Orphanet Journal of Rare Diseases | Artificial Intelligence Applications in NF1-Associated Gliomas

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This systematic review covers AI applications in diagnosis and treatment of NF1-associated gliomas, including deep learning, machine learning, and radiomics technologies. The authors summarize available datasets and discuss challenges and future directions for AI in rare disease research.

 

Literature Overview
This article 'Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors' published in Orphanet Journal of Rare Diseases reviews AI applications in NF1-associated gliomas, focusing on pathological diagnosis, imaging analysis, molecular profiling, and treatment decision support. The article also discusses current data scarcity limitations and proposes potential solutions.

Background Knowledge
Neurofibromatosis Type 1 (NF1) is an autosomal dominant genetic disorder caused by NF1 gene mutations, characterized by skin, neurological, and skeletal manifestations. NF1 patients are prone to various gliomas including benign optic pathway gliomas (OPG) and malignant peripheral nerve sheath tumors (MPNST). Early identification and growth monitoring of these tumors are critical for clinical management. AI technologies, particularly deep learning and machine learning, have been applied in medical image segmentation, pathological analysis, molecular data modeling, and treatment response prediction, but their application in NF1-associated tumors remains in early stages. Current studies face challenges from small sample sizes and heterogeneous data, with most models requiring human interaction rather than full automation. Cross-institutional collaboration and data standardization are key to advancing AI implementation in NF1 research. Additionally, Explainable AI (XAI) technologies can enhance model interpretability and clinical credibility.

 

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Research Methods and Implementation
This article systematically reviews clinical and technical applications of AI in NF1-associated gliomas through literature review. Methods included searching PubMed/MEDLINE and Google Scholar databases with keywords including NF1, glioma, AI, etc., filtering for human studies, English publications, and peer-reviewed articles. The study specifically focused on imaging segmentation, pathological analysis, genomic data modeling, and treatment prediction for NF1-associated gliomas. Multiple AI models (e.g., U-Net, ResNet, 3D CNN) were evaluated across different studies using MRI images, whole slide images (WSI), and gene expression data. The research also analyzed potential of various data platforms (SYNAPSE, TCGA, OpenPBTA) for AI training, and identified data quality and standardization as primary barriers for current AI applications.

Key Findings and Perspectives

• Deep learning shows promising performance in MRI image segmentation, but application in NF1-associated gliomas is limited by data volume and model generalization capability.
• Machine learning models (e.g., random forest, ResNet) demonstrate superior performance over radiologists in differentiating benign and malignant tumors.
• Radiomics combined with AI can extract complex texture, shape, and signal intensity features for predicting tumor progression and treatment response.
• Explainable AI (XAI) technologies hold significant value in improving model interpretability, particularly in clinical settings.
• Current NF1-related AI research primarily focuses on diagnosis and classification, while treatment prediction and prognosis modeling require further exploration.
• Standardized data platforms (SYNAPSE, OpenPBTA) already provide standardized data, but further integration and quality control are needed.

Research Implications and Future Directions
AI applications in NF1-associated gliomas offer new tools for precision medicine, particularly in tumor segmentation, malignant transformation identification, and treatment response prediction. Future research should focus on cross-center and cross-platform data integration to improve model generalization, and combine multi-omics data to enhance prediction accuracy. Developing highly interpretable AI models will also facilitate clinical deployment and physician adoption.

 

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Conclusion
NF1 represents a complex hereditary tumor syndrome frequently associated with central and peripheral nervous system gliomas. Due to variable growth patterns and imaging heterogeneity, traditional diagnostic methods face limitations. AI technologies, particularly deep learning and machine learning, demonstrate significant potential in imaging analysis, pathological identification, and gene expression modeling. While multiple studies have explored AI applications in automatic segmentation and classification of NF1-associated gliomas, challenges remain regarding data volume and model interpretability. Future development directions include data standardization, multi-center collaboration, deep learning models integrating multi-omics information, and clinical translation of explainable AI technologies. Additionally, AI's role in treatment prediction and personalized medication requires further exploration to advance precision medicine for NF1-associated tumors.

 

Fabio Hellmann, Inka Ristow, Lennart Well, Elisabeth André, and Anja Harder. Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors. Orphanet Journal of Rare Diseases.