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Machine Learning in 4DCT Lung Stereotactic Body Radiotherapy (SBRT) Treatment Planning

Workshop: Eighth Computational Approaches for Cancer Workshop (CAFCW22)

Authors: David Gonzalez, Ignacio Bartol, and Sam Taylor (Georgia Institute of Technology); Anees Dhabaan and Mohammad Khan (Emory University); and Shaheen Dewji (Georgia Institute of Technology)

Abstract: The current project seeks to enhance current 4DCTlung cancer patient image processing, image guidance, and adaptive radiotherapy verification through the integration of machine learning (Artificial Intelligence - AI) methods in an existing clinical radiation oncology framework. The current state-of-the-art for Lung SBRT treatment planning begins with the accurate delineation of target organ volumes and their surrounding structures, which is usually done using semi-automatic methods, mixing computer-assisted tools, and dedicated physicians. When it comes to 4DCT scans, what is usually done is to compute a visual average of the images across the different respiratory phases and the contours for those organs (in one specific phase) are delineated. In the last few years, deformable image registration (DIR) techniques have been developed and used in this field to propagate the contour delineation from one specific phase to the rest of the respiratory phases in the CT. Results of the target region delineation are then used by physicians and clinicians to select an optimal treatment phase. Rather than the mostly manual and slow/iterative process introduced above, our current project seeks to create more accurate and more robust delineations through improved machine learning models, decreasing time spent per patient plan, and applying a more mathematically rigorous and objective manner of selecting the optimal radiation treatment gating window, while enhancing image resolution, enhancing target definition, and treatment delivery.

The project is subsequently divided in various phases. One phase consists of deriving the deformation parameters to describe the three-dimensional movement of the patient's target treatment region through deformation propagation. The second phase involves a surrogate model for fast reconstruction of the dose distribution in the gross tumor volume(s), GTV(s), and organs at risk ,OAR(s), across all the phases, accounting for the deformed target region in time. With these dose profiles, the physicians will have the bounds (within a confidence interval) for the absorbed dose by different organs and tumors across all the respiratory cycle and they will be able to determine if the treatment plan for that patient is accurate and appropriate or if it needs to be replanned.

Additional phases of the algorithm use AI approached to enhance this step. The project's algorithm is unique and captures a higher degree of individualization based on the patient's specific organ movement compared with prior non-AI algorithms. Although other studies in the past have explored integrating AI for image segmentation for auto-contouring, our project's novelty lies in the manner of initialized parameters and the specific operations performed. Our project furthers patient-specific treatment planning while adopting a more streamlined approach and helps make more informed decisions using AI, to arrive at improved radiation treatment plans for lung cancer patients undergoing SBRT. We have tested our algorithm in several patients and have seen encouraging improvements.


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