Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America.
Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America.
Phys Med Biol. 2020 Nov 12;65(22):225010. doi: 10.1088/1361-6560/abb6ba.
During minimally invasive surgery (MIS) for lung tumor resection, the localization of tumors or nodules relies on visual inspection of the deflated lung on intra-procedural video. For patients with tumors or nodules located deeper in the lung, this localization is not possible without prior invasive marking techniques. In efforts to avoid the increase of complication rates associated with these invasive techniques, this study investigates the use of biomechanical modeling of the lung deflation to predict the tumor localization during MIS, solely based on a pre-operative computed tomography (CT) scan. The feasibility of the proposed approach is evaluated using preliminary data from six patients who presented with pneumothorax after lung biopsy and underwent chest tube insertion. For each patient, a hyperelastic finite-element model of the lung was created from the CT scan showing the re-inflated lung. Boundary conditions were applied on the lung surface to simulate the gravity and insufflation of carbon dioxide in the chest. The impact of adding rigid constraints around the main airway was also evaluated. To evaluate the accuracy of the model in predicting lung tissues or potential tumor displacement, at least five corresponding landmarks were identified for each patient in the CT scans of their deflated and re-inflated lungs. Using these landmarks, target localization errors (TLE) were measured for different sets of pressure applied to lung surface and shear modulus. For five patients, the minimum achieved mean TLE was inferior to 9 mm using patient-specific parameters and inferior to 10 mm using the same parameterization. The predicted and ground truth deflated lung surfaces presented visually a relatively good agreement. The proposed approach thus appears as a promising tool for integration in future lung surgery image-guidance systems.
在微创肺肿瘤切除术中,肿瘤或结节的定位依赖于术中视频中瘪缩肺的视觉检查。对于位于肺深部的肿瘤或结节,在没有先前的侵入性标记技术的情况下,这种定位是不可能的。为了避免与这些侵入性技术相关的并发症发生率增加,本研究探讨了仅基于术前计算机断层扫描(CT)扫描,利用肺瘪缩的生物力学模型来预测微创术中的肿瘤定位。该方法的可行性通过来自 6 名气胸患者的初步数据进行评估,这些患者在肺活检后出现气胸并接受了胸腔引流管插入。对于每个患者,从显示再充气肺的 CT 扫描中创建了肺的超弹性有限元模型。在肺表面施加边界条件以模拟重力和胸腔内二氧化碳的充气。还评估了在主要气道周围添加刚性约束的影响。为了评估模型在预测肺组织或潜在肿瘤移位方面的准确性,在每个患者的瘪缩和再充气肺的 CT 扫描中至少为每个患者识别了五个对应的标记点。使用这些标记点,针对不同的肺表面压力和剪切模量集测量了目标定位误差(TLE)。对于五名患者,使用特定于患者的参数和相同的参数化,最小实现的平均 TLE 均小于 9mm。预测和地面真实瘪缩肺表面在视觉上呈现出相对较好的一致性。因此,该方法似乎是未来肺手术图像引导系统中集成的有前途的工具。