Exploring a Patient-Specific Respiratory Motion Prediction Model for Tumor Localization in Abdominal and Lung SBRT Patients

Abstract

Managing respiratory motion in radiotherapy for abdominal and lung stereotactic body radiation therapy (SBRT) patients is crucial to achieving dose conformity and sparing healthy tissue. While previous studies have utilized principal component analysis (PCA) combined with deep learning to localize lung tumors in real-time, these models lack testing or validation with patient data from later treatment fractions. This study aims to achieve highly accurate 3D tumor localization for abdominal and lung cancer patients using a PCA-based convolutional neural network (CNN) motion model. Another goal is to enhance this prediction model by addressing interfractional motion in lung patients through deep learning and multiple treatment-acquired CT datasets.The patient's 4D computed tomography (4DCT) image was registered, and resulting deformation vector fields (DVFs) were approximated using PCA. PCA coefficients, linked to diaphragm displacement, controlled breath variability in synthetic CT images. Digitally reconstructed radiographs (DRRs) from synthetic CTs served as network input. The networks were evaluated using a CT image designated for abdominal and lung cancer patient testing. A DRR of the testing CT was input into the model, and the predicted CT image was validated against the test CT to locate the tumor.This study validated a PCA-based CNN motion model for abdominal and lung cancer patients. Intrafractional motion modeling accurately predicted abdominal tumors with a maximum error of 1.41 mm, whereas lung tumor localization resulted in a maximum error of 2.83 mm. Interfractional motion significantly influenced the accuracy of lung tumor prediction.