THE USE OF ARTIFICIAL INTELLIGENCE FOR THE DEVELOPMENT AND VALIDATION OF A COMPUTER-AIDED ALGORITHM FOR THE SEGMENTATION OF LYMPH NODE FEATURES FROM THORACIC IMAGING

Abstract

Background- Mediastinal staging is the rate-limiting step prior to initiation of lung cancer treatment and is essential in identifying the most appropriate treatment for the patient. However, this process is often complex and involves multiple imaging modalities including invasive and non-invasive methods for the assessment of lymph nodes in the mediastinum which are error prone. The use of Artificial Intelligence may be able to provide more accurate and precise measurements and eliminate error associated with medical imaging. Methods-This thesis was conducted in three parts. In Part 1, we synthesized and critically appraised the methodological quality of existing studies that use Artificial Intelligence to diagnosis and stage lung cancer from thoracic imaging based on lymph node features. In Part 2, we determined the inter-rater reliability of segmentation of the ultrasonographic lymph node features performed by an experienced endoscopist (manually) compared to NeuralSeg (automatically). In Part 3, we developed and validated a deep neural network through a clinical prediction model to determine if NeuralSeg could learn and identify ultrasonographic lymph node features from endobronchial ultrasound images in patients undergoing lung cancer staging. Results- In Part 1, there were few studies in the Artificial Intelligence literature that provided a complete and detailed description of the design, Artificial Intelligence architecture, validation strategies and performance measures. In Part 2, NeuralSeg and the experienced endosonographer possessed excellent inter-rater correlation (Intraclass Correlation Coefficient = 0.76, 95% CI= 0.70 – 0.80, p<0.0001). In Part 3, NeuralSeg’s algorithm had an accuracy of 73.78% (95% CI: 68.40% to 78.68%), a sensitivity of 18.37% (95% CI: 8.76% to 32.02%) and specificity of 84.34% (95% CI: 79.22% to 88.62%). Conclusions- Analysis of staging modalities for lung cancer using Artificial Intelligence may be useful for when results are inconclusive or uninterpretable by a human reader. NeuralSeg’s high specificity may inform decision-making regarding biopsy if results are benign. Prospective external validation of algorithms and direct comparisons through cut-off thresholds are required to determine their true predictive capability. Future work with a larger dataset will be required to improve and refine the algorithm prior to trials in clinical practice.