Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

18 F-FDG PET/MRI of Primary Hepatic Malignancies: Differential Diagnosis and Histologic Grading

Author(s): Bedriye Koyuncu Sökmen* and Nagihan Inan

Volume 20, 2024

Published on: 07 July, 2023

Article ID: e080523216636 Pages: 8

DOI: 10.2174/1573405620666230508105758

open_access

Abstract

Background: Distinguishing between IHCC and HCC is important because of their differences in treatment and prognosis. The hybrid Positron Emission Tomography/magnetic Resonance Imaging (PET/MRI) system has become more widely accessible, with oncological imaging becoming one of its most promising applications.

Objective: The objective of this study was to see how well 18F-fluorodeoxyglucose (18F-FDG) PET/MRI could be used for differential diagnosis and histologic grading of primary hepatic malignancies.

Methods: We retrospectively evaluated 64 patients (53 patients with HCC, 11 patients with IHCC) with histologically proven primary hepatic malignancies using 18F-FDG/MRI. The Apparent Diffusion Coefficient (ADC), Coefficient of Variance (CV) of the ADC, and standardized uptake value (SUV) were calculated.

Results: The mean SUVmax value was higher for IHCC (7.7 ± 3.4) than for HCC (5.2 ± 3.1) (p = 0.019). The area under the curve (AUC) was 0.737, an optimal 6.98 cut-off value providing 72% sensitivity and 79% specificity. The ADCcv value in IHCC was statistically significantly higher than in HCC (p=0.014). ADC mean values in HCCs were significantly higher in low-grade tumors than in high-grade tumors. The AUC value was 0.73, and the optimal cut-off point was 1.20x10-6 mm2/s, giving 62% sensitivity and 72% specificity. The SUVmax value was also found to be statistically significantly higher in the high-grade group. The ADCcv value in the HCC low-grade group was found to be lower than in the highgrade group (p=0.036).

Conclusion: 18F FDG PET/MRI is a novel imaging technique that can aid in the differentiation of primary hepatic neoplasms as well as tumor-grade estimation.

Keywords: Hepatocellular carcinoma, Cholangiocarcinoma, Histopathology, Positron emission tomography, Magnetic resonance imaging, CV.

