Pretreatment Radiation Esophagitis Prediction Using Quantum Machine Learning in Patients With Esophageal Cancer

To introduce a hybrid quantum-classical machine learning approach and validate its feasibility and accuracy for pretreatment radiation-induced esophagitis (RE) prediction in patients with esophageal cancer undergoing radiation therapy or chemoradiotherapy.

This study enrolled 218 patients with esophageal cancer from hospital 1 for training and internal validation and 55 patients with esophageal cancer from hospital 2 for external validation, with grade ≥2 RE incidences of 64 and 20, respectively. Dose distribution images were converted into quantum states via angle encoding. Quantum features (Qs) were extracted using 3 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 machine learning model integrates handcrafted dosiomic features (Ds), Qs, and clinical factors (C) through a feature-level concatenation. The concatenated feature vector was processed by a Random Forest classifier for RE prediction.

Models using only Qs 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 Qs 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 (internal validation); and 0.83, 0.73, 0.89 (external validation), respectively. The Random Forest model using fused features Q + D + C extracted via Q-CNN + Q-attention achieved AUCs of 0.89 (training), 0.89 (internal validation), and 0.83 (external validation), outperforming models using only Q-CNN + Q-attention (AUCs: 0.78 training, 0.78 internal validation, and 0.80 external validation).

This study proposes a novel quantum-classical machine learning method for pretreatment RE prediction. By integrating quantum amplitude encoding, quantum attention mechanisms, and multimodal feature fusion (Q + D + C), the model enhances prediction accuracy and reliability, demonstrating significant potential for clinical application.