Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging.

(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.

Interrater variation of vascular classifications used in enhanced laryngeal contact endoscopy.

PURPOSE: Combined use of contact endoscopy (CE) and Narrow Band Imaging (NBI, Olympus®) is suggested for the visualization of specific vascular changes indicative of glottic neoplasia. We investigated the interrater reliability and agreement in 3 recognized classification systems of vascular changes applied to images from CE + NBI in patients suspected for glottic neoplasia. METHODS: Six experienced head and neck surgeons familiar with NBI rated 120 images obtained by CE + NBI by 3 classification systems of vascular changes as suggested by Ni et al. (N-C), Puxeddu et al. (P-C), and the European Laryngological Society (ELS-C). Three raters were experienced in CE, and three raters had only limited experience with CE. Crude agreement and Fleiss' kappa with 95% confidence interval were estimated for all 6 raters, and for the 2 levels of expertise for each original classification system and for dichotomized versions of the N-C and the P-C based on suggested neoplastic potential. RESULTS: The interrater crude agreement and the corresponding kappa values for the ELS-C were good and significantly higher than those for the N-C and P-C for all raters, irrespective of the level of experience with CE (p < 0.0001). There were no significant differences between the N-C and the P-C (p = 0.16). Kappa was considerably improved for both the N-C and the P-C to a level not different from the ELS-C (p = 0.21-0.71) when their 5 original categories were pooled into dichotomized classifications. CONCLUSION: Difficulties in reliably classifying vascular changes in CE + NBI are evident. Two-tier classification systems are the most reliable.

Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach.

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.

Flexible transnasal endoscopy with white light or narrow band imaging for the diagnosis of laryngeal malignancy: diagnostic value, observer variability and influence of previous laryngeal surgery.

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.

[Endoscopic early diagnosis of carcinomas in upper respiratory and digestive tract]. / Endoskopische Frühdiagnostik von Karzinomen im oberen Aerodigestivtrakt.

Modern endoscopic imaging techniques make it possible to detect tumor diseases of the upper respiratory and digestive tract and treat them minimally invasive - with a good oncologic outcome and maintaining the functionality of the tissue.Horizontal techniques permit the inspection of big mucous membrane surfaces, searching vor areas suspicious of dysplasia or tumor. They can be used as screening techniques. Vertical techniques serve for precise examination of in-depth expansion, infiltration chraracteristics and dignity of known lesions. Cellular techniques deal with the detection of cellular changes in vivo. As the techniques have different advantages and disandvantages, it is recommended to combine several techniques for best diagnostic gains.

The Molecular Basis of Olfactory Dysfunction in COVID-19 and Long COVID.

Olfactory dysfunction (OD) is not uncommon following viral infection. Herein, we explore the interplay of host genetics with viral correlates in coronavirus disease 2019 (COVID-19)- and long COVID-related OD, and its diagnosis and treatment that remain challenging. Two genes associated with olfaction, UGT2A1 and UGT2A2, appear to be involved in COVID-19-related anosmia, a hallmark symptom of acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), particularly in the early stages of the pandemic. SARS-CoV-2 infects olfactory support cells, sustentacular and Bowman gland cells, that surround olfactory sensory neurons (OSNs) in the olfactory epithelium (OE) where the initial step of odor detection takes place. Anosmia primarily arises from the infection of support cells of the OE, followed by the deciliation and disruption of OE integrity, typically without OSN infection. Through the projected axons of OSNs, the virus could theoretically reach the olfactory bulb and brain, but current evidence points against this route. Intriguingly, SARS-CoV-2 infection of support cells leads to profound alterations in the nuclear architecture of OSNs, leading to the downregulation of odorant receptor-related genes, e.g., of Adcy3. Viral factors associated with the development of OD include spike protein aminoacidic changes, e.g., D614G, the first substitution that was selected early during SARS-CoV-2 evolution. More recent variants of the Omicron family are less likely to cause OD compared to Delta or Alpha, although OD has been associated with a milder disease course. OD is one of the most prevalent post-acute neurologic symptoms of SARS-CoV-2 infection. The tens of millions of people worldwide who have lingering problems with OD wait eagerly for effective new treatments that will restore their sense of smell which adds value to their quality of life.

Establishment of a Human Immunocompetent 3D Tissue Model to Enable the Long-Term Examination of Biofilm-Tissue Interactions.

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.

Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.

