Background: Adaptive radiotherapy for non-small cell lung cancer (NSCLC) requires accurate image registration to account for anatomical changes during treatment. Artificial intelligence (AI)-based approaches have shown potential in improving automated landmark detection and deformable image registration. Aims and Objectives: This study aimed to investigate the application of AI for automated anatomic landmark detection and image deformation in NSCLC cases to support adaptive radiotherapy planning. Materials and Methods: A multimodal image registration approach combining cone-beam computed tomography (CBCT) and computed tomography (CT) images was implemented. The workflow consisted of multistage registration, beginning with rigid registration followed by deep-learning-based deformable registration using the VoxelMorph framework. A total of 1040 axial CBCT and CT images were used for training, validation, and testing of the landmark detection model based on the You Only Look Once (YOLO) algorithm. Various YOLO models were compared for spine landmark detection. Model performance was evaluated using Intersection over Union (IoU), Mean Average Precision (mAP), Dice Similarity Coefficient (DSC), and Target Registration Error (TRE). Results: Among the evaluated models, YOLOv3 achieved the highest accuracy for spine landmark detection, with an IoU of 0.818 and an mAP of 0.66. For organ-at-risk segmentation in deformable registration, YOLOv9 outperformed YOLOv8. Rigid registration demonstrated an average DSC of 0.88 ± 0.04 and a TRE of 1.7 ± 1.0 mm, within the tolerance range recommended by AAPM Task Group 132. Deformable registration using VoxelMorph achieved acceptable micro-DSC values; however, macro-DSC results indicated the need for further refinement. Conclusion: AI-based methods demonstrate promising performance for automated landmark detection and deformable image registration in adaptive radiotherapy for NSCLC. Nevertheless, further optimization is required to improve accuracy and reduce variability in registered images before routine clinical implementation.
Zulaikho et al. (Thu,) studied this question.