Preoperative evaluation of the segmental artery by three-dimensional image reconstruction vs. thin-section multi-detector computed tomography

Pulmonary Arterial Anatomical Patterns: a Classification Scheme Based on Lobectomy and 3D-CTBA

PURPOSE

 Preoperative evaluation of pulmonary vascular and tracheal routes and variations is of great importance to the surgeon. Three-dimensional computed tomography bronchography and angiography (3D-CTBA) has evolved in recent years with the optimization of 3D reconstruction techniques and artificial intelligence. We aim to apply CT angiography and Exoview 3D reconstruction technology to assess patients' pulmonary arterial tree and its anatomical variants and to try to summarize a set of anatomical typing of the pulmonary arterial tree that is relatively easy and conducive to promoting teaching based on surgical habits of lobectomy.

METHODS

 A total of 358 patients hospitalized in the Department of Thoracic Surgery of the First Affiliated Hospital of Soochow University between July 2020 and August 2021 were included in this study. We carefully analyzed the site of emanation, alignment, and number of branches of the pulmonary artery according to a uniform classification method in conjunction with the two-dimensional CT images and transformed them into 3D reconstruction models.

RESULTS

 Different types of pulmonary artery were observed in 358 cases. We evaluated the complete pulmonary artery tree and counted the number and frequency of major arteries of the pulmonary based on the surgical habits of anatomical lobectomy.

CONCLUSION

 The 3D-CTBA technique enables us to adequately assess the anatomy of the pulmonary arteries. Moreover, we provide a practical classification scheme of pulmonary arterial anatomical patterns based on lobectomy and 3D-CTBA. Our data can be used by clinicians in the teaching of pulmonary artery anatomy and the preoperative preparation for anatomical lobectomy.

Harnessing 3D-CT Simulation and Planning for Enhanced Precision Surgery: A Review of Applications and Advancements in Lung Cancer Treatment

The clinical application of three-dimensional computed tomography (3D-CT) technology has rapidly expanded in the last decade and has been applied to lung cancer surgery. Two consecutive reports of large-scale prospective clinical trials from Japan and the United States have brought a paradigm shift in lung cancer surgery and may have led to a rapid increase in sublobar lung resections. Sublobar resection, especially segmentectomy, requires a more precise understanding of the anatomy than lobectomy, and preoperative 3D simulation and intraoperative navigation support it. The latest 3D simulation software packages are user-friendly. Therefore, in this narrative review, we focus on recent attempts to apply 3D imaging technologies, particularly in the sublobar resection of the lung, and review respective research and outcomes. Improvements in CT accuracy and the use of 3D technology have advanced lung segmental anatomy. Clinical applications have enabled the safe execution of complex sublobar resection through a minimally invasive approach, such as video-assisted thoracoscopic surgery and robotic surgery. However, currently, many facilities still render 3D images on two-dimensional monitors for usage. In the future, it will be challenging to further spread and advance intraoperative navigation through the application of 3D output technologies such as extended reality.

AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels

Background

The recognition of anatomical variants is essential in preoperative planning for lung cancer surgery. Although three-dimensional (3-D) reconstruction provided an intuitive demonstration of the anatomical structure, the recognition process remains fully manual. To render a semiautomated approach for surgery planning, we developed an artificial intelligence (AI)–based chest CT semantic segmentation algorithm that recognizes pulmonary vessels on lobular or segmental levels. Hereby, we present a retrospective validation of the algorithm comparing surgeons’ performance.

Methods

The semantic segmentation algorithm to be validated was trained on non-contrast CT scans from a single center. A retrospective pilot study was performed. An independent validation dataset was constituted by an arbitrary selection from patients who underwent lobectomy or segmentectomy in three institutions during Apr. 2020 to Jun. 2021. The golden standard of anatomical variants of each enrolled case was obtained via expert surgeons’ judgments based on chest CT, 3-D reconstruction, and surgical observation. The performance of the algorithm is compared against the performance of two junior thoracic surgery attendings based on chest CT.

Results

A total of 27 cases were included in this study. The overall case-wise accuracy of the AI model was 82.8% in pulmonary vessels compared to 78.8% and 77.0% for the two surgeons, respectively. Segmental artery accuracy was 79.7%, 73.6%, and 72.7%; lobular vein accuracy was 96.3%, 96.3%, and 92.6% by the AI model and two surgeons, respectively. No statistical significance was found. In subgroup analysis, the anatomic structure-wise analysis of the AI algorithm showed a significant difference in accuracies between different lobes (p = 0.012). Higher AI accuracy in the right-upper lobe (RUL) and left-lower lobe (LLL) arteries was shown. A trend of better performance in non-contrast CT was also detected. Most recognition errors by the algorithm were the misclassification of LA¹⁺² and LA³. Radiological parameters did not exhibit a significant impact on the performance of both AI and surgeons.

Conclusion

The semantic segmentation algorithm achieves the recognition of the segmental pulmonary artery and the lobular pulmonary vein. The performance of the model approximates that of junior thoracic surgery attendings. Our work provides a novel semiautomated surgery planning approach that is potentially beneficial to lung cancer patients.

A fully automated noncontrast CT 3-D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy

Background

Three‐dimensional reconstruction of chest computerized tomography (CT) excels in intuitively demonstrating anatomical patterns for pulmonary segmentectomy. However, current methods are labor‐intensive and rely on contrast CT. We hereby present a novel fully automated reconstruction algorithm based on noncontrast CT and assess its performance both independently and in combination with surgeons.

