Gliomas, the most common malignant primary brain tumors, present significant challenges in classification and grading, which are crucial for treatment planning and prognosis. Despite advancements, current methods face limitations due to high costs, limited accessibility, and time-consuming results. This paper presents a novel hybrid quantum or classical-quantum computing model to differentiate between low-grade and high-grade gliomas using data from The Cancer Genome Atlas (TCGA). The study combines classical and quantum computing methods, with the quantum model achieving the highest classification accuracy. Molecular markers and clinical features play a significant role in the classification process.
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