The Multifaceted Role of Confocal Laser Endomicroscopy in Head and Neck Surgery: Oncologic and Functional Insights

Differentiating Sinonasal Tumor Entities with Fluorescein-Enhanced Confocal Laser Endomicroscopy: A Step Forward in Precision Diagnostics

Background/Objectives: Sinonasal malignancies are rare and highly diverse cancers that pose significant diagnostic challenges due to their variable histological features and complex anatomical locations. Accurate diagnosis is critical for guiding treatment, yet conventional methods often require multiple biopsies. This study aimed to evaluate the potential of confocal laser endomicroscopy (CLE) for real-time imaging of sinonasal tumors to characterize specific features of different entities and improve diagnostic precision. Methods: Ten patients with various sinonasal malignancies, including squamous cell carcinoma, adenocarcinoma, sinonasal undifferentiated carcinoma, olfactory neuroblastoma, sinonasal mucosal melanoma, and endonasal lymphoma, were examined using CLE during diagnostic endoscopy. CLE images were compared descriptively with histopathological cross-sections to identify unique imaging patterns for each tumor type. Results: CLE was feasible across all cases, with high-quality images obtained despite anatomical challenges in some cases. Characteristic features, such as vascular clusters in undifferentiated carcinoma, mucin-filled bubbles in adenocarcinoma, and small round cells in neuroblastoma, were identified and corresponded well with histopathological findings. CLE also helped guide biopsies by revealing areas with diagnostic relevance. Conclusions: CLE demonstrates promise as an adjunct diagnostic tool in sinonasal malignancies, offering real-time imaging that correlates with histopathological findings and aids in targeted biopsies. While this study provides preliminary insights into the utility of CLE, further research with larger cohorts and statistical validation is necessary to establish its diagnostic reliability and broader clinical application.

Feasibility of Optical Biopsy During Endoscopic Sinus Surgery With Confocal Laser Endomicroscopy: A Pilot Study

OBJECTIVE

Confocal laser endomicroscopy (CLE) is an optical imaging technique that allows in vivo, real-time, microscope-like assessment of superficial lesions. Although there is substantial data on CLE use in the upper GI tract, there is limited information regarding its application in the nasal cavity and paranasal sinuses. This study aims to assess the feasibility and diagnostic metrics of CLE in the nasal cavity and paranasal sinuses regarding differentiation between healthy/benign and malignant tissue. These structures show, however, a wider variety of frequent and concomitant benign and malignant pathologies, which could pose an increased challenge for optical biopsy by CLE.

METHODS

We performed CLE on a case series of six patients with various findings in the nose (three chronic rhinosinusitis, adenocarcinoma, meningoenzephalozele, esthesionneuroblastoma). Forty-two sequences (3792 images) from various structures in the nasal cavity and/or paranasal sinuses were acquired. Biopsies were taken at corresponding locations and analyzed in hematoxylin and eosin staining as a standard of reference. Three independent examiners blinded to the histopathology assessed the sequences.

RESULTS

Healthy and inflamed mucosa could be distinguished from malignant lesions with an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 84.1%, 85.4%, 83.1%, 72.5%, and 92.1%, respectively, with a substantial agreement between raters (Fleiss κ = 0.62).

CONCLUSION

This technique shows, despite its limitations, potential as an adjunctive imaging technique during sinus surgery; however, the creation of a scoring system based on reproducible and defined characteristics in a larger more diverse population should be the focus of further research to improve its diagnostic value and clinical utility.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:4217-4224, 2024.

Confocal Laser Endomicroscopy in Resection of Sinonasal Malignant Melanoma-Preliminary Report on Real-Time Margin Assessment and Support in Surgical Decision-Making

Background/Objectives: Building upon the rising value of Confocal Laser Endomicroscopy (CLE) in squamous cell carcinoma of the head and neck, we present the first application of CLE during the resection of sinonasal malignant melanomas. This study aims to evaluate the potential of CLE to assist surgeons in intraoperative decision-making, with a particular focus on resection margin assessment within the constrained nasal cavity. Methods: Two cases of sinonasal malignant melanoma were included in this study. CLE was employed to examine visible tumors and their margins, both pre- and post-endoscopic resection. The findings were compared to histopathological results as well as data on squamous cell carcinoma, for which malignancy criteria had already been established in prior projects. Results: CLE provided the real-time visualization of sinonasal malignant melanomas and their margins, successfully differentiating between healthy and neoplastic tissue compared to histopathological findings. Conclusion: CLE offers the potential for real-time assessment, aiding surgeons in more precise tumor resection and potentially improving patient outcomes. This study demonstrates the feasibility of using CLE in the resection of sinonasal malignant melanoma, highlighting its ability to differentiate between healthy and neoplastic tissue intraoperatively.

Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision

PURPOSE

Confocal Laser Endomicroscopy (CLE) is an imaging tool, that has demonstrated potential for intraoperative, real-time, non-invasive, microscopical assessment of surgical margins of oropharyngeal squamous cell carcinoma (OPSCC). However, interpreting CLE images remains challenging. This study investigates the application of OpenAI's Generative Pretrained Transformer (GPT) 4.0 with Vision capabilities for automated classification of CLE images in OPSCC.

METHODS

CLE Images of histological confirmed SCC or healthy mucosa from a database of 12 809 CLE images from 5 patients with OPSCC were retrieved and anonymized. Using a training data set of 16 images, a validation set of 139 images, comprising SCC (83 images, 59.7%) and healthy normal mucosa (56 images, 40.3%) was classified using the application programming interface (API) of GPT4.0. The same set of images was also classified by CLE experts (two surgeons and one pathologist), who were blinded to the histology. Diagnostic metrics, the reliability of GPT and inter-rater reliability were assessed.

RESULTS

Overall accuracy of the GPT model was 71.2%, the intra-rater agreement was κ = 0.837, indicating an almost perfect agreement across the three runs of GPT-generated results. Human experts achieved an accuracy of 88.5% with a substantial level of agreement (κ = 0.773).

CONCLUSIONS

Though limited to a specific clinical framework, patient and image set, this study sheds light on some previously unexplored diagnostic capabilities of large language models using few-shot prompting. It suggests the model`s ability to extrapolate information and classify CLE images with minimal example data. Whether future versions of the model can achieve clinically relevant diagnostic accuracy, especially in uncurated data sets, remains to be investigated.