Methods

A retrospective pilot study was performed. Patients between May 2020 to August 2020 who underwent segmentectomy in our single institution were enrolled. Noncontrast CTs were used for reconstruction. In the first part of the study, the accuracy of the demonstration of anatomical variants by either automated or manual reconstruction algorithm were compared to surgical observation, respectively. In the second part of the study, we tested the accuracy of the identification of anatomical variants by four independent attendees who reviewed 3‐D reconstruction in combination with CT scans.

Results

A total of 20 cases were enrolled in this study. All segments were represented in this study with two left S1‐3, two left S4 + 5, one left S6, five left basal segmentectomies, one right S1, three right S2, 1 right S2b + 3a, one right S3, two right S6 and two right basal segmentectomies. The median time consumption for the automated reconstruction was 280 (205–324) s. Accurate vessel and bronchial detection were achieved in 85% by the AI approach and 80% by Mimics, p = 1.00. The accuracy of vessel classification was 80 and 95% by AI and manual approaches, respectively, p = 0.34. In real‐world application, the accuracy of the identification of anatomical variant by thoracic surgeons was 85% by AI+CT, and the median time consumption was 2 (1–3) min.

Conclusions

The AI reconstruction algorithm overcame defects of traditional methods and is valuable in surgical planning for segmentectomy. With the AI reconstruction, surgeons may achieve high identification accuracy of anatomical patterns in a short time frame.

Modified thoracoscopic wedge resection of limited peripheral lesions in S10 for children with congenital pulmonary airway malformation: Initial single-center experience

OBJECTIVE

The present study aimed to evaluate the safety and feasibility of modified thoracoscopic wedge resection of limited peripheral lesions in the posterior basal segment (S10) in children with congenital pulmonary airway malformation (CPAM).

MATERIALS AND METHODS

We retrospectively analyzed the clinical data of children with CPAM who underwent thoracoscopic modified wedge resection at our institution from November 2020 to February 2022. The surgical method was as follows: we marked the external boundary of the lesion with an electric hook, dissected and retained the segmental vein between the lesion and normal lung tissue as the internal boundary, cut the arteries, veins, and bronchus entering the lesion, and cut and sealed the lung tissue between the internal and external boundaries with LigaSure™ to complete the modified wedge resection.

RESULTS

A total of 16 patients were included, aged 3.8-70.0 months and weighing 6.5-21.0 kg. The intraoperative course was uneventful in all patients. The median operation time and intraoperative bleeding volume were 74 min (50-110 min) and 5 mL (5-15 mL), respectively. The median postoperative drainage tube indwelling time was 3 days (2-4 days), and the median postoperative hospital stay was 6 days (4-8 days). Pathological diagnosis included two cases of type 1, 10 cases of type 2, and four cases of type 3 CPAM. There were no cases of intraoperative conversion, surgical mortality, or major complications. However, subcutaneous emphysema occurred in two children, which spontaneously resolved without pneumothorax orbronchopleural fistula development. All patients were followed up for a median period of 10 months (3-18 months), and there were no cases of hemoptysis or residual lesions on chest computed tomography.

CONCLUSION

Modified thoracoscopic wedge resection via the inferior pulmonary ligament approach is safe and feasible for children with CPAM with limited peripheral lesions in S10.

Utility of preoperative three-dimensional CT bronchography and angiography in uniportal video-assisted thoracoscopic anatomical lobectomy: a retrospective propensity score-matched analysis

Background

Personalized three-dimensional (3D) reconstruction can help surgeons to overcome technical challenges and variations of pulmonary anatomic structures in the performance of uniportal video-assisted thoracoscopic surgery (UVATS), thus improving the safety and efficacy of the procedure. This study aims to evaluate the utility of preoperative 3D-CT bronchography and angiography (3D-CTBA) with Exoview software in the assessment of anatomical variations of pulmonary vessels, and to analyze short-term surgical outcomes in patients undergoing UVATS lobectomy.

Methods

We retrospectively analyzed the data of 198 consecutive patients who underwent curative UVATS lobectomy between November 2019 and September 2020. The patients were divided into an “Exoview” group (n=53) and a “non-Exoview” group (n=145). We performed 1:1 propensity score matching and compared intraoperative and postoperative outcomes between the two groups. A subgroup analysis of 74 patients who underwent single-direction uniportal lobectomy was also conducted. Aberrant pulmonary vessel patterns related to the surgery were also examined.

Results

The operative time in the Exoview group was significantly shorter than that in the non-Exoview group, both before (145.7±33.9 vs. 159.5±41.6 minutes, P=0.032) and after (145.7±33.9 vs. 164.2±41.8 minutes, P=0.014) propensity score matching. The number of mediastinal lymph nodes dissected was higher in the Exoview group than in the non-Exoview group (8.19±6.89 vs. 5.78±3.3, P=0.024) after propensity score matching. Intraoperative blood loss showed a statistical difference between the Exoview and non-Exoview groups (60.4±45.4 vs. 100.8±83.9, P=0.009). Four types of arterial variations and 2 types of venous variations related to the surgery were observed among 8 patients (15%), which have rarely been reported before.

Conclusions

Personalized preoperative 3D-CT bronchography and angiography helped to clearly visualize the pulmonary anatomical structures and could contribute to the safe and efficient performance of UVATS anatomical lobectomy.