In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy. (March 2021)

• Record Type: Journal Article
• Authors: Jafari, Parya; Dempsey, Sergio; Hoover, Douglas A.; Karami, Elham; Gaede, Stewart; Sadeghi-Naini, Ali; Lee, Ting Yim; Samani, Abbas
• Is Part Of: Computers in biology and medicine. Volume 130(2021)
• Publication Date: 2021-03
• Subjects: Lung biomechanical model -- Respiratory motion -- Tumor tracking -- Image registration -- Radiation therapy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104231
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Lung cancer is the most common cause of cancer-related death in both men and women. Radiation therapy is widely used for lung cancer treatment; however, respiratory motion presents challenges that can compromise the accuracy and/or effectiveness of radiation treatment. Respiratory motion compensation using biomechanical modeling is a common approach used to address this challenge. This study focuses on the development and validation of a lung biomechanical model that can accurately estimate the motion and deformation of lung tumor. Towards this goal, treatment planning 4D-CT images of lung cancer patients were processed to develop patient-specific finite element (FE) models of the lung to predict the patients' tumor motion/deformation. The tumor motion/deformation was modeled for a full respiration cycle, as captured by the 4D-CT scans. Parameters driving the lung and tumor deformation model were found through an inverse problem formulation. The CT datasets pertaining to the inhalation phases of respiration were used for validating the model's accuracy. The volumetric Dice similarity coefficient between the actual and simulated gross tumor volumes (GTVs) of the patients calculated across respiration phases was found to range between 0.80 ± 0.03 and 0.92 ± 0.01. The average error in estimating tumor's center of mass calculated across respiration phases ranged between 0.50 ± 0.10 (mm) and 1.04 ± 0.57 (mm), indicating a reasonably good accuracy of the proposed model. The proposed model demonstrates favorable accuracy for estimating the lung tumor motion/deformation, and therefore can potentially be used in radiation therapy applications for respiratory motion compensation. Highlights: A patient-specific lung FE model is proposed to predict the patients' tumor motion/deformation. Loading in the model is formulated to be adaptable for real-time tumor tracking. The model takes full advantage of patient-specific image data to determine respiration driving forces and tissue properties. Lung tissue compressibility variability throughout respiration cycle is considered. The model indicates reasonably high accuracy in capturing the lung and tumor motion and deformation.