Segmentectomy Guided by 3-Dimensional Images Reconstructed From Nonenhanced Computed Tomographic Data

Robotic-assisted right upper lobectomy with systemic pulmonary vein anomaly: a case report

BACKGROUND

While the role of low-dose computed tomography (CT) in lung cancer screening is established, its limitations in detailing pulmonary vascular variations are less emphasized. Three-dimensional reconstruction technology allows surgeons to reconstruct a patient's bronchial and pulmonary vascular structures using CT scan results. However, low-dose CT may not provide the same level of clarity as enhanced CT in displaying pulmonary vascular details. This limitation can be unfavorable for preoperative detection of potential pulmonary vascular variations, especially in cases involving planned segmentectomy.

CASE PRESENTATION

We report a case of a 58-year-old female with lung cancer, initially planned for Da Vinci robot-assisted thoracoscopic segmentectomy. Unexpectedly, during surgery, a pulmonary vein variation in the right upper lobe was discovered, leading to a change in the surgical method to a lobectomy. The patient had four variant right upper lobe veins draining into the superior vena cava and one into the left atrium. The surgery was complicated by significant bleeding and postoperative pulmonary congestion. Postoperative pathology confirmed adenocarcinoma.

CONCLUSIONS

This case highlights the importance of meticulous intraoperative exploration, particularly in cases involving planned segmentectomy, as unexpected pulmonary vein variations can significantly affect surgical decision-making. While three-dimensional reconstruction based on preoperative CT data is a valuable tool, it may not capture the full complexity of the anatomical variations. We discuss potential preoperative imaging techniques, including contrast-enhanced CT and CT angiography, as methods to better identify these variations. The enhanced visualization provided by robot-assisted surgery plays a crucial role in identifying and adapting to these variations, underscoring the advantages of this surgical approach. Our report contributes to the existing literature by providing a detailed account of how these principles were applied in a real-world scenario, reinforcing the need for surgical adaptability and awareness of the limitations of low-dose CT in complex cases.

Three-dimensional image simulation for lung segmentectomy from unenhanced computed tomography data

We developed a novel three-dimensional (3D) image simulation system, focused on pulmonary segmentectomy. The novel algorithms run by the software, which are independent of the differences in computed tomography (CT) values of vascular structures, enabled the creation of 3D images from unenhanced CT data with accuracy comparable to that from contrast-enhanced CT data. To evaluate the anatomical accuracy, we compared it between images created from unenhanced and contrast-enhanced CT in seven patients who underwent thoracoscopic segmentectomy. With regard to the automatic recognition of pulmonary vessels, the 3D image from unenhanced CT falsely recognized one or two points in two cases, whereas that from contrast-enhanced CT false recognitions in one case. Both 3D images had similar creation time and capability for identifying the intersegmental plain. The novel 3D image simulation for segmentectomy from unenhanced CT had sufficient anatomical accuracy for practical use but required attention due to inevitable minor false recognition.

Technical Advances in Segmentectomy for Lung Cancer: A Minimally Invasive Strategy for Deep, Small, and Impalpable Tumors

With the increased detection of early-stage lung cancer and the technical advancement of minimally invasive surgery (MIS) in the field of thoracic surgery, lung segmentectomy using MIS, including video- and robot-assisted thoracic surgery, has been widely adopted. However, lung segmentectomy can be technically challenging for thoracic surgeons due to (1) complex segmental and subsegmental anatomy with frequent anomalies, and (2) difficulty in localizing deep, small, and impalpable tumors, leading to difficulty in obtaining adequate margins. In this review, we summarize the published evidence and discuss key issues related to MIS segmentectomy, focusing on preoperative planning/simulation and intraoperative tumor localization. We also demonstrate two of our techniques: (1) three-dimensional computed tomography (3DCT)-based resection planning using a novel 3DCT processing software, and (2) tumor localization using a novel radiofrequency identification technology.