Summary of Quantifying Intra-tumoral Genetic Heterogeneity Of Glioblastoma Toward Precision Medicine Using Mri and a Data-inclusive Machine Learning Algorithm, by Lujia Wang et al.
Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm
by Lujia Wang, Hairong Wang, Fulvio D’Angelo, Lee Curtin, Christopher P. Sereduk, Gustavo De Leon, Kyle W. Singleton, Javier Urcuyo, Andrea Hawkins-Daarud, Pamela R. Jackson, Chandan Krishna, Richard S. Zimmerman, Devi P. Patra, Bernard R. Bendok, Kris A. Smith, Peter Nakaji, Kliment Donev, Leslie C. Baxter, Maciej M. Mrugała, Michele Ceccarelli, Antonio Iavarone, Kristin R. Swanson, Nhan L. Tran, Leland S. Hu, Jing Li
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel machine learning model called Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict the regional genetic alteration status within glioblastoma (GBM) tumors using magnetic resonance imaging (MRI). The WSO-SVM is trained on features extracted from MRI contrast images and can accurately predict the alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN). Compared to other machine learning algorithms, WSO-SVM achieves better classification accuracy for each gene. The study also uses SHapley Additive exPlanations (SHAP) method to compute contribution scores of different contrast images. Finally, the trained model is used to generate prediction maps within the tumoral area of each patient to visualize the intra-tumoral genetic heterogeneity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research aims to use MRI scans and machine learning to better understand and predict the genetic changes that occur in glioblastoma tumors. This can help doctors choose more effective treatments for individual patients. The researchers developed a new model called WSO-SVM that uses information from MRI scans to identify specific genetic alterations in different parts of the tumor. They tested this model on a large dataset of MRI scans and biopsies, and found that it was more accurate than other models. This can ultimately help doctors create personalized treatment plans for patients with glioblastoma. |
Keywords
* Artificial intelligence * Classification * Machine learning * Supervised * Support vector machine
