiffness, a non-invasive marker of liver disease: a core study group report (2010) Antivir Ther, 15, pp. 69-78. , PMID: 21041906; Castéra, L., Foucher, J., Bernard, P.H., Carvalho, F., Allaix, D., Merrouche, W., Couzigou, P., de Lédinghen, V., Pitfalls of liver stiffness measurement: a 5-year prospective study of 13,369 examinations (2010) Hepatology, 51, pp. 828-835. , PMID: 20063276; Nascimbeni, F., Lebray, P., Fedchuk, L., Oliveira, C.P., Alvares-da-silva, M.R., Varault, A., Ingiliz, P., Ratziu, V., Significant variations in elastometry measurements made within short-term in patients with chronic liver diseases (2015) Clin Gastroenterol Hepatol, 13, pp. 763-771. , e1-6 [PMID: 25086193]; Lee, M.S., Bae, J.M., Joo, S.K., Woo, H., Lee, D.H., Jung, Y.J., Kim, B.G., Kim, W., Prospective comparison among transient elastography, supersonic shear imaging, and ARFI imaging for predicting fibrosis in nonalcoholic fatty liver disease (2017) PLoS One, 12, p. e0188321. , PMID: 29176844; Ichikawa, S., Motosugi, U., Morisaka, H., Sano, K., Ichikawa, T., Tatsumi, A., Enomoto, N., Onishi, H., Comparison of the diagnostic accuracies of magnetic resonance elastography and transient elastography for hepatic fibrosis (2015) Magn Reson Imaging, 33, pp. 26-30. , PMID: 25308096; Park, C.C., Nguyen, P., Hernandez, C., Bettencourt, R., Ramirez, K., Fortney, L., Hooker, J., Loomba, R., Magnetic Resonance Elastography vs Transient Elastography in Detection of Fibrosis and Noninvasive Measurement of Steatosis in Patients With Biopsy-Proven Nonalcoholic Fatty Liver Disease (2017) Gastroenterology, 152, pp. 598-607. , e2 [PMID: 27911262]; Dulai, P.S., Sirlin, C.B., Loomba, R., MRI and MRE for non-invasive quantitative assessment of hepatic steatosis and fibrosis in NAFLD and NASH: Clinical trials to clinical practice (2016) J Hepatol, 65, pp. 1006-1016. , PMID: 27312947; Hoad, C.L., Palaniyappan, N., Kaye, P., Chernova, Y., James, M.W., Costigan, C., Austin, A., Aithal, G.P., A study of T1 relaxation time as a measure of liver fibrosis and the influence of confounding histological factors (2015) NMR Biomed, 28, pp. 706-714. , PMID: 25908098; Heye, T., Yang, S.R., Bock, M., Brost, S., Weigand, K., Longerich, T., Kauczor, H.U., Hosch, W., MR relaxometry of the liver: significant elevation of T1 relaxation time in patients with liver cirrhosis (2012) Eur Radiol, 22, pp. 1224-1232. , PMID: 22302503; Torok, N.J., Dranoff, J.A., Schuppan, D., Friedman, S.L., Strategies and endpoints of antifibrotic drug trials: Summary and recommendations from the AASLD Emerging Trends Conference, Chicago, June 2014 (2015) Hepatology, 62, pp. 627-634. , PMID: 25626988; Gandon, Y., Olivié, D., Guyader, D., Aubé, C., Oberti, F., Sebille, V., Deugnier, Y., Non-invasive assessment of hepatic iron stores by MRI (2004) Lancet, 363, pp. 357-362. , PMID: 15070565; Reeder, S.B., Cruite, I., Hamilton, G., Sirlin, C.B., Quantitative Assessment of Liver Fat with Magnetic Resonance Imaging and Spectroscopy (2011) J Magn Reson Imaging, 34. , spcone [PMID: 22025886]; Longo, R., Pollesello, P., Ricci, C., Masutti, F., Kvam, B.J., Bercich, L., Crocè, L.S., de Bernard, B., Proton MR spectroscopy in quantitative in vivo determination of fat content in human liver steatosis (1995) J Magn Reson Imaging, 5, pp. 281-285. , PMID: 7633104; Qayyum, A., MR spectroscopy of the liver: principles and clinical applications (2009) Radiographics, 29, pp. 1653-1664. , PMID: 19959513; Dixon, W.T., Simple proton spectroscopic imaging (1984) Radiology, 153, pp. 189-194. , PMID: 