[1]
Kim SA, Lee JM, Lee KB, et al. Intrahepatic mass-forming cholangiocarcinomas: Enhancement patterns at multiphasic CT, with special emphasis on arterial enhancement pattern correlation with clinicopathologic findings. Radiology 2011; 260(1): 148-57.
[http://dx.doi.org/10.1148/radiol.11101777] [PMID: 21474703]
[2]
Granata V, Fusco R, Catalano O, et al. Intravoxel incoherent motion (IVIM) in diffusion-weighted imaging (DWI) for Hepatocellular carcinoma: Correlation with histologic grade. Oncotarget 2016; 7(48): 79357-64.
[http://dx.doi.org/10.18632/oncotarget.12689] [PMID: 27764817]
[3]
Judenhofer MS, Wehrl HF, Newport DF, et al. Simultaneous PET-MRI: A new approach for functional and morphological imaging. Nat Med 2008; 14(4): 459-65.
[http://dx.doi.org/10.1038/nm1700] [PMID: 18376410]
[4]
Kwon HW, Becker AK, Goo JM, Cheon GJ. FDG Whole-Body PET/MRI in oncology: A systematic review. Nucl Med Mol Imaging 2017; 51(1): 22-31.
[http://dx.doi.org/10.1007/s13139-016-0411-3] [PMID: 28250855]
[5]
Fraioli F, Screaton NJ, Janes SM, et al. Non-small-cell lung cancer resectability: Diagnostic value of PET/MR. Eur J Nucl Med Mol Imaging 2015; 42(1): 49-55.
[http://dx.doi.org/10.1007/s00259-014-2873-9] [PMID: 25120040]
[6]
Nagtegaal ID, Odze RD, Klimstra D, et al. The 2019 WHO classification of tumours of the digestive system. Histopathology 2020; 76(2): 182-8.
[http://dx.doi.org/10.1111/his.13975] [PMID: 31433515]
[7]
Peng J, Zheng J, Yang C, et al. Intravoxel incoherent motion diffusion-weighted imaging to differentiate hepatocellular carcinoma from intrahepatic cholangiocarcinoma. Sci Rep 2020; 10(1): 7717.
[http://dx.doi.org/10.1038/s41598-020-64804-9] [PMID: 32382050]
[8]
Conrad R, Castelino-Prabhu S, Cobb C, Raza A. Cytopathologic diagnosis of liver mass lesions. J Gastrointest Oncol 2013; 4(1): 53-61.
[PMID: 23450205]
[9]
Onur MR, Çiçekçi M, Kayalı A, Poyraz AK, Kocakoç E. The role of ADC measurement in differential diagnosis of focal hepatic lesions. Eur J Radiol 2012; 81(3): e171-6.
[http://dx.doi.org/10.1016/j.ejrad.2011.01.116] [PMID: 21353418]
[10]
Min JH, Kim YK, Choi SY, et al. Differentiation between cholangiocarcinoma and hepatocellular carcinoma with target sign on diffusion-weighted imaging and hepatobiliary phase gadoxetic acid-enhanced MR imaging: Classification tree analysis applying capsule and septum. Eur J Radiol 2017; 92: 1-10.
[http://dx.doi.org/10.1016/j.ejrad.2017.04.008] [PMID: 28624005]
[11]
Higashi T, Saga T, Nakamoto Y, et al. Relationship between retention index in dual-phase (18)F-FDG PET, and hexokinase-II and glucose transporter-1 expression in pancreatic cancer. J Nucl Med 2002; 43(2): 173-80.
[PMID: 11850481]
[12]
Roh MS, Jeong JS, Kim YH, Kim MC, Hong SH. Diagnostic utility of GLUT1 in the differential diagnosis of liver carcinomas. Hepatogastroenterology 2004; 51(59): 1315-8.
[PMID: 15362741]
[13]
Lee JD, Yang WI, Park YN, et al. Different glucose uptake and glycolytic mechanisms between hepatocellular carcinoma and intrahepatic mass-forming cholangiocarcinoma with increased (18)F-FDG uptake. J Nucl Med 2005; 46(10): 1753-9.
[PMID: 16204727]
[14]
Lim CH, Moon SH, Cho YS, Choi JY, Lee KH, Hyun SH. Prognostic value of 18F-fluorodeoxyglucose positron emission tomography/ computed tomography in patients with combined hepatocellular-cholangiocarcinoma. Eur J Nucl Med Mol Imaging 2019; 46(8): 1705-12.
[http://dx.doi.org/10.1007/s00259-019-04327-2] [PMID: 31049603]
[15]
Kong E, Chun KA, Cho IH. Quantitative assessment of simultaneous F-18 FDG PET/MRI in patients with various types of hepatic tumors: Correlation between glucose metabolism and apparent diffusion coefficient. PLoS One 2017; 12(7): e0180184.
[http://dx.doi.org/10.1371/journal.pone.0180184] [PMID: 28672016]
[16]
Boroughs LK, DeBerardinis RJ. Metabolic pathways promoting cancer cell survival and growth. Nat Cell Biol 2015; 17(4): 351-9.
