Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Proteomic analysis of malignant ascites and its impact on ovarian cancer spheroids.

Clinical proteomics·2026
Same author

Simplification of a Three-Constant Intraocular Lens Calculation Formula to a Single-Constant Approach: The Haigis Formula.

Diagnostics (Basel, Switzerland)·2026
Same author

Intraocular Lens Calculation Concept Based on Aphakic Refraction-Considerations on a Cornea Model With Two Refracting Surfaces.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)·2026
Same author

Response to the Letter to the Editor: Dual-Zone Keratometry for Identifying Central Radius and Corneal Asphericity.

Current eye research·2026
Same author

Variation in Manifest Subjective Refraction in a Population Screened for Refractive Surgery.

Diagnostics (Basel, Switzerland)·2026
Same author

Tissue-resolved proteomic characterization of oat grains guided by metabolite MALDI mass spectrometry imaging.

Analytical and bioanalytical chemistry·2026

Related Experiment Video

Updated: Mar 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Classification of MALDI-MS imaging data of tissue microarrays using canonical correlation analysis-based variable

Lyron Winderbaum1, Inge Koch1, Parul Mittal1

  • 1The University of Adelaide, Adelaide, SA, Australia.

Proteomics
|March 31, 2016
PubMed
Summary
This summary is machine-generated.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging of tissue microarrays (TMAs) aids in predicting lymph node metastasis (LNM) in endometrial cancer. A novel variable selection method improves diagnostic accuracy over standard techniques.

Keywords:
BioinformaticsCanonical correlation analysisClassificationEndometrial cancerMALDI-MS imagingVariable ranking

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.7K

Related Experiment Videos

Last Updated: Mar 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.7K

Area of Science:

  • Proteomics
  • Oncology
  • Biostatistics

Background:

  • Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging of tissue microarrays (TMAs) offers cost- and time-efficient proteomics data for large patient cohorts.
  • High-dimensional, low-sample-size TMA data presents statistical challenges for classical methods, particularly when the number of variables exceeds the number of samples.
  • Predicting lymph node metastasis (LNM) status is crucial for cancer diagnosis and treatment planning.

Purpose of the Study:

  • To develop and evaluate a novel statistical approach for predicting LNM status in endometrial primary carcinomas using MALDI-MS imaging TMA data.
  • To address the statistical challenges posed by high-dimensional, low-sample-size data in clinical proteomics.
  • To improve the accuracy of LNM status prediction compared to existing methods.

Main Methods:

  • Utilized MALDI-MS imaging on TMAs from 43 endometrial primary carcinoma patients with known LNM status.
  • Proposed a variable selection method based on canonical correlation analysis, incorporating LNM information.
  • Applied Linear Discriminant Analysis (LDA) exclusively to the selected variables after leave-one-out cross-validation.

Main Results:

  • The proposed method achieved a misclassification rate of 2.3-20.9% in predicting LNM status.
  • This approach significantly outperformed standard LDA applied after principal component analysis (PCA) reduction of the data.
  • Demonstrated the effectiveness of targeted variable selection for high-dimensional proteomic data.

Conclusions:

  • The developed variable selection method, leveraging canonical correlation analysis, is effective for predicting LNM status from MALDI-MS imaging TMA data.
  • This approach offers a promising tool for clinical diagnosis by overcoming statistical limitations in analyzing complex proteomic datasets.
  • MALDI-MS imaging combined with advanced statistical analysis holds potential for advancing precision oncology.