Saad Nadeem1, Pengpeng Zhang1, Andreas Rimner2, Jan-Jakob Sonke3, Joseph O Deasy1, Allen Tannenbaum4. 1. Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA. 2. Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA. 3. Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. 4. Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, 11794, USA.
Abstract
PURPOSE: Conventional radiotherapy for large lung tumors is given over several weeks, during which the tumor typically regresses in a highly nonuniform and variable manner. Adaptive radiotherapy would ideally follow these shape changes, but we need an accurate method to extrapolate tumor shape changes. We propose a computationally efficient algorithm to quantitate tumor surface shape changes that makes minimal assumptions, identifies fixed points, and can be used to predict future tumor geometrical response. METHODS: A novel combination of nonrigid iterative closest point (ICP) and local shape-preserving map algorithms, LDeform, is developed to enable visualization, prediction, and categorization of both diffeomorphic and nondiffeomorphic tumor deformations during an extended course of radiotherapy. RESULTS: We tested and validated our technique on 31 longitudinal CT/MRI subjects, with five to nine time points each. Based on this tumor deformation analysis, regions of local growth, shrinkage, and anchoring are identified and tracked across multiple time points. This categorization in turn represents a rational biomarker of local response. Results demonstrate useful predictive power, with an averaged Dice coefficient and surface mean-squared error of 0.85 and 2.8 mm, respectively, over all images. CONCLUSIONS: We conclude that the LDeform algorithm can facilitate the adaptive decision-making process during lung cancer radiotherapy.
PURPOSE: Conventional radiotherapy for large lung tumors is given over several weeks, during which the tumor typically regresses in a highly nonuniform and variable manner. Adaptive radiotherapy would ideally follow these shape changes, but we need an accurate method to extrapolate tumor shape changes. We propose a computationally efficient algorithm to quantitate tumor surface shape changes that makes minimal assumptions, identifies fixed points, and can be used to predict future tumor geometrical response. METHODS: A novel combination of nonrigid iterative closest point (ICP) and local shape-preserving map algorithms, LDeform, is developed to enable visualization, prediction, and categorization of both diffeomorphic and nondiffeomorphic tumor deformations during an extended course of radiotherapy. RESULTS: We tested and validated our technique on 31 longitudinal CT/MRI subjects, with five to nine time points each. Based on this tumor deformation analysis, regions of local growth, shrinkage, and anchoring are identified and tracked across multiple time points. This categorization in turn represents a rational biomarker of local response. Results demonstrate useful predictive power, with an averaged Dice coefficient and surface mean-squared error of 0.85 and 2.8 mm, respectively, over all images. CONCLUSIONS: We conclude that the LDeform algorithm can facilitate the adaptive decision-making process during lung cancer radiotherapy.
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