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Items: 1 to 20 of 96

1.

Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers.

Zhou RQ, Ji HC, Liu Q, Zhu CY, Liu R.

World J Clin Cases. 2019 Jul 6;7(13):1611-1622. doi: 10.12998/wjcc.v7.i13.1611.

2.

Simple Vascular Architecture Classification in Predicting Pancreatic Neuroendocrine Tumor Grade and Prognosis.

Chen K, Zhang W, Zhang Z, He Y, Liu Y, Yang X.

Dig Dis Sci. 2018 Nov;63(11):3147-3152. doi: 10.1007/s10620-018-5240-z. Epub 2018 Aug 18.

PMID:
30121810
3.

Clinical utility of 2-[(18)F] fluoro-2-deoxy-D-glucose positron emission tomography in predicting World Health Organization grade in pancreatic neuroendocrine tumors.

Tomimaru Y, Eguchi H, Tatsumi M, Kim T, Hama N, Wada H, Kawamoto K, Kobayashi S, Morii E, Mori M, Doki Y, Nagano H.

Surgery. 2015 Feb;157(2):269-76. doi: 10.1016/j.surg.2014.09.011. Epub 2014 Oct 11.

PMID:
25311263
4.

Staging accuracy of MR for pancreatic neuroendocrine tumor and imaging findings according to the tumor grade.

Kim JH, Eun HW, Kim YJ, Han JK, Choi BI.

Abdom Imaging. 2013 Oct;38(5):1106-14. doi: 10.1007/s00261-013-0011-y.

PMID:
23728305
5.

Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade.

Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z, Chen X.

Abdom Radiol (NY). 2019 Feb;44(2):576-585. doi: 10.1007/s00261-018-1763-1.

PMID:
30182253
6.

NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation.

Dellacasa Bellingegni A, Gruppioni E, Colazzo G, Davalli A, Sacchetti R, Guglielmelli E, Zollo L.

J Neuroeng Rehabil. 2017 Aug 14;14(1):82. doi: 10.1186/s12984-017-0290-6.

7.

Hormone profiling, WHO 2010 grading, and AJCC/UICC staging in pancreatic neuroendocrine tumor behavior.

Morin E, Cheng S, Mete O, Serra S, Araujo PB, Temple S, Cleary S, Gallinger S, Greig PD, McGilvray I, Wei A, Asa SL, Ezzat S.

Cancer Med. 2013 Oct;2(5):701-11. doi: 10.1002/cam4.96. Epub 2013 Aug 6.

8.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance.

Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, Duan SF, Wang ZQ.

Cancer Manag Res. 2019 Mar 4;11:1933-1944. doi: 10.2147/CMAR.S195376. eCollection 2019.

9.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis.

Choi TW, Kim JH, Yu MH, Park SJ, Han JK.

Acta Radiol. 2018 Apr;59(4):383-392. doi: 10.1177/0284185117725367. Epub 2017 Aug 2.

PMID:
28766979
10.

Molecular alterations in sporadic pancreatic neuroendocrine microadenomas.

Hadano A, Hirabayashi K, Yamada M, Kawanishi A, Takanashi Y, Kawaguchi Y, Nakagohri T, Nakamura N, Mine T.

Pancreatology. 2016 May-Jun;16(3):411-5. doi: 10.1016/j.pan.2016.01.011. Epub 2016 Feb 9.

PMID:
26905832
11.

Intravoxel incoherent motion diffusion-weighted imaging of pancreatic neuroendocrine tumors: prediction of the histologic grade using pure diffusion coefficient and tumor size.

Hwang EJ, Lee JM, Yoon JH, Kim JH, Han JK, Choi BI, Lee KB, Jang JY, Kim SW, Nickel MD, Kiefer B.

Invest Radiol. 2014 Jun;49(6):396-402. doi: 10.1097/RLI.0000000000000028.

PMID:
24500090
12.

Fine-needle aspiration biopsy of pancreatic neuroendocrine tumors: Correlation between Ki-67 index in cytological samples and clinical behavior.

Díaz Del Arco C, Esteban López-Jamar JM, Ortega Medina L, Díaz Pérez JÁ, Fernández Aceñero MJ.

Diagn Cytopathol. 2017 Jan;45(1):29-35. doi: 10.1002/dc.23635. Epub 2016 Nov 14.

PMID:
27863178
13.

Correlation of computed tomography imaging features and pathological features of 41 patients with pancreatic neuroendocrine tumors.

Utsumi M, Umeda Y, Takagi K, Takashi K, Nobuoka D, Yoshida R, Shinoura S, Sadamori H, Yagi T, Fujiwara T.

Hepatogastroenterology. 2015 Mar-Apr;62(138):441-6.

PMID:
25916078
14.

Efficacy of endoscopic ultrasonography and endoscopic ultrasonography-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors.

Fujimori N, Osoegawa T, Lee L, Tachibana Y, Aso A, Kubo H, Kawabe K, Igarashi H, Nakamura K, Oda Y, Ito T.

Scand J Gastroenterol. 2016;51(2):245-52. doi: 10.3109/00365521.2015.1083050. Epub 2015 Sep 11.

PMID:
26513346
15.

Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis.

Canellas R, Burk KS, Parakh A, Sahani DV.

AJR Am J Roentgenol. 2018 Feb;210(2):341-346. doi: 10.2214/AJR.17.18417. Epub 2017 Nov 15.

PMID:
29140113
16.

Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma.

Wang YW, Zhang XH, Wang BT, Wang Y, Liu MQ, Wang HY, Ye HY, Chen ZY.

Chin Med Sci J. 2019 Mar 30;34(1):1-9. doi: 10.24920/003531.

PMID:
30961774
17.

Pancreatic neuroendocrine neoplasms at magnetic resonance imaging: comparison between grade 3 and grade 1/2 tumors.

Guo C, Chen X, Xiao W, Wang Q, Sun K, Wang Z.

Onco Targets Ther. 2017 Mar 7;10:1465-1474. doi: 10.2147/OTT.S127803. eCollection 2017.

18.

[Analysis of diagnosis, therapy and prognosis factors of 103 patients with pancreatic neuroendocrine tumors].

Luo Q, Liu YN, Ma HY, Li S, Huang JY, Li G, Jin G.

Zhonghua Wai Ke Za Zhi. 2017 Oct 1;55(10):755-759. doi: 10.3760/cma.j.issn.0529-5815.2017.10.008. Chinese.

PMID:
29050176
19.

Seminal quality prediction using data mining methods.

Sahoo AJ, Kumar Y.

Technol Health Care. 2014;22(4):531-45. doi: 10.3233/THC-140816.

PMID:
24898862
20.

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Paliwal N, Jaiswal P, Tutino VM, Shallwani H, Davies JM, Siddiqui AH, Rai R, Meng H.

Neurosurg Focus. 2018 Nov 1;45(5):E7. doi: 10.3171/2018.8.FOCUS18332.

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