Background
Lung cancer is the leading cause of cancer deaths worldwide. The increasing use of minimally invasive surgical approaches has expanded the scope of treatment options for patients with lung cancer. However, these operations are not risk-free as there have been several reported cases of intraoperative catastrophes, with major hemorrhage from the pulmonary artery being the most common. The objective of this study was to develop a computer vision-based approach to reliably identify the pulmonary artery during robotic right lower lobectomies. 

Methods
Twelve patients with biopsy-proven Stage I non-small-cell lung cancer of the right lower lobe underwent robotic lobectomy with mediastinal lymph node dissection. Complete videos of each operation were obtained, and video fragments of the pulmonary artery were identified by two board-certified thoracic surgeons. Annotation masks of the pulmonary artery were then created using Computer Vision Annotation Tool, an open-source web-based annotation tool. The labeled data was next used to train a state-of-the-art instance segmentation algorithm called Mask Regional-Convolutional Neural Network (Mask R-CNN) using an 80:20 random sample split from ten cases for training and validation, respectively. Two cases were utilized for generalization testing. Three custom performance metrics commonly utilized in deep learning-based instance segmentation were used to evaluate our approach: Intersection over Union (IoU), Average Precision at IoU threshold of 50% (AP50) and at 75% (AP75). 

Results
Annotation masks of the pulmonary artery were created in 4,139 images across twelve cases of robotic right lower lobectomy. 2,893 and 684 annotated images across ten (83.3%) cases were used for training and validation, respectively; and 261 and 301 annotated images from two (16.7%) cases were used for generalization testing. A mean IoU of 87.7% was achieved in the validation dataset. The AP50 attained by our model was 96.7% and AP75 was 82.3%. In generalizability testing, when the model was tested on data never exposed to it, it was able to partially generalize with a mean IoU of 86.0%. 

Conclusion
Our study shows that our Mask R-CNN model was able to identify surgical anatomy during robotic lobectomy with high accuracy in the validation dataset. While still at an early stage, more variable labeled data and collaborative efforts are needed to improve the generalizability of our model. We envision such a computer vision system to be valuable in early surgeon training and incorporate future real-time augmentation of video feedback to improve outcomes.






Using Artificial Intelligence for Automatic Detection of the Pulmonary Artery During Robotic Right Lower Lobectomies

Background: Despite the significant advances in detecting pulmonary nodules, the management plan of small tumors with malignant potential remains controversial. The objective of this study was to evaluate the overall survival of patients with ≤8 mm non-small cell lung cancer (NSCLC) who undergo stereotactic body radiation therapy (SBRT) versus wedge resection.

Methods: Kaplan-Meier analysis, multivariable Cox proportional hazards modeling, and propensity score-matched analysis were performed to evaluate the overall survival of patients with ≤8 mm NSCLC in the National Cancer Database (NCDB) from 2004-2017 who underwent SBRT versus patients who underwent wedge resection. The above-mentioned matched analyses were repeated for patients with no comorbidities. Patients who were coded in the NCDB as having undergone radiation because surgery was contraindicated due to patient risk factors (e.g., comorbid conditions, advance age, etc.) and those with a history of prior malignancy were excluded from analysis.

Results: Of the 1,505 patients who had NSCLC ≤8 mm during the study period, 1,339 (89%) patients underwent wedge resection, and 166 (11%) patients underwent SBRT. In both unadjusted analysis and in multivariable Cox modeling and propensity score-matched analysis, wedge resection was associated with improved survival when compared to SBRT. These results were consistent in a sensitivity analysis limited to patients with no comorbidities.

Conclusion: In this national analysis, patients with NSCLC ≤8 mm undergoing wedge resection experienced improved survival when compared to SBRT. This finding can be used to help inform the treatment decision-making process for tumors that are being discovered incidentally or through lung cancer screening. 





Stereotactic Body Radiation Therapy Versus Wedge Resection for Early-Stage Node-Negative Non-Small Cell Lung Cancer Tumors ≤8 mm: A National Analysis

 Thoracic Surgery

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Arian Mansur

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Using Artificial Intelligence for Automatic Detection of the Pulmonary Artery During Robotic Right Lower Lobectomies

Stereotactic Body Radiation Therapy Versus Wedge Resection for Early-Stage Node-Negative Non-Small Cell Lung Cancer Tumors ≤8 mm: A National Analysis

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