<p>Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial for effective treatment and prognosis. While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. This study introduces a novel hybrid classical and quantum computing model to distinguish LGGs from HGGs using data from The Cancer Genome Atlas (TCGA). In the classical part, an ensemble feature selection method was employed to identify the most informative molecular markers and clinical features in the TCGA. In the quantum component, six variational quantum classifier (VQC) models with varying hyperparameters were evaluated. These classifiers utilize selected features to differentiate LGGs from HGGs. Among these, the VQC-1 model, which employs <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_97067_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="22" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{R}_{x}\)</EquationSource> </InlineEquation> and CX gates in the feature map and <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_97067_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="27" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\:R}_{y},\)</EquationSource> </InlineEquation> <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_97067_Article_IEq3.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{R}_{z}\)</EquationSource> </InlineEquation> and CY gates in the parameterized quantum circuit, achieved the highest classification accuracy of 0.74 using the AQCD optimization method. Additionally, VQC-1 identified IDH1, age at diagnosis, PTEN, EGFR, and ATRX, in descending order of importance, as the most informative features distinguishing LGGs from HGGs. Compared to classical machine learning models, VQC-1 demonstrated performance comparable to that of XGBoost and GBM, while outperformed KNN, SVC, DTC, and RFC in five-fold cross-validation experiments. This study provides a novel perspective on glioma classification by integrating classical and quantum computing, offering valuable insights into hybrid computational approaches.</p>

Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data

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2025年7月17日

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Emine Akpinar,
Murat Oduncuoglu

摘要

Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial for effective treatment and prognosis. While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. This study introduces a novel hybrid classical and quantum computing model to distinguish LGGs from HGGs using data from The Cancer Genome Atlas (TCGA). In the classical part, an ensemble feature selection method was employed to identify the most informative molecular markers and clinical features in the TCGA. In the quantum component, six variational quantum classifier (VQC) models with varying hyperparameters were evaluated. These classifiers utilize selected features to differentiate LGGs from HGGs. Among these, the VQC-1 model, which employs \(\:{R}_{x}\) and CX gates in the feature map and \(\:{\:R}_{y},\) \(\:{R}_{z}\) and CY gates in the parameterized quantum circuit, achieved the highest classification accuracy of 0.74 using the AQCD optimization method. Additionally, VQC-1 identified IDH1, age at diagnosis, PTEN, EGFR, and ATRX, in descending order of importance, as the most informative features distinguishing LGGs from HGGs. Compared to classical machine learning models, VQC-1 demonstrated performance comparable to that of XGBoost and GBM, while outperformed KNN, SVC, DTC, and RFC in five-fold cross-validation experiments. This study provides a novel perspective on glioma classification by integrating classical and quantum computing, offering valuable insights into hybrid computational approaches.