Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities

SIMPLE SUMMARY: Despite their prevalence in research, ML tools that can predict glioma grade from medical images have yet to be incorporated clinically. The reporting quality of ML glioma grade prediction studies is below 50% according to TRIPOD—limiting model reproducibility and, thus, clinical tra...

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Detalles Bibliográficos
Autores principales: Merkaj, Sara, Bahar, Ryan C., Zeevi, Tal, Lin, MingDe, Ikuta, Ichiro, Bousabarah, Khaled, Cassinelli Petersen, Gabriel I., Staib, Lawrence, Payabvash, Seyedmehdi, Mongan, John T., Cha, Soonmee, Aboian, Mariam S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179416/
https://www.ncbi.nlm.nih.gov/pubmed/35681603
http://dx.doi.org/10.3390/cancers14112623

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179416/
https://www.ncbi.nlm.nih.gov/pubmed/35681603
http://dx.doi.org/10.3390/cancers14112623

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