Pretreatment Radiation Esophagitis Prediction using Quantum Machine Learning in Esophageal Cancer Patients
Abstract
PURPOSE
To introduce a hybrid quantum-classical machine learning (QML) approach and validate its feasibility and accuracy for pretreatment radiation esophagitis (RE) prediction in patients with esophageal cancer (EC) undergoing radiotherapy or chemoradiotherapy.
METHODS
This study enrolled 218 EC patients (training and internal validation) from hospital one and 55 EC patients (external validation) from hospital two, with grade ≥ 2 RE incidences of 64 and 20, respectively. Dose distribution images were converted into quantum states via angle encoding. Quantum features (Q) were extracted using three quantum models: 1) classical convolutional neural network (CNN) with quantum convolution (Q-CNN), 2) Q-CNN plus classical Attention (Q-CNN+Attention), and 3) Q-CNN plus quantum Attention (Q-CNN+Q-Attention). The hybrid QML model integrates handcrafted dosiomic features (D), Quantum features (Q), and clinical factors (C) through a feature-level concatenation. The concatenated feature vector was processed by a Random Forest classifier for RE prediction.
RESULTS
Models using only quantum features achieved an accuracy of 0.70 (Q-CNN), 0.83 (Q-CNN +Attention), and 0.80 (Q-CNN+ Q-Attention) in external validation, respectively. Feature fusion (Q+D+C) improved the accuracy of models in comparison with quantum features alone. Q (Q-CNN+Q-Attention) + D + C demonstrated optimal performance, achieving accuracy, sensitivity, and specificity of 0.85, 0.83, 0.92 (training); 0.80, 0.73, 0.84…
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