An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection.
Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them are eagerly needed to avoid potentially morbid biopsies and su...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2335 - 2347 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173050940&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050940 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050940 167359840 173050940 173050940 10.1007/s10278-023-00886-x 173050940 ppf: 2335 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection. aug: au: Felefly, Tony Roukoz, Camille Fares, Georges Achkar, Samir Yazbeck, Sandrine Meyer, Philippe Kordahi, Manal Azoury, Fares Nasr, Dolly Nehme Nasr, Elie Noël, Georges Francis, Ziad affil: Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon sug: subj: Brain Neoplasms Diagnosis Neoplasm Metastasis Diagnosis Glioma Diagnosis Diagnosis, Differential Magnetic Resonance Imaging Methods Radiographic Image Interpretation, Computer-Assisted Methods Neural Networks (Computer) Evaluation Prediction Models Evaluation Radiomics Human Retrospective Design Record Review Validity ROC Curve Descriptive Statistics Machine Learning Contrast Media Diagnostic Use Image Enhancement Funding Source ab: Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them are eagerly needed to avoid potentially morbid biopsies and surgical procedures. We explore herein the performance and interpretability of an MRI-radiomics variational quantum neural network (QNN) using a quantum-annealing mutual-information (MI) feature selection approach. We retrospectively included 423 patients with HGG and LBM (> 2 cm) who had a contrast-enhanced T1-weighted (CE-T1) MRI between 2012 and 2019. After exclusion, 72 HGG and 129 LBM were kept. Tumors were manually segmented, and a 5-mm peri-tumoral ring was created. MRI images were pre-processed, and 1813 radiomic features were extracted. A set of best features based on MI was selected. MI and conditional-MI were embedded into a quadratic unconstrained binary optimization (QUBO) formulation that was mapped to an Ising-model and submitted to D'Wave's quantum annealer to solve for the best combination of 10 features. The 10 selected features were embedded into a 2-qubits QNN using PennyLane library. The model was evaluated for balanced-accuracy (bACC) and area under the receiver operating characteristic curve (ROC-AUC) on the test set. The model performance was benchmarked against two classical models: dense neural networks (DNN) and extreme gradient boosting (XGB). Shapley values were calculated to interpret sample-wise predictions on the test set. The best 10-feature combination included 6 tumor and 4 ring features. For QNN, DNN, and XGB, respectively, training ROC-AUC was 0.86, 0.95, and 0.94; test ROC-AUC was 0.76, 0.75, and 0.79; and test bACC was 0.74, 0.73, and 0.72. The two most influential features were tumor Laplacian-of-Gaussian-GLRLM-Entropy and sphericity. We developed an accurate interpretable QNN model with quantum-informed feature selection to differentiate between LBM and HGG on CE-T1 brain MRI. The model performance is comparable to state-of-the-art classical models. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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