6089263; Yu, H., McKenzie, C.A., Shimakawa, A., Vu, A.T., Brau, A.C., Beatty, P.J., Pineda, A.R., Reeder, S.B., Multiecho reconstruction for simultaneous water-fat decomposition and T2* estimation (2007) J Magn Reson Imaging, 26, pp. 1153-1161. , PMID: 17896369; Bydder, M., Yokoo, T., Hamilton, G., Middleton, M.S., Chavez, A.D., Schwimmer, J.B., Lavine, J.E., Sirlin, C.B., Relaxation effects in the quantification of fat using gradient echo imaging (2008) Magn Reson Imaging, 26, pp. 347-359. , PMID: 18093781; Chebrolu, V.V., Hines, C.D., Yu, H., Pineda, A.R., Shimakawa, A., McKenzie, C.A., Samsonov, A., Reeder, S.B., Independent estimation of T*2 for water and fat for improved accuracy of fat quantification (2010) Magn Reson Med, 63, pp. 849-857. , PMID: 20373385; Horng, D.E., Hernando, D., Hines, C.D., Reeder, S.B., Comparison of R2* correction methods for accurate fat quantification in fatty liver (2013) J Magn Reson Imaging, 37, pp. 414-422. , PMID: 23165934; Zhong, X., Nickel, M.D., Kannengiesser, S.A., Dale, B.M., Kiefer, B., Bashir, M.R., Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging (2014) Magn Reson Med, 72, pp. 1353-1365. , PMID: 24323332; Bashir, M.R., Zhong, X., Nickel, M.D., Fananapazir, G., Kannengiesser, S.A., Kiefer, B., Dale, B.M., Quantification of hepatic steatosis with a multistep adaptive fitting MRI approach: prospective validation against MR spectroscopy (2015) AJR Am J Roentgenol, 204, pp. 297-306. , PMID: 25615751; Sofue, K., Mileto, A., Dale, B.M., Zhong, X., Bashir, M.R., Inter-examination repeatability and spatial heterogeneity of liver iron and fat quantification using MRI-based multistep adaptive fitting algorithm (2015) J Magn Reson Imaging, 42, pp. 1281-1290. , PMID: 25920074; Jiménez-agüero, R., Emparanza, J.I., Beguiristain, A., Bujanda, L., Alustiza, J.M., García, E., Hijona, E., Banales, J.M., Novel equation to determine the hepatic triglyceride concentration in humans by MRI: diagnosis and monitoring of NAFLD in obese patients before and after bariatric surgery (2014) BMC Med, 12, p. 137. , PMID: 25164060; Folch, J., Lees, M., Sloane stanley, G.H., A simple method for the isolation and purification of total lipides from animal tissues (1957) J Biol Chem, 226, pp. 497-509. , PMID: 13428781; Middleton, M.S., Heba, E.R., Hooker, C.A., Bashir, M.R., Fowler, K.J., Sandrasegaran, K., Brunt, E.M., Sirlin, C.B., Agreement Between Magnetic Resonance Imaging Proton Density Fat Fraction Measurements and Pathologist-Assigned Steatosis Grades of Liver Biopsies From Adults With Nonalcoholic Steatohepatitis (2017) Gastroenterology, 153, pp. 753-761. , PMID: 28624576; Sigrist, R.M.S., Liau, J., Kaffas, A.E., Chammas, M.C., Willmann, J.K., Ultrasound Elastography: Review of Techniques and Clinical Applications (2017) Theranostics, 7, pp. 1303-1329. , PMID: 28435467; Mancini, M., Prinster, A., Annuzzi, G., Liuzzi, R., Giacco, R., Medagli, C., Cremone, M., Salvatore, M., Sonographic hepatic-renal ratio as indicator of hepatic steatosis: comparison with (1)H magnetic resonance spectroscopy (2009) Metabolism, 58, pp. 1724-1730. , PMID: 19716568; Xia, M.F., Yan, H.M., He, W.Y., Li, X.M., Li, C.L., Yao, X.Z., Li, R.K., Gao, X., Standardized ultrasound hepatic/renal ratio and hepatic attenuation rate to quantify liver fat content: an improvement method (2012) Obesity, 20, pp. 444-452. , Silver Spring, [PMID: 22016092]; Edens, M.A., van Ooijen, P.M., Post, W.J., Haagmans, M.J., Kristanto, W., Sijens, P.E., van der Jagt, E.J., Stolk, R.P., Ultrasonography to quantify hepatic fat content: