RT info:eu-repo/semantics/article
T1 Predicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI
A1 Cepeda, Santiago
A1 Pérez Núñez, Ángel
A1 García García, Sergio
A1 Arrese, Ignacio
A1 Jiménez Roldán, Luis
A1 García Galindo, Manuel
A1 González, Pedro
A1 Velasco Casares, María
A1 Zamora, Tomas
A1 Sarabia Herrero, María Rosario
K1 Neurosciences
K1 Cancer Research
K1 Artificial intelligence
K1 Glioblastoma
K1 Radiomics
K1 Texture analysis
K1 Survival
K1 Radiómica
K1 Análisis de textura
K1 Supervivencia
K1 2490 Neurociencias
K1 3213.08 Neurocirugía
AB 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 (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.
PB MDPI
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59639
UL https://uvadoc.uva.es/handle/10324/59639
LA eng
NO Cancers, 2021, vol. 13, n. 20, 5047
NO Producción Científica
DS UVaDOC
RD 19-nov-2024