RT info:eu-repo/semantics/article
T1 Current evidence, limitations and future challenges of survival prediction for glioblastoma based on advanced noninvasive methods: A narrative review
A1 García García, Sergio
A1 García Galindo, Manuel
A1 Arrese, Ignacio
A1 Sarabia Herrero, María Rosario
A1 Cepeda, Santiago
K1 Oncology
K1 Cancer Research
K1 Neurosciences
K1 Survival
K1 Supervivencia
K1 Artificial intelligence
K1 Machine learning
K1 Aprendizaje automático
K1 Glioblastoma
K1 Glioma
K1 3207.13 Oncología
K1 1203.04 Inteligencia Artificial
AB Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor’s biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
PB MDPI
SN 1648-9144
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/60271
UL https://uvadoc.uva.es/handle/10324/60271
LA eng
NO Medicina, 2022, Vol. 58, Nº. 12, 1746
NO Producción Científica
DS UVaDOC
RD 17-jul-2024