Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929195605557248 |
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| author | Wang, Lujia Wang, Hairong D'Angelo, Fulvio Curtin, Lee Sereduk, Christopher P. De Leon, Gustavo Singleton, Kyle W. Urcuyo, Javier Hawkins-Daarud, Andrea Jackson, Pamela R. Krishna, Chandan Zimmerman, Richard S. Patra, Devi P. Bendok, Bernard R. Smith, Kris A. Nakaji, Peter Donev, Kliment Baxter, Leslie C. Mrugała, Maciej M. Ceccarelli, Michele Iavarone, Antonio Swanson, Kristin R. Tran, Nhan L. Hu, Leland S. Li, Jing |
| author_facet | Wang, Lujia Wang, Hairong D'Angelo, Fulvio Curtin, Lee Sereduk, Christopher P. De Leon, Gustavo Singleton, Kyle W. Urcuyo, Javier Hawkins-Daarud, Andrea Jackson, Pamela R. Krishna, Chandan Zimmerman, Richard S. Patra, Devi P. Bendok, Bernard R. Smith, Kris A. Nakaji, Peter Donev, Kliment Baxter, Leslie C. Mrugała, Maciej M. Ceccarelli, Michele Iavarone, Antonio Swanson, Kristin R. Tran, Nhan L. Hu, Leland S. Li, Jing |
| contents | Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00128 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm Wang, Lujia Wang, Hairong D'Angelo, Fulvio Curtin, Lee Sereduk, Christopher P. De Leon, Gustavo Singleton, Kyle W. Urcuyo, Javier Hawkins-Daarud, Andrea Jackson, Pamela R. Krishna, Chandan Zimmerman, Richard S. Patra, Devi P. Bendok, Bernard R. Smith, Kris A. Nakaji, Peter Donev, Kliment Baxter, Leslie C. Mrugała, Maciej M. Ceccarelli, Michele Iavarone, Antonio Swanson, Kristin R. Tran, Nhan L. Hu, Leland S. Li, Jing Machine Learning Computer Vision and Pattern Recognition Optimization and Control Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology. |
| title | Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm |
| topic | Machine Learning Computer Vision and Pattern Recognition Optimization and Control |
| url | https://arxiv.org/abs/2401.00128 |