Association of graph-based spatial features with overall survival status of glioblastoma patients
Abstract Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44353-7 |
| _version_ | 1797559506538856448 |
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| author | Joonsang Lee Shivali Narang Juan Martinez Ganesh Rao Arvind Rao |
| author_facet | Joonsang Lee Shivali Narang Juan Martinez Ganesh Rao Arvind Rao |
| author_sort | Joonsang Lee |
| collection | DOAJ |
| description | Abstract Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma. |
| first_indexed | 2024-03-10T17:46:22Z |
| format | Article |
| id | doaj.art-e5af149ae2c048ebb8c114e86d1e31a4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:46:22Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e5af149ae2c048ebb8c114e86d1e31a42023-11-20T09:31:21ZengNature PortfolioScientific Reports2045-23222023-10-0113111110.1038/s41598-023-44353-7Association of graph-based spatial features with overall survival status of glioblastoma patientsJoonsang Lee0Shivali Narang1Juan Martinez2Ganesh Rao3Arvind Rao4Department of Computational Medicine and Bioinformatics, University of MichiganDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Neurosurgery, The University of Texas MD Anderson Cancer CenterDepartment of Neurosurgery, The University of Texas MD Anderson Cancer CenterDepartment of Computational Medicine and Bioinformatics, University of MichiganAbstract Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.https://doi.org/10.1038/s41598-023-44353-7 |
| spellingShingle | Joonsang Lee Shivali Narang Juan Martinez Ganesh Rao Arvind Rao Association of graph-based spatial features with overall survival status of glioblastoma patients Scientific Reports |
| title | Association of graph-based spatial features with overall survival status of glioblastoma patients |
| title_full | Association of graph-based spatial features with overall survival status of glioblastoma patients |
| title_fullStr | Association of graph-based spatial features with overall survival status of glioblastoma patients |
| title_full_unstemmed | Association of graph-based spatial features with overall survival status of glioblastoma patients |
| title_short | Association of graph-based spatial features with overall survival status of glioblastoma patients |
| title_sort | association of graph based spatial features with overall survival status of glioblastoma patients |
| url | https://doi.org/10.1038/s41598-023-44353-7 |
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