Search Results (44)

Recurrent laryngeal nerve palsy remains a significant complication following minimally invasive esophagectomy for esophageal cancer. Despite advancements in surgical techniques and lymphadenectomy precision, the incidence of recurrent laryngeal nerve palsy has not been improved. Recurrent laryngeal nerve palsy predominantly affects the left side and may lead to unilateral or bilateral vocal cord paralysis, resulting in hoarseness, dysphagia, and an increased risk of aspiration pneumonia. While most cases of recurrent laryngeal nerve palsy are temporary and resolve within 6 to 12 months, some patients may experience permanent nerve dysfunction, severely impacting their quality of life. Prevention strategies, such as nerve integrity monitoring, robotic-assisted minimally invasive esophagectomy, and advanced dissection techniques, aim to minimize nerve injury, though their effectiveness varies. The management of recurrent laryngeal nerve palsy includes voice and swallowing rehabilitation, reinnervation techniques, and, in severe cases, surgical interventions such as thyroplasty and intracordal injection. As recurrent laryngeal nerve palsy can lead to significant postoperative respiratory complications, a multidisciplinary approach involving surgical precision, early detection, and comprehensive rehabilitation is crucial to improving patient outcomes and minimizing long-term morbidity in minimally invasive esophagectomy. This review article aims to inform esophageal surgeons and other clinicians about strategies for the prevention and management of recurrent laryngeal nerve palsy in esophagectomy. Full article

This study modifies the U-Net architecture for pixel-based segmentation to automatically classify lesions in laryngeal endoscopic images. The advanced U-Net incorporates five-level encoders and decoders, with an autoencoder layer to derive latent vectors representing the image characteristics. To enhance performance, a WGAN was implemented to address common issues such as mode collapse and gradient explosion found in traditional GANs. The dataset consisted of 8171 images labeled with polygons in seven colors. Evaluation metrics, including the F1 score and intersection over union, revealed that benign tumors were detected with lower accuracy compared to other lesions, while cancers achieved notably high accuracy. The model demonstrated an overall accuracy rate of 99%. This enhanced U-Net model shows strong potential in improving cancer detection, reducing diagnostic errors, and enhancing early diagnosis in medical applications. Full article

Background: Narrow-band imaging is an advanced endoscopic technology used to detect changes on the laryngeal tissue surface, employing a comparative approach alongside white-light endoscopy to facilitate histopathological examination. Objective: This study aimed to assess the utility and advantages of NBI (narrow-band imaging) in identifying malignant laryngeal lesions through a comparative analysis with histopathological examination. Methods: We conducted a systematic literature review, utilizing databases such as PubMed, the CNKI database, and Embase for our research. Results: We analyzed the articles by reviewing their titles and abstracts, selecting those we considered relevant based on determined criteria; in the final phase, we examined the relevant studies according to the specific eligibility criteria. Conclusions: Narrow-band imaging is an advanced endoscopic technology that demonstrates its efficacy as a tool for diagnosing malignant laryngeal lesions and comparing them to premalignant lesions. The European Society of Laryngology has implemented a standardized classification system for laryngeal lesions to enhance data correlation and organization. Full article

The PIK3CA and SOX2 genes map at 3q26, a chromosomal region frequently amplified in head and neck cancers, which is associated with poor prognosis. This study explores the clinical significance of PIK3CA and SOX2 gene amplification in early tumorigenesis. Gene copy number was analyzed by real-time PCR in 62 laryngeal precancerous lesions and correlated with histopathological grading and laryngeal cancer risk. Amplification of the SOX2 and PIK3CA genes was frequently detected in 19 (31%) and 32 (52%) laryngeal dysplasias, respectively, and co-amplification in 18 (29%) cases. The PIK3CA and SOX2 amplifications were predominant in high-grade dysplasias and significantly associated with laryngeal cancer risk beyond histological criteria. Multivariable Cox analysis further revealed PIK3CA gene amplification as an independent predictor of laryngeal cancer development. Interestingly, combined PIK3CA and SOX2 amplification allowed us to distinguish three cancer risk subgroups, and PIK3CA and SOX2 co-amplification was found the strongest predictor by ROC analysis. Our data demonstrate the clinical relevance of PIK3CA and SOX2 amplification in early laryngeal tumorigenesis. Remarkably, PIK3CA amplification was found to be an independent cancer predictor. Furthermore, combined PIK3CA and SOX2 amplification is emerging as a valuable and easy-to-implement tool for cancer risk assessment in patients with laryngeal precancerous lesions beyond current WHO histological grading. Full article

