Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer
| العنوان: | Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer |
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| المؤلفون: | Sagus Sampath, Tao Wang, Ningrong Ye, Chi Wah Wong, Isa Mambetsariev, Rivka R. Colen, Zikuan Chen, Taihao Jin, Andrei I. Holodny, Russell C. Rockne, Ravi Salgia, Bihong T. Chen |
| المصدر: | Frontiers in Oncology Frontiers in Oncology, Vol 11 (2021) |
| بيانات النشر: | Frontiers Media S.A., 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Oncology, Cancer Research, medicine.medical_specialty, brain MRI, medicine.medical_treatment, medicine.disease_cause, lcsh:RC254-282, survival, 030218 nuclear medicine & medical imaging, Targeted therapy, 03 medical and health sciences, 0302 clinical medicine, Radiomics, Internal medicine, brain metastases, medicine, Lung cancer, Original Research, Proportional hazards model, business.industry, medicine.disease, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, artificial intelligence, lung cancer, machine learning, Sample size determination, radiomics, 030220 oncology & carcinogenesis, Cohort, KRAS, Non small cell, business |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 2234-943X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fb323891504df63cc5365290b321a5b http://europepmc.org/articles/PMC7973105 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5fb323891504df63cc5365290b321a5b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 2234943X |
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