validation by 1H magnetic resonance spectroscopy (2009) Obesity, 17, pp. 2239-2244. , Silver Spring, [PMID: 19461588]; Karlas, T., Petroff, D., Sasso, M., Fan, J.G., Mi, Y.Q., de Lédinghen, V., Kumar, M., Wiegand, J., Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis (2017) J Hepatol, 66, pp. 1022-1030. , PMID: 28039099; Caussy, C., Alquiraish, M.H., Nguyen, P., Hernandez, C., Cepin, S., Fortney, L.E., Ajmera, V., Loomba, R., Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis (2017) Hepatology, , Epub ahead of print [PMID: 29108123]; Di Lascio, N., Avigo, C., Salvati, A., Martini, N., Ragucci, M., Monti, S., Prinster, A., Faita, F., Steato-score: non-invasive quantitative assessment of liver fat by ultrasound imaging (2018) Ultrasound Med Biol; Gao, S., Peng, Y., Guo, H., Liu, W., Gao, T., Xu, Y., Tang, X., Texture analysis and classification of ultrasound liver images (2014) Biomed Mater Eng, 24, pp. 1209-1216. , PMID: 24212015; Dasarathy, S., Dasarathy, J., Khiyami, A., Joseph, R., Lopez, R., McCullough, A.J., Validity of real time ultrasound in the diagnosis of hepatic steatosis: a prospective study (2009) J Hepatol, 51, pp. 1061-1067. , PMID: 19846234; Lin, C.Y., Lin, W.Y., Lin, C.C., Shih, C.M., Jeng, L.B., Kao, C.H., The negative impact of fatty liver on maximum standard uptake value of liver on FDG PET (2011) Clin Imaging, 35, pp. 437-441. , PMID: 22040787; Keramida, G., Potts, J., Bush, J., Verma, S., Dizdarevic, S., Peters, A.M., Accumulation of (18)F-FDG in the liver in hepatic steatosis (2014) AJR Am J Roentgenol, 203, pp. 643-648. , PMID: 25148170; Abele, J.T., Fung, C.I., Effect of hepatic steatosis on liver FDG uptake measured in mean standard uptake values (2010) Radiology, 254, pp. 917-924. , PMID: 20177102; Keyes, J.W., Jr., SUV: standard uptake or silly useless value? (1995) J Nucl Med, 36, pp. 1836-1839. , PMID: 7562051; Boellaard, R., Standards for PET image acquisition and quantitative data analysis (2009) J Nucl Med, 50, pp. 11s-20s. , PMID: 19380405; Lee, H.J., Lee, C.H., Kim, S., Hwang, S.Y., Hong, H.C., Choi, H.Y., Chung, H.S., Choi, K.M., Association between vascular inflammation and non-alcoholic fatty liver disease: Analysis by 18F-fluorodeoxyglucose positron emission tomography (2017) Metabolism, 67, pp. 72-79. , PMID: 28081780; Dimitriu-leen, A.C., Scholte, A.J.H.A., Hepatic FDG uptake in patients with NAFLD: An important prognostic factor for cardio-cerebrovascular events? (2017) J Nucl Cardiol, 24, pp. 900-902. , PMID: 26728014; Moon, S.H., Hong, S.P., Cho, Y.S., Noh, T.S., Choi, J.Y., Kim, B.T., Lee, K.H., Hepatic FDG uptake is associated with future cardiovascular events in asymptomatic individuals with non-alcoholic fatty liver disease (2017) J Nucl Cardiol, 24, pp. 892-899. , PMID: 26510948; Borra, R., Lautamäki, R., Parkkola, R., Komu, M., Sijens, P.E., Hällsten, K., Bergman, J., Nuutila, P., Inverse association between liver fat content and hepatic glucose uptake in patients with type 2 diabetes mellitus (2008) Metabolism, 57, pp. 1445-1451. , PMID: 18803951; Iozzo, P., Geisler, F., Oikonen, V., Mäki, M., Takala, T., Solin, O., Ferrannini, E., Nuutila, P., Insulin stimulates liver glucose uptake in humans: an 18F-FDG PET Study (2003) J Nucl Med, 44, pp. 682-689. , PMID: 12732668; Immonen, H., Hannukainen, J.C., Kudomi, N., Pihlajamäki, J., Saunavaara, V., Laine, J., Salminen, P., Nuutila, P., Increased Liver Fatty Acid Uptake Is Partly Reversed and Liver Fat Content Normalized After Bariatric Surgery (2018) Diabetes Care, 41, pp. 368-371. , PMID: 