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Items: 1 to 20 of 93

1.

Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI.

Lang N, Zhang Y, Zhang E, Zhang J, Chow D, Chang P, Yu HJ, Yuan H, Su MY.

Magn Reson Imaging. 2019 Feb 28. pii: S0730-725X(18)30672-6. doi: 10.1016/j.mri.2019.02.013. [Epub ahead of print]

PMID:
30826448
2.

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

Shi L, Zhang Y, Nie K, Sun X, Niu T, Yue N, Kwong T, Chang P, Chow D, Chen JH, Su MY.

Magn Reson Imaging. 2019 May 3;61:33-40. doi: 10.1016/j.mri.2019.05.003. [Epub ahead of print]

PMID:
31059768
3.

Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.

Spuhler KD, Ding J, Liu C, Sun J, Serrano-Sosa M, Moriarty M, Huang C.

Magn Reson Med. 2019 Aug;82(2):786-795. doi: 10.1002/mrm.27758. Epub 2019 Apr 8.

PMID:
30957936
4.

Diagnosis of Spinal Lesions Using Heuristic and Pharmacokinetic Parameters Measured by Dynamic Contrast-Enhanced MRI.

Lang N, Yuan H, Yu HJ, Su MY.

Acad Radiol. 2017 Jul;24(7):867-875. doi: 10.1016/j.acra.2016.12.014. Epub 2017 Feb 2.

5.

Predicting malignant nodules by fusing deep features with classical radiomics features.

Paul R, Hawkins SH, Schabath MB, Gillies RJ, Hall LO, Goldgof DB.

J Med Imaging (Bellingham). 2018 Jan;5(1):011021. doi: 10.1117/1.JMI.5.1.011021. Epub 2018 Mar 21.

6.

Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks.

Nyflot MJ, Thammasorn P, Wootton LS, Ford EC, Chaovalitwongse WA.

Med Phys. 2019 Feb;46(2):456-464. doi: 10.1002/mp.13338. Epub 2018 Dec 28.

PMID:
30548601
7.

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.

Xie T, Wang Z, Zhao Q, Bai Q, Zhou X, Gu Y, Peng W, Wang H.

Front Oncol. 2019 Jun 14;9:505. doi: 10.3389/fonc.2019.00505. eCollection 2019.

8.

Influence of temporal parameters of DCE-MRI on the quantification of heterogeneity in tumor vascularization.

Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M.

J Magn Reson Imaging. 2019 Apr 13. doi: 10.1002/jmri.26753. [Epub ahead of print]

PMID:
30980697
9.

Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI.

Lang N, Su MY, Yu HJ, Lin M, Hamamura MJ, Yuan H.

Magn Reson Imaging. 2013 Oct;31(8):1285-91. doi: 10.1016/j.mri.2012.10.006. Epub 2013 Jan 3.

10.

Morphological and dynamic contrast enhanced MR imaging features for the differentiation of chordoma and giant cell tumors in the Axial Skeleton.

Lang N, Su MY, Xing X, Yu HJ, Yuan H.

J Magn Reson Imaging. 2017 Apr;45(4):1068-1075. doi: 10.1002/jmri.25414. Epub 2016 Aug 4.

11.

Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

Yamada A, Oyama K, Fujita S, Yoshizawa E, Ichinohe F, Komatsu D, Fujinaga Y.

Int J Comput Assist Radiol Surg. 2019 Aug;14(8):1295-1301. doi: 10.1007/s11548-019-01987-1. Epub 2019 May 3.

PMID:
31054130
12.

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.

Liu Z, Feng B, Li C, Chen Y, Chen Q, Li X, Guan J, Chen X, Cui E, Li R, Li Z, Long W.

J Magn Reson Imaging. 2019 Feb 17. doi: 10.1002/jmri.26688. [Epub ahead of print]

PMID:
30773770
13.

Differentiation of tuberculosis and metastatic cancer in the spine using dynamic contrast-enhanced MRI.

Lang N, Su MY, Yu HJ, Yuan H.

Eur Spine J. 2015 Aug;24(8):1729-37. doi: 10.1007/s00586-015-3851-z. Epub 2015 Mar 8.

14.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.

Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A.

Breast Cancer Res. 2017 May 18;19(1):57. doi: 10.1186/s13058-017-0846-1. Erratum in: Breast Cancer Res. 2017 Jul 10;19(1):80.

15.

Effect of Temporal Resolution on Diagnostic Performance of Dynamic Contrast-Enhanced Magnetic Resonance Imaging of the Prostate.

Othman AE, Falkner F, Weiss J, Kruck S, Grimm R, Martirosian P, Nikolaou K, Notohamiprodjo M.

Invest Radiol. 2016 May;51(5):290-6. doi: 10.1097/RLI.0000000000000234.

PMID:
26619282
16.

Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response?

Alic L, van Vliet M, van Dijke CF, Eggermont AM, Veenland JF, Niessen WJ.

Phys Med Biol. 2011 Mar 21;56(6):1601-16. doi: 10.1088/0031-9155/56/6/006. Epub 2011 Feb 18.

PMID:
21335648
17.

A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI.

Yin P, Mao N, Zhao C, Wu J, Chen L, Hong N.

J Magn Reson Imaging. 2019 Mar;49(3):752-759. doi: 10.1002/jmri.26238. Epub 2018 Nov 14.

PMID:
30430686
18.

Assessment of liver cirrhosis for patients with Child's A classification before hepatectomy using dynamic contrast-enhanced MRI.

Liao YS, Lee LW, Yang PH, Kuo LM, Kuan LY, Tseng WI, Hwang DW.

Clin Radiol. 2019 May;74(5):407.e11-407.e17. doi: 10.1016/j.crad.2019.01.017. Epub 2019 Feb 21.

PMID:
30799098
19.

Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.

Wang J, Kato F, Oyama-Manabe N, Li R, Cui Y, Tha KK, Yamashita H, Kudo K, Shirato H.

PLoS One. 2015 Nov 24;10(11):e0143308. doi: 10.1371/journal.pone.0143308. eCollection 2015.

20.

Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.

Suh HB, Choi YS, Bae S, Ahn SS, Chang JH, Kang SG, Kim EH, Kim SH, Lee SK.

Eur Radiol. 2018 Sep;28(9):3832-3839. doi: 10.1007/s00330-018-5368-4. Epub 2018 Apr 6.

PMID:
29626238

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