Bronchoscopic confocal laser endomicroscopy for lung cancer diagnosis Shining light on advanced needle techniques
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| Award date | 29-02-2024 |
| Number of pages | 235 |
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| Abstract |
Lung cancer is a global health concern and leading cause of cancer-related deaths. Diagnostic procedures such as bronchoscopy and endosonography regulary yield insufficient tissue due to inadequate needle positioning or sampling errors. The first part of the thesis focused on an emerging imaging technique called needle based confocal laser endomicroscopy (nCLE) to overcome these problems. nCLE is a laser-based imaging technique, providing high-resolution real-time images at the biopsy needle tip in-vivo. Bronchoscopic nCLE-imaging of peripheral lung nodules suspected of lung cancer proved to be feasible, safe and allowed immediate tumor cell visualization. Additionally, by validation of nCLE criteria for surrounding airway and lung parenchyma, nCLE-imaging during (robotic-)bronchoscopic procedures allowed optimization of the needle positioning. Furthermore, nCLE criteria for granulomas were identified and validated in sarcoid mediastinal lymph nodes and lung nodules. Whether the addition of nCLE-imaging to bronchoscopic procedures will result in an improved diagnostic yield needs further exploration. Future integration with artificial intelligence and fluorescent tracers holds promise for improved diagnostic efficiency and potential therapeutic applications. The second part of the thesis focused on endobronchial ultrasound in diagnosing lung cancer, detailing a randomized trial comparing 22G Acquire and Expect biopsy needles for PD-L1 suitability. Despite superior quality in samples obtained with the Acquire needle, no statistically significant difference in the PD-L1 suitability rate between both arms was found. In conclusion, nCLE as a ‘smart needle’ technique has the potential to improve the diagnostic yield of bronchoscopic peripheral lung nodule analysis but further studies are needed before clinical implementation.
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| Document type | PhD thesis |
| Language | English |
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