29158250; Iozzo, P., Bucci, M., Roivainen, A., Någren, K., Järvisalo, M.J., Kiss, J., Guiducci, L., Nuutila, P., Fatty acid metabolism in the liver, measured by positron emission tomography, is increased in obese individuals (2010) Gastroenterology, 139, pp. 846-856. , 856.e1-856.e6 [PMID: 20685204]; Kudomi, N., Slimani, L., Järvisalo, M.J., Kiss, J., Lautamäki, R., Naum, G.A., Savunen, T., Iozzo, P., Non-invasive estimation of hepatic blood perfusion from H2 15O PET images using tissue-derived arterial and portal input functions (2008) Eur J Nucl Med Mol Imaging, 35, pp. 1899-1911. , PMID: 18458902; Groß, C., Steiger, K., Sayyed, S., Heid, I., Feuchtinger, A., Walch, A., Heß, J., Braren, R., Model Matters: Differences in Orthotopic Rat Hepatocellular Carcinoma Physiology Determine Therapy Response to Sorafenib (2015) Clin Cancer Res, 21, pp. 4440-4450. , PMID: 25995341; Sung, P.S., Park, H.L., Yang, K., Hwang, S., Song, M.J., Jang, J.W., Choi, J.Y., Bae, S.H., 18F-fluorodeoxyglucose uptake of hepatocellular carcinoma as a prognostic predictor in patients with sorafenib treatment (2018) Eur J Nucl Med Mol Imaging, 45, pp. 384-391. , PMID: 29124280; Lee, J.H., Park, J.Y., Kim, D.Y., Ahn, S.H., Han, K.H., Seo, H.J., Lee, J.D., Choi, H.J., Prognostic value of 18F-FDG PET for hepatocellular carcinoma patients treated with sorafenib (2011) Liver Int, 31, pp. 1144-1149. , PMID: 21745288; Siemerink, E.J., Mulder, N.H., Brouwers, A.H., Hospers, G.A., 18F-Fluorodeoxyglucose positron emission tomography for monitoring response to sorafenib treatment in patients with hepatocellular carcinoma (2008) Oncologist, 13, pp. 734-735. , author reply 736-737 [PMID: 18586929]; Tseng, J.C., Narayanan, N., Ho, G., Groves, K., Delaney, J., Bao, B., Zhang, J., Peterson, J.D., Fluorescence imaging of bombesin and transferrin receptor expression is comparable to 18F-FDG PET in early detection of sorafenib-induced changes in tumor metabolism (2017) PLoS One, 12, p. e0182689. , PMID: 28792505; Takahashi, Y., Sano, R., Nakajima, T., Kominato, Y., Kubo, R., Takahashi, K., Ohshima, N., Ishige, T., Combination of postmortem mass spectrometry imaging and genetic analysis reveals very long-chain acyl-CoA dehydrogenase deficiency in a case of infant death with liver steatosis (2014) Forensic Sci Int, 244, pp. e34-e37. , PMID: 25242572; Le Naour, F., Bralet, M.P., Debois, D., Sandt, C., Guettier, C., Dumas, P., Brunelle, A., Laprévote, O., Chemical imaging on liver steatosis using synchrotron infrared and ToF-SIMS microspectroscopies (2009) PLoS One, 4, p. e7408. , PMID: 19823674; Kuo, M.D., Gollub, J., Sirlin, C.B., Ooi, C., Chen, X., Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma (2007) J Vasc Interv Radiol, 18, pp. 821-831. , PMID: 17609439; Sørensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J.H., de Bruijne, M., Texture-based analysis of COPD: a data-driven approach (2012) IEEE Trans Med Imaging, 31, pp. 70-78. , PMID: 21859615; Vanderbeck, S., Bockhorst, J., Kleiner, D., Komorowski, R., Chalasani, N., Gawrieh, S., Automatic quantification of lobular inflammation and hepatocyte ballooning in non alcoholic fatty liver disease liver biopsies (2015) Hum Pathol, 46, pp. 767-775. , PMID:2577603; Divine, M.R., Katiyar, P., Kohlhofer, U., Quintanilla-martinez, L., Pichler, B.J., Disselhorst, J.A., A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes (2016) J Nucl Med, 57, pp. 473-479. , PMID: 26659350; Messiou, C., Orton, M., Ang, J.E., Collins, D.J., Morgan, V.A., Mears, D., Castellano, I., deSouza, N.M., Advanced solid