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Single-cell RNA insights and densely scaled vision transformer-based MRI classification for precision brain tumors
Pratikkumar Chauhan1, Munindra Lunagaria1, Deepak Kumar Verma1
1Department of Computer Engineering, Marwadi University, Rajkot, Gujarat 360003, India.
Abstract:
There are >1600 evolutionarily conserved RNA-binding proteins (RBPs) in the human genome. Many multi-omics studies have demonstrated that these proteins are often not working properly in malignancies like glioblastoma and melanoma. These RBPs are very important for the complex regulatory networks that govern the activities that are typical of cancer. RBPs' intricate control of RNA activity at many levels and their post-translational modifications, which make them more functional, make things even more convoluted. Additionally, other RBP-based therapies have emerged, each underpinned by distinct molecular mechanisms, including genomic analysis and the inhibition of RBP functionality. This paper reports findings from patients with brain tumours undergoing experimental RNA interference treatment. We also suggest a Densely Scaled Vision Transformer (DSViT) made to find and locate brain tumors of different types. The model is evaluated on the FigShare Brain Tumor Dataset comprising 3064 MRI images categorized into Glioma, Meningioma, and Pituitary tumors, with final testing conducted on 614 samples. Experimental results show that DSViT achieves an accuracy of 96.09 %, precision of 96.57 %, recall of 95.97 %, and an F1-score of 96.27 %, significantly outperforming the ViT-Baseline and ablation variants. Future directions include extending DSViT into multimodal pipelines that fuse imaging with molecular profiles, thereby enhancing precision neuro-oncology. Its modular structure also enables integration into radiological reporting systems for automated annotation and clinician-guided decision support. This innovative RNA interference (iRNAi) based therapeutic intervention has significant therapeutic potential and is, as far as we are aware, the first time RNA interference has been used to treat human disease.
Insights
This study introduces an innovative RNA interference (iRNAi) therapy for brain tumors and a Densely Scaled Vision Transformer (DSViT) for accurate tumor detection, achieving high diagnostic performance.
Area of Science:
- Oncology
- Bioinformatics
- Medical Imaging
Background:
- Over 1600 RNA-binding proteins (RBPs) are crucial in human gene regulation, with dysregulation observed in cancers like glioblastoma and melanoma.
- RBPs play complex roles in cancer through intricate RNA activity control and post-translational modifications.
- Emerging RBP-based therapies, including genomic analysis and functional inhibition, show promise for cancer treatment.
Purpose of the Study:
- To report findings from brain tumor patients treated with experimental RNA interference (iRNAi).
- To introduce and evaluate a Densely Scaled Vision Transformer (DSViT) model for brain tumor detection and localization.
- To explore the therapeutic potential of iRNAi in treating human diseases, specifically brain tumors.
Main Methods:
- Development and evaluation of a Densely Scaled Vision Transformer (DSViT) model for brain tumor classification.
- Utilizing the FigShare Brain Tumor Dataset (3064 MRI images) for training and validation, with final testing on 614 samples.
- Conducting experimental RNA interference (iRNAi) treatment in patients with brain tumors.
Main Results:
- The DSViT model achieved high performance on the brain tumor dataset: 96.09% accuracy, 96.57% precision, 95.97% recall, and 96.27% F1-score.
- DSViT significantly outperformed the ViT-Baseline and ablation variants in brain tumor detection.
- The study presents the first known instance of RNA interference (iRNAi) being used as a therapeutic intervention in human disease.
Conclusions:
- The developed DSViT model demonstrates significant potential for accurate brain tumor detection and localization.
- RNA interference (iRNAi) represents a novel and promising therapeutic strategy for brain tumors and potentially other human diseases.
- Future work will focus on integrating DSViT into multimodal pipelines for enhanced precision neuro-oncology and clinical decision support systems.
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