Multi-modality classification between myxofibrosarcoma and myxoma using radiomics and machine learning models

Can Cui; Samuel R. Johnson; Cullen P. Moran; Katherine S. Hajdu; Joanna Shechtel; John J. Block; Brian Bingham; David Smith; Leo Y. Luo; Hakmook Kang; Jennifer L. Halpern; Herbert S. Schwartz; Ginger E. Holt; Joshua M. Lawrenz; Benoit M. Dawant

doi:10.1117/12.2612311

4 April 2022 Multi-modality classification between myxofibrosarcoma and myxoma using radiomics and machine learning models

Can Cui, Samuel R. Johnson, Cullen P. Moran, Katherine S. Hajdu, Joanna Shechtel, John J. Block, Brian Bingham, David Smith, Leo Y. Luo, Hakmook Kang, Jennifer L. Halpern, Herbert S. Schwartz, Ginger E. Holt, Joshua M. Lawrenz, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203330 (2022) https://doi.org/10.1117/12.2612311
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Myxofibrosarcoma is a rare, malignant myxoid soft tissue tumor. It can be challenging to distinguish it from a benign myxoma in clinical practice as there exists imaging and histologic feature overlap between these two entities. Some previous works used radiomics features of T1-weighted images to differentiate myxoid tumors, but few have used multimodality data. In this project, we collect a dataset containing 20 myxomas and 20 myxofibrosarcomas, each with a T1- weighted image, a T2-weighted image, and clinical features. Radiomics features from multi-modality images and clinical features are used to train multiple machine learning models. Our experiment results show that the prediction accuracy using the multi-modality features surpasses the results from a single modality. The radiomics features Gray Level Variance, Gray Level Non-uniformity Normalized extracted from the Gray Level Run Length Matrix (GLRLM) of the T2 images, and age are the top three features selected by the least absolute shrinkage and selection operator (LASSO) feature reduction model

Citation Download Citation

Can Cui, Samuel R. Johnson, Cullen P. Moran, Katherine S. Hajdu, Joanna Shechtel, John J. Block, Brian Bingham, David Smith, Leo Y. Luo, Hakmook Kang, Jennifer L. Halpern, Herbert S. Schwartz, Ginger E. Holt, Joshua M. Lawrenz, and Benoit M. Dawant "Multi-modality classification between myxofibrosarcoma and myxoma using radiomics and machine learning models", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203330 (4 April 2022); https://doi.org/10.1117/12.2612311

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available