463-05-007 ISMRM Abstract

Texture and Radiomic Feature Analysis in Breast MRI: Supervised Classification and Unsupervised Clustering of IDC and ILC

Primary: Brain Function and fMRI - fMRI Analysis

Secondary: Analysis Methods - Radiomics

463-05-007 · Malignant Tumor in the Body · Tuesday, 12 May, 4:00 PM–4:55 PM · Digital Posters Row D

Keywords: Radiomics Breast cancer Breast MRI Machine learning (ML) Texture analysis

Accepted

Mohan Jayatilake¹, Senal D Peiris¹, Dhevin Karunanayaka¹, Supundi Waidyarathna², Angela Choe¹, Sanjib Adhikary³, Prasanna Karunanayaka ¹

¹Radiology, The Pennsylvania State University, Hershey, Pennsylvania, United States of America

²Psychology, London Metropolitan University, London, United Kingdom

³Anesthesiology, The Pennsylvania State University, Hershey, Pennsylvania, United States of America

Presenting Author: Prasanna Karunanayaka

Synopsis

Motivation:

Goals:

Approach:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. RM, H. (1973). Texture features for image classification. IEEE Trans Syst Man Cybern, 3, 610-621.

2. Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563-577.

3. Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S., ... & Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications, 5(1), 4006.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/463-05-007