Background: Before making any treatment decisions for patients with non-small cell lung cancer (NSCLC), it is crucial to determine whether the cancer has spread to the mediastinal lymph nodes (LNs). The preferred method for mediastinal staging is Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA). However, EBUS-TBNA has been reported to generate inconclusive results as much as 40% of the time. Since this jeopardizes good patient care, there is near-universal consensus on the need to develop and study a novel method for LN staging. Recent research has shown that AI and deep learning are used to accurately interpret images with comparisons to clinicians in radiology, pathology, and cardiology. Additionally, EBUS-Elastography is a novel modality which could be used as an adjunct to EBUS-TBNA for LN staging. This technology uses impedance ultrasonography to measure tissue stiffness.
Methods: There are three parts to this thesis. The first part involved the training, validating, and testing NeuralSeg, a deep neural network, to predict LN malignancy based on B-mode EBUS-TBNA images. The second part of this thesis involves EBUS-Elastography, defining the blue colour threshold and the optimal SAR cut-off value based on the blue threshold that most accurately distinguished benign and malignant LN. Finally, this thesis's third part involves validating part II's findings.
Results: Part I resulted in an overall accuracy of 80.63% (76.93% to 83.97%), a sensitivity of 43.23% (35.30% to 51.41%), a specificity of 96.91% (94.54% to 98.45%), a positive predictive value of 85.90% (76.81% to 91.80%), a negative predictive value of 79.68% (77.34% to 81.83%), and an AUC of 0.701 (0.646 to 0.755). Part II Level 60 was chosen as the blue threshold with an AUC of 0.89 (95% CI: 0.77-1.00), and the optimal SAR cut off was found to be 0.4959 with a sensitivity of 92.30% (95% CI: 62.10% to 99.60%) and a specificity of 76.50% (95% CI: 49.80% to 92.20%). Using the blue threshold and SAR cut-off, the results of part III resulted in an overall accuracy of 70.59% (95% (CI) 63.50% to 77.01%), the sensitivity of 43.04% (CI: 31.94% to 54.67%), and a specificity of 90.74% (CI: 83.63% to 95.47%).
Conclusion: It was observed that both AI and AI-powered EBUS-Elastography achieved high specificities on larger sample sizes, indicative that these methods may be helpful in identifying LN malignancy. However, due to the novelty of these technologies, more extensive multi-centre studies must be conducted before these processes can be standardized. / Thesis / Master of Health Sciences (MSc) / Non-Small Cell Lung Cancer (NSCLC) treatment decisions are made using vital information by performing biopsies to collect tissue from the lymph nodes near the lungs. The current method is called Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA), which involves a scope with a fine needle attached to it. This scope is led down the airway and guided by ultrasound to obtain the tissue needed to determine whether the lymph nodes have cancerous tissue. If the lymph nodes contain cancerous tissue, the patient may need chemotherapy; however, lung surgery may be the best treatment option if they do not. Many factors impact how successfully these tissue samples can be obtained, such as the skill and experience of the surgeon. These factors often lead to inconclusive results, making it difficult to make correct treatment decisions. Novel technologies such as Artificial Intelligence and Elastography are being used to diagnose lung cancer by interpreting images and providing information on tissue stiffness. We trained an Artificial Intelligence program to predict malignancy based on EBUS-TBNA images. Additionally, we trained the AI program to analyze Elastography images to aid us in understanding the relationship between the colour pattern of the elastography images and cancerous tissue. This thesis assesses how these novel technologies contribute to lung cancer diagnosis.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27709 |
| Date | January 2022 |
| Creators | Mistry, Nikkita |
| Contributors | Hanna, Wael, Clinical Health Sciences (Health Research Methodology) |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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