Development of a CT image-based virtual atelectasis simulation model and noninvasive lung nodule localization system.

BACKGROUND

In the process of video-assisted thoracoscopic surgery (VATS) for lung nodule resection, lung is leaded to atelectasis. However, preoperative computed tomography (CT) images are taken during inspiration, which means they differ significantly from the lung status observed during surgery. Consequently, this discrepancy can make the localization of small or subsolid nodules challenging during the operation. This study aimed to develop a CT-based virtual atelectasis simulation system for noninvasive lung nodule localization. By accurately simulating atelectasis, this study aimed to improve the precision of presurgical planning from lung nodule resections.

METHODS

This study retrospectively examined 20 patients who had either subsolid nodules or small nodules less than 3 cm in size, selected from a cohort of 279 patients who underwent VATS surgery for lung nodules in Korea University Ansan Hospital between June 28, 2021, and January 22, 2024. Chest CT images of the lungs of 20 patients were acquired, and image data were converted three-dimensional models. The mesh points extracted from these lung models were manipulated to simulate the effects of gravity, by adjusting the lung shapes and nodule locations to align with the respective surgical postures of the patients. Subsequently, we assessed the similarity of the simulation by comparing the resulting deformed lung shapes and nodule locations with the corresponding perspectives observed in the surgical videos.

RESULTS

The average volume of the entire lung among the patients was 2,336 cm (±588). After atelectasis simulation, the average lung shrinkage rate was 48.6% (±12.9%). Evaluations of an average of 15 pairs of images per case revealed significant conformity between atelectasis simulation images and surgical video snapshots, with average Dice and Jaccard similarity coefficient values of 90.27 and 88.25, respectively. Furthermore, the alignment of nodule locations between the simulations and surgical anticipation demonstrated notable accuracy, with an average Hausdorff distance of 6.39 mm.

CONCLUSIONS

We successfully developed a simulation of lung atelectasis based on preoperative CT scans that closely resembled actual surgical videos. The integration of this presurgical atelectasis simulation is anticipated to enhance the accuracy of nodule locations, thus contributing to more efficient and precise surgical planning.