Classification of liver lesions based on temporal changes in hepatobiliary phase contrast on magnetic resonance imaging: a preliminary study

Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101–10. https://doi.org/10.1148/radiology.211.1.r99ap38101.

Article  CAS  PubMed  Google Scholar 

Ammar ASM, Dawoud MM, Hefeda MM, Badawy ME, Abozaid EH. The utility of diffusion weighted imaging and dynamic contrast enhanced MRI techniques in differentiation between benign and malignant uterine masses. Int J Med Imag. 2019;7:66–80. https://doi.org/10.11648/j.ijmi.20190703.12.

Article  Google Scholar 

Hawighorst H, Knapstein PG, Knopp MV, et al. Uterine cervical carcinoma: comparison of standard and pharmacokinetic analysis of time–intensity curves for assessment of tumor angiogenesis and patient survival. Cancer Res. 1998;58:3598–602.

CAS  PubMed  Google Scholar 

Zhang TT, Wang L, Liu HH, et al. Differentiation of pancreatic carcinoma and mass-forming focal pancreatitis: qualitative and quantitative assessment by dynamic contrast-enhanced MRI combined with diffusion-weighted imaging. Oncotarget. 2017;8:1744–59.

Article  PubMed  Google Scholar 

Verma S, Turkbey B, Muradyan N, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol. 2012;198:1277–88. https://doi.org/10.2214/AJR.12.8510.

Article  PubMed  PubMed Central  Google Scholar 

Engelbrecht MR, Huisman HJ, Laheij RJF, et al. Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology. 2003;229:248–54. https://doi.org/10.1148/radiol.2291020200.

Article  PubMed  Google Scholar 

King KG, Gulati M, Malhi H, et al. Quantitative assessment of solid renal masses by contrast-enhanced ultrasound with time–intensity curves: how we do it. Abdom Imaging. 2015;40:2461–71. https://doi.org/10.1007/s00261-015-0468-y.

Article  PubMed  Google Scholar 

Maurya V, Sharma P, Bhatia M. Time intensity curve in primary solid hepatic lesions: does it provide objectivity to otherwise subjective interpretation? Med J Dr DY Patil Vidyapeeth. 2023;16:373–80.

Article  Google Scholar 

Yamashita Y, Fan ZM, Yamamoto H, et al. Spin-echo and dynamic gadolinium-enhanced FLASH MR imaging of hepatocellular carcinoma: correlation with histopathologic findings. J Magn Reson Imaging. 1994;4:83–90. https://doi.org/10.1002/jmri.1880040117.

Article  CAS  PubMed  Google Scholar 

Lavini C, Buiter MS, Maas M. Use of dynamic contrast enhanced time intensity curve shape analysis in MRI: theory and practice. Rep Med Imag. 2013;6:71–82. https://doi.org/10.2147/RMI.S35088.

Article  Google Scholar 

van Beers BE, Grandin C, Pauwels S, et al. Gd-EOB-DTPA enhancement pattern of hepatocellular carcinomas in rats: comparison with Tc-99m-IDA uptake. J Magn Reson Imaging. 1994;4:351e4. https://doi.org/10.1002/jmri.1880040321.

Article  Google Scholar 

Nassif A, Jia J, Keiser M, et al. Visualization of hepatic uptake transporter function in healthy subjects by using gadoxetic acid-enhanced MR imaging. Radiology. 2012;264:741–50. https://doi.org/10.1148/radiol.12112061.

Article  PubMed  Google Scholar 

Testa ML, Chojniak R, Sene LS, et al. Is DWI/ADC a useful tool in the characterization of focal hepatic lesions suspected of malignancy? PLoS ONE. 2014;9: e101944. https://doi.org/10.1371/journal.pone.0101944.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cieszanowski A, Anysz-Grodzicka A, Szeszkowski W, et al. Characterization of focal liver lesions using quantitative techniques: comparison of apparent diffusion coefficient values and T2 relaxation times. Eur Radiol. 2012;22:2514–24. https://doi.org/10.1007/s00330-012-2519-x.

Article  PubMed  PubMed Central  Google Scholar 

Ohtomo K, Itai Y, Furui S, et al. Hepatic tumors: differentiation by transverse relaxation time (T2) of magnetic resonance imaging. Radiology. 1985;155:421–3. https://doi.org/10.1148/radiology.155.2.2984719.

Article  CAS  PubMed  Google Scholar 

Deepho C, Watanabe H, Sakamoto J, Kurabayashi T. Mandibular canal visibility using a plain volumetric interpolated breath-hold examination sequence in MRI. Dento Maxillo Fac Radiol. 2018;47:20170245. https://doi.org/10.1259/dmfr.20170245.

