Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI
| Autor: | Luis Jiménez-Roldán, Pedro Gonzalez, Ignacio Arrese, Rosario Sarabia, Daniel García-Pérez, Manuel Garcia-Galindo, Sergio García-García, Angel Perez-Nuñez, María Velasco-Casares, Tomás Zamora, Santiago Cepeda |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cancer Research
Artificial intelligence Machine learning computer.software_genre survival Article Radiómica Naive Bayes classifier Radiomics 3213.08 Neurocirugía medicine Multiparametric Magnetic Resonance Imaging Survival analysis texture analysis RC254-282 Análisis de textura Supervivencia business.industry Area under the curve Neurosciences glioblastoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Retrospective cohort study medicine.disease machine learning Oncology Feature (computer vision) radiomics 2490 Neurociencias business computer Glioblastoma |
| Zdroj: | Cancers, Vol 13, Iss 5047, p 5047 (2021) Cancers Volume 13 Issue 20 |
| ISSN: | 2072-6694 |
| Popis: | Producción Científica Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality ( |
| Databáze: | OpenAIRE |
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