PreprintReviewVersion 1This version is not peer-reviewed
From Voxel to Gene: a Scoping Reviews on MRI Radiogenomics Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas - the Promise of Virtual Biopsy?
Version 1
: Received: 1 August 2024 / Approved: 2 August 2024 / Online: 3 August 2024 (15:56:53 CEST)
How to cite:
LE GUILLOU HORN, X. From Voxel to Gene: a Scoping Reviews on MRI Radiogenomics Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas - the Promise of Virtual Biopsy?. Preprints2024, 2024080169. https://doi.org/10.20944/preprints202408.0169.v1
LE GUILLOU HORN, X. From Voxel to Gene: a Scoping Reviews on MRI Radiogenomics Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas - the Promise of Virtual Biopsy?. Preprints 2024, 2024080169. https://doi.org/10.20944/preprints202408.0169.v1
LE GUILLOU HORN, X. From Voxel to Gene: a Scoping Reviews on MRI Radiogenomics Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas - the Promise of Virtual Biopsy?. Preprints2024, 2024080169. https://doi.org/10.20944/preprints202408.0169.v1
APA Style
LE GUILLOU HORN, X. (2024). From Voxel to Gene: a Scoping Reviews on MRI Radiogenomics Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas - the Promise of Virtual Biopsy?. Preprints. https://doi.org/10.20944/preprints202408.0169.v1
Chicago/Turabian Style
LE GUILLOU HORN, X. 2024 "From Voxel to Gene: a Scoping Reviews on MRI Radiogenomics Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas - the Promise of Virtual Biopsy?" Preprints. https://doi.org/10.20944/preprints202408.0169.v1
Abstract
Background: Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. Aims and Methods: This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. Results: We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. Conclusions: Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
