Toward Lung Tumor Localization based on Strain Variability of Lung Surface during Video-Assisted Thoracoscopic Surgery.

Tumor localization, especially in case of minimally invasive lung tumor resection surgery, is extremely challenging due to the continuous motion of the organ. This motion can be troublesome as it results in spatial discrepancy corresponding to preoperative and intraoperative tumor location. In order to characterize lung tissue stiffness for the purpose of lung tumor localization, in this paper, we present a novel characterization approach based on variability in resistance of the healthy region vs. the tumorous region resulting from lung motion. The proposed approach is numerically validated on a Finite Element (FE) model of the lung with varying surface stiffnesses, where higher stiffness represents tumor and lower stiffness corresponds to healthy lung tissue. The numerical simulation validates the sensitivity of our mechanism for different grades of tumors by demonstrating that the strain on the healthy tissue is 31.8 and 67.1 times higher than that on the tumor surface for a selected relative stiffness variation of 3.6x and 24.4x respectively, at a pressure of 1.6 KPa. Additionally, a framework is developed to validate the proposed approach in a video of a video-assisted thoracoscopic surgery (VATS), where multiple landmarks on the lung surface are tracked. This enables us to quantify the motion of points residing on healthy surface and tumorous surface. The motion data is further analyzed to study the relative surface strain, and it is shown that the proposed approach differentiates a tumor from healthy surface.