[http://dx.doi.org/10.1038/ncb3124] [PMID: 25774832]
[17]
Robertson-Tessi M, Gillies RJ, Gatenby RA, Anderson ARA. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res 2015; 75(8): 1567-79.
[http://dx.doi.org/10.1158/0008-5472.CAN-14-1428] [PMID: 25878146]
[18]
Ganeshan B, Miles KA, Young RCD, Chatwin CR. Hepatic entropy and uniformity: Additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol 2007; 62(8): 761-8.
[http://dx.doi.org/10.1016/j.crad.2007.03.004] [PMID: 17604764]
[19]
Ganeshan B, Abaleke S, Young RCD, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: Initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010; 10(1): 137-43.
[http://dx.doi.org/10.1102/1470-7330.2010.0021] [PMID: 20605762]
[20]
Miles KA, Ganeshan B, Griffiths MR, Young RCD, Chatwin CR. Colorectal cancer: Texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 2009; 250(2): 444-52.
[http://dx.doi.org/10.1148/radiol.2502071879] [PMID: 19164695]
[21]
Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: A systematic review. PLoS One 2014; 9(10): e110300.
[http://dx.doi.org/10.1371/journal.pone.0110300] [PMID: 25330171]
[22]
Stein D, Goldberg N, Domachevsky L, et al. Quantitative biomarkers for liver metastases: Comparison of MRI diffusion-weighted imaging heterogeneity index and fluorine-18-fluoro-deoxyglucose standardised uptake value in hybrid PET/MR. Clin Radiol 2018; 73(9): 832.e17-22.
[http://dx.doi.org/10.1016/j.crad.2018.04.012] [PMID: 29859634]
[23]
Dong X, Wu P, Sun X, et al. Intra-tumour 18 F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J Med Imaging Radiat Oncol 2015; 59(3): 338-45.
[http://dx.doi.org/10.1111/1754-9485.12289] [PMID: 25708154]
[24]
Yoon HJ, Kim Y, Kim BS. Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol 2015; 25(12): 3648-58.
[http://dx.doi.org/10.1007/s00330-015-3761-9] [PMID: 26063655]
[25]
Bundschuh RA, Dinges J, Neumann L, et al. Textural parameters of tumor heterogeneity in (18)F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med 2014; 55(6): 891-7.
[http://dx.doi.org/10.2967/jnumed.113.127340] [PMID: 24752672]
[26]
Rosenkrantz AB, Sigmund EE, Winnick A, et al. Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: Preliminary experience in fresh liver explants. Magn Reson Imaging 2012; 30(10): 1534-40.
[http://dx.doi.org/10.1016/j.mri.2012.04.020] [PMID: 22819175]
[27]
Nakanishi M, Chuma M, Hige S, et al. Relationship between diffusion-weighted magnetic resonance imaging and histological tumor grading of hepatocellular carcinoma. Ann Surg Oncol 2012; 19(4): 1302-9.
[http://dx.doi.org/10.1245/s10434-011-2066-8] [PMID: 21927976]
[28]
Iwata Y, Shiomi S, Sasaki N, et al. Clinical usefulness of positron emission tomography with fluorine-18-fluorodeoxyglucose in the diagnosis of liver tumors. Ann Nucl Med 2000; 14(2): 121-6.
[http://dx.doi.org/10.1007/BF02988591] [PMID: 10830530]
[29]
Yang SH, Suh KS, Lee HW, et al. The role of18F-FDG-PET imaging for the selection of liver transplantation candidates among hepatocellular carcinoma patients. Liver Transpl 2006; 12(11): 1655-60.
[http://dx.doi.org/10.1002/lt.20861] [PMID: 16964589]
[30]
Yaprak O, Acar S, Ertugrul G, Dayangac M. Role of pre-transplant 18F-FDG PET/CT in predicting hepatocellular carcinoma recurrence after liver transplantation. World J Gastrointest Oncol 2018; 10(10): 336-43.
[http://dx.doi.org/10.4251/wjgo.v10.i10.336] [PMID: 30364796]
[31]
Torizuka T, Tamaki N, Inokuma T, et al. In vivo assessment of glucose metabolism in hepatocellular carcinoma with FDG-PET. J Nucl Med 1995; 36(10): 1811-7.
[PMID: 7562048]
[32]
Zhang Y, Huang Z, Chen J, et al. Imaging biomarkers for predicting poor prognosis of hepatocellular carcinoma: A review. Hepatoma Res 2020; 6: 30.

© 2024 Bentham Science Publishers | Privacy Policy