The endoscopic examination of subepithelial vascular patterns within the vocal fold is crucial for clinicians seeking to distinguish between benign lesions and laryngeal cancer. Among innovative techniques, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) offers real-time visualization of these vascular structures. Despite the advent of CE-NBI, concerns have arisen regarding the subjective interpretation of its images. As a result, several computer-based solutions have been developed to address this issue. This study introduces the CE-NBI data set, the first publicly accessible data set that features enhanced and magnified visualizations of subepithelial blood vessels within the vocal fold. This data set encompasses 11144 images from 210 adult patients with pathological vocal fold conditions, where CE-NBI images are annotated using three distinct label categories. The data set has proven invaluable for numerous clinical assessments geared toward diagnosing laryngeal cancer using Optical Biopsy. Furthermore, given its versatility for various image analysis tasks, we have devised and implemented diverse image classification scenarios using Machine Learning (ML) approaches to address critical clinical challenges in assessing laryngeal lesions.

Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy-Narrow Band Imaging.

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.

Evaluation of Vascular Patterns Using Contact Endoscopy and Narrow-Band Imaging (CE-NBI) for the Diagnosis of Vocal Fold Malignancy.

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.

A Preliminary Study on Automatic Characterization and Classification of Vascular Patterns of Contact Endoscopy Images.

The structure and organization of blood vessels in the vocal fold go through changes during the advancement from healthy to benign and further on to malignant stages. Contact Endoscopy (CE) is an optical technique providing real-time information related to the vascular structure of laryngeal mucosa. However, this technique comes with subjectivity in the interpretation of vascular patterns. In this study, a novel automated approach is proposed for vessel pattern charac-terization and classification of larynx CE + Narrow Band Imaging (NBI) images. This method is mainly based on the computation of indicators related to the level of disorder of vessels. 12 features were extracted from the indicators and were fed into two supervised classifiers. Linear Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) showed an accuracy of 95.76% and 93.92% for vascular patterns and 86.04% and 82.23% for larynx histopathologies classification, respectively. These promising results show that the proposed method can potentially solve the subjectivity issues of CE.

Novel automated vessel pattern characterization of larynx contact endoscopic video images.

PURPOSE: Contact endoscopy (CE) is a minimally invasive procedure providing real-time information about the cellular and vascular structure of the superficial layer of laryngeal mucosa. This method can be combined with optical enhancement methods such as narrow band imaging (NBI). However, these techniques have some problems like subjective interpretation of vascular patterns and difficulty in differentiation between benign and malignant lesions. We propose a novel automated approach for vessel pattern characterization of larynx CE + NBI images in order to solve these problems. METHODS: In this approach, five indicators were computed to characterize the level of vessel's disorder based on evaluation of consistency of gradient and two-dimensional curvature analysis and then 24 features were extracted from these indicators. The method evaluated the ability of the extracted features to classify CE + NBI images based on the vascular pattern and based on the laryngeal lesions. Four datasets were generated from 32 patients involving 1485 images. The classification scenarios were implemented using four supervised classifiers. RESULTS: For classification of CE + NBI images based on the vascular pattern, polykernel support vector machine (SVM), SVM with radial basis function (RBF), k-nearest neighbor (kNN), and random forest (RF) show an accuracy of 97%, 96%, 96%, and 96%, respectively. For the classification based on the histopathology, Polykernel SVM showed an accuracy of 84%, 86% and 84%, RBF SVM showed an accuracy of 81%, 87% and 83%, kNN showed an accuracy of 89%, 87%, 91%, RF showed an accuracy of 90%, 88% and 91% for classification between benign histopathologies, between malignant histopathologies and between benign and malignant lesions, respectively. CONCLUSION: These promising results show that the proposed method could solve the problem of subjectivity in interpretation of vascular patterns and also support the clinicians in the early detection of benign, pre-malignant and malignant lesions.

Value of narrow band imaging in the early diagnosis of laryngeal cancer.

BACKGROUND: Narrow band imaging (NBI) is a new imaging technique for the depiction of tumor-specific neoangiogenesis. The purpose of the present study was to assess the value of NBI in the early diagnosis of laryngeal cancer. METHODS: This prospective study includes 205 consecutive patients with a laryngeal lesion scheduled for microlaryngoscopy. NBI was immediately performed after white light endoscopy (WLE), whereas excisional biopsy was carried out for histologic verification of diagnosis. RESULTS: In identifying laryngeal cancer and its precursor lesions, NBI with WLE showed a significantly higher sensitivity (97% vs 79%) and accuracy (97% vs 90%) than WLE alone, but the specificity (96% vs 95%) was essentially equal in both methods. CONCLUSION: NBI qualifies for rapid detection and delineation of suspicious lesions. Additionally, this noninvasive method is also beneficial in a variety of benign lesions.