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values. Full article

Objectives. The aim of this prospective study was to investigate the role of procalcitonin as an early diagnostic marker of pharyngocutaneous fistula (PCF) in a cohort of head and neck patients treated with total laryngectomy for squamous cell carcinoma. Methods. This prospective study was conducted on a sample of patients enrolled from January 2019 to March 2022. All patients were subjected to a “protocol” of blood chemistry investigations, scheduled as follows: complete blood count with formula, ESR dosage, CPR, and PCT. PCT was also dosed by salivary sampling and a pharyngo-cutaneous swab in patients who presented with PCF. The dosage scheme was systematically repeated: the day before the intervention (t0); the 5th day postoperative (t1); the 20th day postoperative (t2); and at time X, the day of the eventual appearance of the pharyngocutaneous fistula. Results. A total of 36 patients met the inclusion criteria. The patients enrolled in the study were subsequently divided into two groups: 27 patients underwent total laryngectomy (TL) for laryngeal cancer without postoperative complications, and 9 patients were undergoing TL with postoperative PCF. Using the Cochran’s Q test, statistical significance was found for PCT among T0, T1, Tx, and T2 (p-value < 0.001) between the PCF and non-PCF groups. The Z test demonstrated that there is a difference in PCT levels at T1 and T2 and that this difference is statistically significant (p < 0.001). Conclusions. PCT could be considered an early marker of complications in open laryngeal surgery. According to our results, it could be useful in the precocious detection of pharyngocutaneous fistulas and in the management of antibiotic therapy. Full article

Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures. Full article

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201’s performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model. Full article

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios. Full article

Laryngeal squamous cell carcinoma (LSCC) is the second most common cancer among head and neck cancers. Despite a lower incidence of laryngeal carcinoma, new diagnostic techniques, and more targeted therapies, the overall survival has not changed significantly in the last decades, leading to a negative prognosis in advanced stages. Recently, several studies have focused on the identification of biomarkers that may play a critical role in the pathogenesis of LSCC. Reviewing the literature on the main databases, this study aims to investigate the role of some biomarkers in LSCC that are correlated with oxidative stress and inflammation: heat shock proteins; metallothioneins; nuclear factor erythroid 2-related factor 2; heme oxygenase; cyclooxygenase-2; and micro ribonucleic acids. This review shows that biomarker expression depends on the type, grade of differentiation, stage, and site of carcinoma. In addition, the role of these biomarkers in LSCC is still little-known and little-studied. However, the study of biomarker expression and the detection of a possible correlation with patients’ epidemiological, clinicopathological, and therapeutics data may lead to better awareness and knowledge of the tumor, to the identification of the best therapeutic strategy, and the most proper follow-up protocol tailored for each patient. In conclusion, the achievement of these goals may improve the prognosis of LSCC patients. Full article

