Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Intravoxel Incoherent Motion Diffusion for Identification of Breast Malignant and Benign Tumors Using Chemometrics
KTH, School of Technology and Health (STH). College of Life Information Science & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
KTH, School of Technology and Health (STH).
2017 (English)In: BioMed Research International, ISSN 2314-6133, E-ISSN 2314-6141, 3845409Article in journal (Refereed) Published
Abstract [en]

Theaim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D*, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0 similar to 1000 s/mm(2)). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support VectorMachine Binary Classification (SVMBC, also known Support VectorMachine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The.. value and ADC provide accurate identification of malignant lesions with.. = 300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r(2) (cv) = 0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions.

Place, publisher, year, edition, pages
HINDAWI LTD , 2017. 3845409
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-209081DOI: 10.1155/2017/3845409ISI: 000402163200001OAI: oai:DiVA.org:kth-209081DiVA: diva2:1111439
Note

QC 20170619

Available from: 2017-06-19 Created: 2017-06-19 Last updated: 2017-06-19Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Muhammed, Hamed Hamid
By organisation
School of Technology and Health (STH)
In the same journal
BioMed Research International
Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 10 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf