Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer
Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.621088/full |
| _version_ | 1819320468097728512 |
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| author | Bihong T. Chen Taihao Jin Ningrong Ye Isa Mambetsariev Tao Wang Chi Wah Wong Zikuan Chen Russell C. Rockne Rivka R. Colen Andrei I. Holodny Sagus Sampath Ravi Salgia |
| author_facet | Bihong T. Chen Taihao Jin Ningrong Ye Isa Mambetsariev Tao Wang Chi Wah Wong Zikuan Chen Russell C. Rockne Rivka R. Colen Andrei I. Holodny Sagus Sampath Ravi Salgia |
| author_sort | Bihong T. Chen |
| collection | DOAJ |
| description | Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration.Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration.Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22–85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group.Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results. |
| first_indexed | 2024-12-24T11:20:03Z |
| format | Article |
| id | doaj.art-7cd0d04869fa47e0afdea474c07bf42c |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-24T11:20:03Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-7cd0d04869fa47e0afdea474c07bf42c2022-12-21T16:58:17ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-03-011110.3389/fonc.2021.621088621088Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung CancerBihong T. Chen0Taihao Jin1Ningrong Ye2Isa Mambetsariev3Tao Wang4Chi Wah Wong5Zikuan Chen6Russell C. Rockne7Rivka R. Colen8Andrei I. Holodny9Sagus Sampath10Ravi Salgia11Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United StatesDepartment of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United StatesDepartment of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United StatesDepartment of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United StatesDepartments of Interventional Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, ChinaApplied AI and Data Science, City of Hope National Medical Center, Duarte, CA, United StatesDepartment of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United StatesDivision of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, United StatesDepartment of Radiology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United StatesDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, United StatesDepartment of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United StatesDepartment of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United StatesBackground: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration.Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration.Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22–85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group.Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.https://www.frontiersin.org/articles/10.3389/fonc.2021.621088/fullradiomicsmachine learningsurvivallung cancerbrain metastasesbrain MRI |
| spellingShingle | Bihong T. Chen Taihao Jin Ningrong Ye Isa Mambetsariev Tao Wang Chi Wah Wong Zikuan Chen Russell C. Rockne Rivka R. Colen Andrei I. Holodny Sagus Sampath Ravi Salgia Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer Frontiers in Oncology radiomics machine learning survival lung cancer brain metastases brain MRI |
| title | Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer |
| title_full | Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer |
| title_fullStr | Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer |
| title_full_unstemmed | Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer |
| title_short | Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer |
| title_sort | predicting survival duration with mri radiomics of brain metastases from non small cell lung cancer |
| topic | radiomics machine learning survival lung cancer brain metastases brain MRI |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.621088/full |
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