|
1
|
Yao JC, Hassan M, Phan A, Dagohoy C, Leary
C, Mares JE, Abdalla EK, Fleming JB, Vauthey JN, Rashid A and Evans
DB: One hundred years after ‘carcinoid’: Epidemiology of and
prognostic factors for neuroendocrine tumors in 35,825 cases in the
United States. J Clin Oncol. 26:3063–3072. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Dong M, Phan AT and Yao JC: New strategies
for advanced neuroendocrine tumors in the era of targeted therapy.
Clin Cancer Res. 18:1830–1836. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Halfdanarson TR, Rabe KG, Rubin J and
Petersen GM: Pancreatic neuroendocrine tumors (PNETs): Incidence,
prognosis and recent trend toward improved survival. Ann Oncol.
19:1727–1733. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Kim JY, Hong SM and Ro JY: Recent updates
on grading and classification of neuroendocrine tumors. Ann Diagn
Pathol. 29:11–16. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Boudreaux JP, Klimstra DS, Hassan MM,
Woltering EA, Jensen RT, Goldsmith SJ, Nutting C, Bushnell DL,
Caplin ME and Yao JC: North American Neuroendocrine Tumor Society
(NANETS): The NANETS consensus guideline for the diagnosis and
management of neuroendocrine tumors: Well-differentiated
neuroendocrine tumors of the jejunum, ileum, appendix, and cecum.
Pancreas. 39:753–766. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Crippa S, Partelli S, Bassi C, Berardi R,
Capelli P, Scarpa A, Zamboni G and Falconi M: Long-term outcomes
and prognostic factors in neuroendocrine carcinomas of the
pancreas: Morphology matters. Surgery. 159:862–871. 2016.
View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Crippa S, Partelli S, Belfiori G, Palucci
M, Muffatti F, Adamenko O, Cardinali L, Doglioni C, Zamboni G and
Falconi M: Management of neuroendocrine carcinomas of the pancreas
(WHO G3): A tailored approach between proliferation and morphology.
World J Gastroenterol. 22:9944–9953. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Sorbye H, Strosberg J, Baudin E, Klimstra
DS and Yao JC: Gastroenteropancreatic high-grade neuroendocrine
carcinoma. Cancer. 120:2814–2823. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Kunz PL, Reidy-Lagunes D, Anthony LB,
Bertino EM, Brendtro K, Chan JA, Chen H, Jensen RT, Kim MK,
Klimstra DS, et al: Consensus guidelines for the management and
treatment of neuroendocrine tumors. Pancreas. 42:557–577. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Kim DW, Kim HJ, Kim KW, Byun JH, Song KB,
Kim JH and Hong SM: Neuroendocrine neoplasms of the pancreas at
dynamic enhanced CT: Comparison between grade 3 neuroendocrine
carcinoma and grade 1/2 neuroendocrine tumour. Eur Radiol.
25:1375–1383. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Kim JH, Eun HW, Kim YJ, Han JK and Choi
BI: Staging accuracy of MR for pancreatic neuroendocrine tumor and
imaging findings according to the tumor grade. Abdom Imaging.
38:1106–1114. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Jang KM, Kim SH, Lee SJ and Choi D: The
value of gadoxetic acid-enhanced and diffusion-weighted MRI for
prediction of grading of pancreatic neuroendocrine tumors. Acta
Radiol. 55:140–148. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Tofts PS, Brix G, Buckley DL, Evelhoch JL,
Henderson E, Knopp MV, Larsson HB, Lee TY, Mayr NA, Parker GJ, et
al: Estimating kinetic parameters from dynamic contrast-enhanced
T(1)-weighted MRI of a diffusable tracer: Standardized quantities
and symbols. J Magn Reson Imaging. 10:223–232. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Morgan B, Thomas AL, Drevs J, Hennig J,
Buchert M, Jivan A, Horsfield MA, Mross K, Ball HA, Lee L, et al:
Dynamic contrast-enhanced magnetic resonance imaging as a biomarker
for the pharmacological response of PTK787/ZK 222584, an inhibitor
of the vascular endothelial growth factor receptor tyrosine
kinases, in patients with advanced colorectal cancer and liver
metastases: Results from two phase I studies. J Clin Oncol.
21:3955–3964. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Cho E, Chung DJ, Yeo DM, Sohn D, Son Y,
Kim T and Hahn ST: Optimal cut-off value of perfusion parameters
for diagnosing prostate cancer and for assessing aggressiveness
associated with Gleason score. Clin Imaging. 39:834–840. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Park JJ, Kim CK, Park SY, Simonetti AW,
Kim E, Park BK and Huh SJ: Assessment of early response to
concurrent chemoradiotherapy in cervical cancer: Value of
diffusion-weighted and dynamic contrast-enhanced MR imaging. Magn
Reson Imaging. 32:993–1000. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Chakiba C, Cornelis F, Descat E,
Gross-Goupil M, Sargos P, Roubaud G and Houédé N: Dynamic contrast
enhanced MRI-derived parameters are potential biomarkers of
therapeutic response in bladder carcinoma. Eur J Radiol.
84:1023–1028. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Furukawa M, Parvathaneni U, Maravilla K,
Richards TL and Anzai Y: Dynamic contrast-enhanced MR perfusion
imaging of head and neck tumors at 3 Tesla. Head Neck. 35:923–929.
2013. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Huang B, Wong CS, Whitcher B, Kwong DL,
Lai V, Chan Q and Khong PL: Dynamic contrast-enhanced magnetic
resonance imaging for characterising nasopharyngeal carcinoma:
Comparison of semiquantitative and quantitative parameters and
correlation with tumour stage. Eur Radiol. 23:1495–1502. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Yuan Y, Kuai YP, Chen XS and Tao XF:
Assessment of dynamic contrast-enhanced magnetic resonance imaging
in the differentiation of malignant from benign orbital masses. Eur
J Radiol. 82:1506–1511. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Koo HR, Cho N, Song IC, Kim H, Chang JM,
Yi A, Yun BL and Moon WK: Correlation of perfusion parameters on
dynamic contrast-enhanced MRI with prognostic factors and subtypes
of breast cancers. J Magn Reson Imaging. 36:145–151. 2012.
View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Joo I, Lee JM, Han JK, Yang HK, Lee HJ and
Choi BI: Dynamic contrast-enhanced MRI of gastric cancer:
Correlation of the perfusion parameters with pathological
prognostic factors. J Magn Reson Imaging. 41:1608–1614. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Filice S and Crisi G: Dynamic
contrast-enhanced perfusion MRI of high grade brain gliomas
obtained with arterial or venous waveform input function. J
Neuroimaging. 26:124–129. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Handayani A, Triadyaksa P, Dijkstra H,
Pelgrim GL, van Ooijen PM, Prakken NH, Schoepf UJ, Oudkerk M,
Vliegenthart R and Sijens PE: Intermodel agreement of myocardial
blood flow estimation from stress-rest myocardial perfusion
magnetic resonance imaging in patients with coronary artery
disease. Invest Radiol. 50:275–282. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Jennings D, Raghunand N and Gillies RJ:
Imaging hemodynamics. Cancer Metastasis Rev. 27:589–613. 2008.
View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Turkbey B, Kobayashi H, Ogawa M, Bernardo
M and Choyke PL: Imaging of tumor angiogenesis: Functional or
targeted? AJR Am J Roentgenol. 193:304–313. 2009. View Article : Google Scholar : PubMed/NCBI
|
