Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies

A. Srinivasan; C.J. Galbán; T.D. Johnson; T.L. Chenevert; B.D. Ross; S.K. Mukherji

doi:10.3174/ajnr.A1901

Research ArticleHead and NeckE

Open Access

Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies

A. Srinivasan, C.J. Galbán, T.D. Johnson, T.L. Chenevert, B.D. Ross and S.K. Mukherji

American Journal of Neuroradiology April 2010, 31 (4) 736-740; DOI: https://doi.org/10.3174/ajnr.A1901

A. Srinivasan

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site

C.J. Galbán

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site

T.D. Johnson

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site

T.L. Chenevert

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site

B.D. Ross

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site

S.K. Mukherji

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site

Article
Figures & Data
Info & Metrics
Responses
References
PDF

Submit a Response to This Article

Compose eLetter

Title *

Contents

More information about text formats

Plain text

No HTML tags allowed.
Web page addresses and e-mail addresses turn into links automatically.
Lines and paragraphs break automatically.

Author Information

Contributors
First Name and Middle Initial * First or given name, e.g. 'Peter'. Last Name * Your last, or family, name, e.g. 'MacMoody'. Email Address * Your email address, e.g. [email protected] Occupation * Your role and/or occupation, e.g. 'Orthopedic Surgeon'. Affiliation * Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.

Statement of Competing Interests

Competing interests? *

Yes

Please describe the competing interests

Vertical Tabs

Jump to comment:

No eLetters have been published for this article.

PreviousNext

In this issue

American Journal of Neuroradiology

Vol. 31, Issue 4

1 Apr 2010

Table of Contents
Index by author

Download PDF

Email Article

Thank you for your interest in spreading the word on American Journal of Neuroradiology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Your Email *

Your Name *

Send To *

Enter multiple addresses on separate lines or separate them with commas.

You are going to email the following Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies

Message Subject (Your Name) has sent you a message from American Journal of Neuroradiology

Message Body (Your Name) thought you would like to see the American Journal of Neuroradiology web site.

Your Personal Message

CAPTCHA

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Cite this article

A. Srinivasan, C.J. Galbán, T.D. Johnson, T.L. Chenevert, B.D. Ross, S.K. Mukherji

Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies

American Journal of Neuroradiology Apr 2010, 31 (4) 736-740; DOI: 10.3174/ajnr.A1901

Citation Manager Formats

BibTeX
Bookends
EasyBib
EndNote (tagged)
EndNote 8 (xml)
Medlars
Mendeley
Papers
RefWorks Tagged
Ref Manager
RIS
Zotero

0 Responses

Respond to this article

Bookmark this article

User Name *

Password *

Remember my user name & password.

Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies

A. Srinivasan, C.J. Galbán, T.D. Johnson, T.L. Chenevert, B.D. Ross, S.K. Mukherji

American Journal of Neuroradiology Apr 2010, 31 (4) 736-740; DOI: 10.3174/ajnr.A1901

Share This Article:

Tweet Widget
Facebook Like
Google Plus One

Purchase

Jump to section

Article
Figures & Data
Info & Metrics
Responses
References
PDF

No related articles found.

PubMed
Google Scholar

Cited By...

No citing articles found.

Crossref (19)
Google Scholar

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

High‐Throughput Metabolomics by 1D NMR

Alessia Vignoli, Veronica Ghini, Gaia Meoni, Cristina Licari, Panteleimon G. Takis, Leonardo Tenori, Paola Turano, Claudio Luchinat

Angewandte Chemie International Edition 2019 58 4
Differentiation between benign and malignant orbital tumors at 3-T diffusion MR-imaging

Ahmed Abdel Khalek Abdel Razek, Sahar Elkhamary, Amani Mousa

Neuroradiology 2011 53 7
Histogram Analysis of Apparent Diffusion Coefficient Map of Standard and High B-value Diffusion MR Imaging in Head and Neck Squamous Cell Carcinoma

Se Jin Ahn, Seung Hong Choi, Young-Jae Kim, Kwang Gi Kim, Chul-Ho Sohn, Moon Hee Han, Kee-Hyun Chang, Hye Sook Min

Academic Radiology 2012 19 10
Prediction of chemotherapeutic response in bladder cancer using K-means clustering of dynamic contrast-enhanced (DCE)-MRI pharmacokinetic parameters

Huyen T. Nguyen, Guang Jia, Zarine K. Shah, Kamal Pohar, Amir Mortazavi, Debra L. Zynger, Lai Wei, Xiangyu Yang, Daniel Clark, Michael V. Knopp

Journal of Magnetic Resonance Imaging 2015 41 5
Assessment of diffusion parameters by intravoxel incoherent motion MRI in head and neck squamous cell carcinoma

Simona Marzi, Francesca Piludu, Antonello Vidiri

NMR in Biomedicine 2013 26 12
Quantitative Assessment of Heterogeneity in Bladder Tumor MRI Diffusivity: Can Response be Predicted Prior to Neoadjuvant Chemotherapy?

Huyen T. Nguyen, Amir Mortazavi, Kamal S. Pohar, Debra L. Zynger, Lai Wei, Zarine K. Shah, Guang Jia, Michael V. Knopp

Bladder Cancer 2017 3 4
Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment

Timothy J. Larkin, Holly C. Canuto, Mikko I. Kettunen, Thomas C. Booth, De‐En Hu, Anant S. Krishnan, Sarah E. Bohndiek, André A. Neves, Charles McLachlan, Michael P. Hobson, Kevin M. Brindle

Magnetic Resonance in Medicine 2014 71 1
Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer

Marco Rengo, Simona Picchia, Simona Marzi, Davide Bellini, Damiano Caruso, Mauro Caterino, Maria Ciolina, Domenico De Santis, Daniela Musio, Vincenzo Tombolini, Andrea Laghi

Oncotarget 2017 8 70
Concordance of FDG PET/CT metabolic tumour volume versus DW-MRI functional tumour volume with T2-weighted anatomical tumour volume in cervical cancer

Alta Y. T. Lai, Jose A. U. Perucho, Xiaopei Xu, Edward S. Hui, Elaine Y. P. Lee

BMC Cancer 2017 17 1
Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study

Huyen T. Nguyen, Zarine K. Shah, Amir Mortazavi, Kamal S. Pohar, Lai Wei, Guang Jia, Debra L. Zynger, Michael V. Knopp

European Radiology 2017 27 5

Main menu

User menu

Search

American Journal of Neuroradiology

Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies

Plain text

Vertical Tabs

Jump to comment:

In this issue

Citation Manager Formats

Related Articles

Cited By...

More in this TOC Section

Similar Articles

Indexed Content

Cases

More from AJNR

Multimedia

Resources

About Us

American Society of Neuroradiology