(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of $0.835$ on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.

Purpose Flexible transnasal endoscopy is a common examination technique for the evaluation of laryngeal lesions, while the use of narrow band imaging (NBI) has been reported to enhance the diagnostic value of white light endoscopy (WLE). The purpose of this study is to assess observer variability and diagnostic value of both modalities and investigate the possible influence of previous laryngeal surgery on the detection rates of laryngeal malignancy. Methods The study was based on the retrospective evaluation of 170 WLE and NBI images of laryngeal lesions by three observers in a random order. The histopathological diagnoses serve as the gold standard. Results In identifying laryngeal malignancy, the sensitivity of NBI proved to be higher than that of WLE (93.3% vs. 77.0%). NBI was also superior to WLE in terms of accuracy (96.3% vs. 92%) and diagnostic odds ratio (501.83 vs. 120.65). Both modalities had a specificity of 97.3%. The inter-observer agreement was substantial (kappa = 0.661) for WLE and almost perfect (kappa = 0.849) for NBI. Both WLE and NBI showed a high level of intra-observer agreement. The sensitivity was significantly lower in images with history of previous laryngeal surgery compared to those without. Conclusions Flexible transnasal endoscopy has been proved to be a valuable tool in the diagnosis of laryngeal malignancy. The use of NBI can increase the sensitivity and observer reliability in that context and can also provide a diagnostic gain in cases with previous laryngeal surgery

The endoscopic detection of perpendicular vascular changes (PVC) of the vocal folds has been associated with vocal fold cancer, dysplastic lesions, and papillomatosis, according to a classification proposed by the European Laryngological Society (ELS). The combination of contact endoscopy with narrow-band imaging (NBI-CE) allows intraoperatively a highly contrasted, real-time visualization of vascular changes of the vocal folds. Aim of the present study was to determine the association of PVC to specific histological diagnoses, the level of interobserver agreement in the detection of PVC, and their diagnostic effectiveness in diagnosing laryngeal malignancy. The evaluation of our data confirmed the association of PVC to vocal fold cancer, dysplastic lesions, and papillomatosis. The level of agreement between the observers in the identification of PVC was moderate for the less-experienced observers and almost perfect for the experienced observers. The identification of PVC during NBI-CE proved to be a valuable indicator for diagnosing malignant and premalignant lesions.

Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach’s subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach’s misclassification issue.

Background: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)—Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. Methods: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). Results: The CyEfF features showed maximum classification accuracy of $0.882$ and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. Conclusion: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.

Different studies suggest an impact of biofilms on carcinogenic lesion formation in varying human tissues. However, the mechanisms of cancer formation are difficult to examine in vivo as well as in vitro. Cell culture approaches, in most cases, are unable to keep a bacterial steady state without any overgrowth. In our approach, we aimed to develop an immunocompetent 3D tissue model which can mitigate bacterial outgrowth. We established a three-dimensional (3D) co-culture of human primary fibroblasts with pre-differentiated THP-1-derived macrophages on an SIS-muc scaffold which was derived by decellularisation of a porcine intestine. After establishment, we exposed the tissue models to define the biofilms of the Pseudomonas spec. and Staphylococcus spec. cultivated on implant mesh material. After 3 days of incubation, the cell culture medium in models with M0 and M2 pre-differentiated macrophages presented a noticeable turbidity, while models with M1 macrophages presented no noticeable bacterial growth. These results were validated by optical density measurements and a streak test. Immunohistology and immunofluorescent staining of the tissue presented a positive impact of the M1 macrophages on the structural integrity of the tissue model. Furthermore, multiplex ELISA highlighted the increased release of inflammatory cytokines for all the three model types, suggesting the immunocompetence of the developed model. Overall, in this proof-of-principle study, we were able to mitigate bacterial overgrowth and prepared a first step for the development of more complex 3D tissue models to understand the impact of biofilms on carcinogenic lesion formation.