tumors treated with cediranib: comparison of dynamic contrast-enhanced MR imaging and CT as markers of vascular activity (2012) Radiology, 265, pp. 426-436. , PMID: 22891356; Son, J.Y., Lee, H.Y., Kim, J.H., Han, J., Jeong, J.Y., Lee, K.S., Kwon, O.J., Shim, Y.M., Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping (2016) Eur Radiol, 26, pp. 43-54. , PMID: 25981222; Lee, J., Kim, S.H., Kang, T.W., Song, K.D., Choi, D., Jang, K.T., Massforming Intrahepatic Cholangiocarcinoma: Diffusion-weighted Imaging as a Preoperative Prognostic Marker (2016) Radiology, 281, pp. 119-128. , PMID: 27115053; Kim, K.A., Kim, M.J., Jeon, H.M., Kim, K.S., Choi, J.S., Ahn, S.H., Cha, S.J., Chung, Y.E., Prediction of microvascular invasion of hepatocellular carcinoma: usefulness of peritumoral hypointensity seen on gadoxetate disodium-enhanced hepatobiliary phase images (2012) J Magn Reson Imaging, 35, pp. 629-634. , PMID: 22069244; Miura, T., Ban, D., Tanaka, S., Mogushi, K., Kudo, A., Matsumura, S., Mitsunori, Y., Tanabe, M., Distinct clinicopathological phenotype of hepatocellular carcinoma with ethoxybenzyl-magnetic resonance imaging hyperintensity: association with gene expression signature (2015) Am J Surg, 210, pp. 561-569. , PMID: 26105803; Zhou, Y., He, L., Huang, Y., Chen, S., Wu, P., Ye, W., Liu, Z., Liang, C., CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma (2017) Abdom Radiol, 42, pp. 1695-1704. , NY, [PMID: 28180924]; Romeo, S., Kozlitina, J., Xing, C., Pertsemlidis, A., Cox, D., Pennacchio, L.A., Boerwinkle, E., Hobbs, H.H., Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease (2008) Nat Genet, 40, pp. 1461-1465. , PMID: 18820647; Kozlitina, J., Smagris, E., Stender, S., Nordestgaard, B.G., Zhou, H.H., Tybjærg-hansen, A., Vogt, T.F., Cohen, J.C., Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease (2014) Nat Genet, 46, pp. 352-356. , PMID: 24531328; Dulai, P.S., Singh, S., Patel, J., Soni, M., Prokop, L.J., Younossi, Z., Sebastiani, G., Loomba, R., Increased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: Systematic review and meta-analysis (2017) Hepatology, 65, pp. 1557-1565. , PMID: 28130788; Dongiovanni, P., Stender, S., Pietrelli, A., Mancina, R.M., Cespiati, A., Petta, S., Pelusi, S., Valenti, L., Causal association of hepatic fat with liver damage and insulin resistance in nonalcoholic fatty liver (2018) J Intern Med; van Ommen, G.J., Törnwall, O., Bréchot, C., Dagher, G., Galli, J., Hveem, K., Landegren, U., Zatloukal, K., BBMRI-ERIC as a resource for pharmaceutical and life science industries: the development of biobank-based Expert Centres (2015) Eur J Hum Genet, 23, pp. 893-900. , PMID: 25407005; ESR Position Paper on Imaging Biobanks (2015) Insights Imaging, 6, pp. 403-410. , [PMID: 25999018]; Neri, E., Regge, D., Imaging biobanks in oncology: European perspective (2017) Future Oncol, 13, pp. 433-441. , PMID: 27788586; Wilman, H.R., Kelly, M., Garratt, S., Matthews, P.M., Milanesi, M., Herlihy, A., Gyngell, M., Thomas, E.L., Characterisation of liver fat in the UK Biobank cohort (2017) PLoS One, 12, p. e0172921. , PMID: 28241076; Banerjee, S., Wang, D.S., Kim, H.J., Sirlin, C.B., Chan, M.G., Korn, R.L., Rutman, A.M., Kuo, M.D., A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma (2015) Hepatology, 62, pp. 792-800. , PMID: 25930992; Renzulli, M., Brocchi, S., Cucchetti, A., Mazzotti, F., Mosconi, C., Sportoletti, C., Brandi, G., Golfieri, R., Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma? (2016) Radiology, 279, pp. 432-442. , PMID: 