Article  Google Scholar 

Rhee H, Kim MJ, Park MS, Kim KA. Differentiation of early hepatocellular carcinoma from benign hepatocellular nodules on gadoxetic acid-enhanced MRI. Br J Radiol. 2012;85:e837–44. https://doi.org/10.1259/bjr/13212920.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xie S, Sun Y, Wang L, et al. Assessment of liver function and liver fibrosis with dynamic Gd-EOB-DTPA-enhanced MRI. Acad Radiol. 2015;22:460–6. https://doi.org/10.1016/j.acra.2014.11.006.

Article  PubMed  Google Scholar 

Kim HY, Choi JY, Kim CW, et al. Gadolinium Ethoxybenzyl Diethylenetriamine Pentaacetic Acid-Enhanced Magnetic Resonance Imaging Predicts the Histological Grade of Hepatocellular Carcinoma Only in Patients with Child-Pugh Class A Cirrhosis. Liver Transpl. 2012;18:850–7. https://doi.org/10.1002/lt.23426.

Article  PubMed  Google Scholar 

Wang YXJ. Superparamagnetic iron oxide based MRI contrast agents: current status of clinical application. Quant Imaging Med Surg. 2011;1:35–40. https://doi.org/10.3978/j.issn.2223-4292.2011.08.03.

Article  PubMed  PubMed Central  Google Scholar 

Zech CJ, Herrmann KA, Reiser MF, Schoenberg SO. MR imaging in patient with suspected liver metastases: value of liver-specific contrast agent Gd-EOB-DTPA. Magn Reason Med Sci. 2007;6:43–52. https://doi.org/10.2463/mrms.6.43.

Article  Google Scholar 

Jonas S, Bechstein WO, Steinmüller T, et al. Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis. Hepatology. 2001;33:1080–6. https://doi.org/10.1053/jhep.2001.23561.

Article  CAS  PubMed  Google Scholar 

Hamm B, Thoeni RF, Gould RG, et al. Focal liver lesions: characterization with nonenhanced and dynamic contrast material-enhanced MR imaging. Radiology. 1994;190:417–23. https://doi.org/10.1148/radiology.190.2.8284392.

Article  CAS  PubMed  Google Scholar 

Motosugi U, Ichikawa T, Sou H, et al. Liver parenchymal enhancement of hepatocyte-phase images in Gd-EOB-DTPA-enhanced MR Imaging: which biological markers of the liver function affect the enhancement? J Magn Reson Imaging. 2009;30:1042–6. https://doi.org/10.1002/jmri.21956.

Article  PubMed  Google Scholar 

Takatsu Y, Kobayashi S, Miyati T, Shiozaki T. A novel method for evaluating enhancement using gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid in the hepatobiliary phase of magnetic resonance imaging. Clin Imaging. 2016;40:1112e7. https://doi.org/10.1016/j.clinimag.2016.07.001.

Article  Google Scholar 

Haimerl M, Wächtler M, Zeman F, et al. Quantitative evaluation of enhancement patterns in focal solid liver lesions with Gd-EOB-DTPA-enhanced MRI. PLoS ONE. 2014;9: e100315. https://doi.org/10.1371/journal.pone.0100315.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Son J, Hwang SM, Park S, et al. Imaging features of hepatocellular carcinoma: quantitative and qualitative comparison between MRI-enhanced with Gd-EOB-DTPA and Gd-DTPA. Invest Radiol. 2019;54:494–9. https://doi.org/10.1097/RLI.0000000000000562.

Article  CAS  PubMed  Google Scholar 

Auer TA, Walter-Rittel T, Geisel D, et al. HBP-enhancing hepatocellular adenomas and how to discriminate them from FNH in Gd-EOB MRI. BMC Med Imaging. 2021;21:28.

Article  PubMed  PubMed Central  Google Scholar 

Johnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach—the ALBI grade. J Clin Oncol. 2015;33:550–8. https://doi.org/10.1200/JCO.2014.57.9151.

Article  PubMed  Google Scholar 

Takatsu Y, Kobayashi S, Miyati T, Shiozaki T. Hepatobiliary phase images using gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid-enhanced MRI as an imaging surrogate for the albumin–bilirubin grading system. Eur J Radiol. 2016;85:2206–10. https://doi.org/10.1016/j.ejrad.2016.10.010.

Article  PubMed  Google Scholar 

Basu S, Kumbier K, Brown JB, Yu B. Iterative random forests to detect predictive and stable high-order interactions. Proc Natl Acad Sci U S A. 2018;115:1943–8. https://doi.org/10.1073/pnas.1711236115.

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