26653683; Sasso, M., Tengher-barna, I., Ziol, M., Miette, V., Fournier, C., Sandrin, L., Poupon, R., Beaugrand, M., Novel controlled attenuation parameter for noninvasive assessment of steatosis using Fibroscan(®): validation in chronic hepatitis C (2012) J Viral Hepat, 19, pp. 244-253. , PMID: 22404722; Szczepaniak, L.S., Babcock, E.E., Schick, F., Dobbins, R.L., Garg, A., Burns, D.K., McGarry, J.D., Stein, D.T., Measurement of intracellular triglyceride stores by H spectroscopy: validation in vivo (1999) Am J Physiol, 276, pp. E977-E989. , [PMID: 10329993]
PY - 2018/4/25
Y1 - 2018/4/25
N2 - The rapidly growing field of functional, molecular and structural bio-imaging is providing an extraordinary new opportunity to overcome the limits of invasive liver biopsy and introduce a "digital biopsy" for in vivo study of liver pathophysiology. To foster the application of bio-imaging in clinical and translational research, there is a need to standardize the methods of both acquisition and the storage of the bio-images of the liver. It can be hoped that the combination of digital, liquid and histologic liver biopsies will provide an innovative synergistic tri-dimensional approach to identifying new aetiologies, diagnostic and prognostic biomarkers and therapeutic targets for the optimization of personalized therapy of liver diseases and liver cancer. A group of experts of different disciplines (Special Interest Group for Personalized Hepatology of the Italian Association for the Study of the Liver, Institute for Biostructures and Bio-imaging of the National Research Council and Bio-banking and Biomolecular Resources Research Infrastructure) discussed criteria, methods and guidelines for facilitating the requisite application of data collection. This manuscript provides a multi-Author review of the issue with special focus on fatty liver. © The Author(s) 2018.
AB - The rapidly growing field of functional, molecular and structural bio-imaging is providing an extraordinary new opportunity to overcome the limits of invasive liver biopsy and introduce a "digital biopsy" for in vivo study of liver pathophysiology. To foster the application of bio-imaging in clinical and translational research, there is a need to standardize the methods of both acquisition and the storage of the bio-images of the liver. It can be hoped that the combination of digital, liquid and histologic liver biopsies will provide an innovative synergistic tri-dimensional approach to identifying new aetiologies, diagnostic and prognostic biomarkers and therapeutic targets for the optimization of personalized therapy of liver diseases and liver cancer. A group of experts of different disciplines (Special Interest Group for Personalized Hepatology of the Italian Association for the Study of the Liver, Institute for Biostructures and Bio-imaging of the National Research Council and Bio-banking and Biomolecular Resources Research Infrastructure) discussed criteria, methods and guidelines for facilitating the requisite application of data collection. This manuscript provides a multi-Author review of the issue with special focus on fatty liver. © The Author(s) 2018.
KW - Bio-imaging
KW - Biobank
KW - Fatty liver
KW - Genomics
KW - Liver biopsy
KW - Liver cancer
KW - Magnetic resonance
KW - Non-alcoholic fatty liver disease
KW - Non-alcoholic steatohepatitis
KW - Radiomics
KW - Ultrasound
U2 - 10.4254/wjh.v10.i2.231
DO - 10.4254/wjh.v10.i2.231
M3 - Article
VL - 10
SP - 231
EP - 245
JO - World Journal of Hepatology
JF - World Journal of Hepatology
SN - 1948-5182
IS - 2
ER -