Radiotherapy (RT) is an important modality for laryngeal cancer treatment to preserve laryngeal function. During beam delivery, laryngeal motion remains uncontrollable and may compromise tumor-targeting efficacy. We aimed to examine real-time laryngeal motion by developing a surface depth-sensing technique with preliminary testing during RT-based treatment of patients with laryngeal cancer. A surface depth-sensing (SDS) camera was set up and integrated into RT simulation procedures. By recording the natural swallowing of patients, SDS calculation was performed using the Pose Estimation Model and deep neural network technique. Seven male patients with laryngeal cancer were enrolled in this prospective study. The calculated motion distances of the laryngeal prominence (mean ± standard deviation) were 1.6 ± 0.8 mm, 21.4 ± 5.1 mm, 6.4 ± 3.3 mm, and 22.7 ± 4.9 mm in the left–right, cranio–caudal, and anterior–posterior directions and for the spatial displacement, respectively. The calculated differences in the 3D margins for generating the planning tumor volume by senior physicians with and without SDS data were −0.7 ± 1.0 mm (−18%), 11.3 ± 6.8 mm (235%), and 1.8 ± 2.6 mm (45%) in the left–right, cranio–caudal, and anterior–posterior directions, respectively. The SDS technique developed for detecting laryngeal motion during swallowing may be a practical guide for individualized RT design in the treatment of laryngeal cancer. Full article

To date, histological biomarkers expressed by laryngeal cancer are poorly known. The identification of biomarkers associated with laryngeal squamous cell carcinoma (SCC), would help explain the tumorogenesis and prevent the possible recurrence of the lesion after treatment. For this reason, the aim of this study is to investigate, for the first time, the Orphanin expression in 48 human specimens of laryngeal SCC and evaluate its possible correlation with patients prognosis. We analyzed pathological specimens from 48 patients with laryngeal SCC to detect the presence of Orphanin by using an immunohistochemistry test. We compared the findings with healthy tissue acquired from patients who underwent surgery for mesenchymal benign tumours of the larynx. The specimens were stained with anti-Orphanin monoclonal antibodies. Results were processed through a computerised image analysis system to determine a scale of staining intensity. All the tumoural specimens examined showed a significant immunoreaction for Orphanin when compared with healthy tissues (p < 0.05) but with a different immune reactivity related to clinical-pathological features. A high Orphanin expression was not significantly related to Histological Grading (HG), TNM, and stage (p > 0.05). In the multivariate analysis, the Orphanin expression was significantly related only to the malignant recurrence (p < 0.05). Our study suggests that Orphanin could have a role in tumorigenesis by increasing the recurrence of cancer; therefore, it should be further explored as a possible biomarker for laryngeal cancer. Full article

Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection accuracy and speed up computer-aided diagnosis. It often takes a lot of time for the physician to manually inspect the informative frames. This issue is commonly addressed by a classifier with task-specific categories of the uninformative frames. However, the definition of the uninformative categories is ambiguous, and tedious labeling still cannot be avoided. Here, we show that a novel unsupervised scheme is comparable to the current benchmarks on the dataset of NBI-InfFrames. We extract feature embedding using a vanilla neural network (VGG16) and introduce a new dimensionality reduction method called UMAP that distinguishes the feature embedding in the lower-dimensional space. Along with the proposed automatic cluster labeling algorithm and cost function in Bayesian optimization, the proposed method coupled with UMAP achieves state-of-the-art performance. It outperforms the baseline by 12% absolute. The overall median recall of the proposed method is currently the highest, 96%. Our results demonstrate the effectiveness of the proposed scheme and the robustness of detecting the informative frames. It also suggests the patterns embedded in the data help develop flexible algorithms that do not require manual labeling. Full article

Laryngeal squamous cell cancer (LSCC) is one of the most common malignant tumors of the head and neck region, with a poor survival rate (5-year overall survival 50–80%) as a consequence of an advanced-stage diagnosis and high recurrence rate. Tobacco smoking and alcohol abuse are the main risk factors of LSCC development. An early diagnosis of LSCC, a prompt detection of recurrence and a more precise monitoring of the efficacy of different treatment modalities are currently needed to reduce the mortality. Therefore, the identification of effective diagnostic and prognostic biomarkers for LSCC is crucial to guide disease management and improve clinical outcomes. In the past years, a dysregulated expression of small non-coding RNAs, including microRNAs (miRNAs), has been reported in many human cancers, including LSCC, and many miRNAs have been explored for their diagnostic and prognostic potential and proposed as biomarkers. We searched electronic databases for original papers that were focused on miRNAs and LSCC, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. According to the outcome, 566 articles were initially screened, of which 177 studies were selected and included in the analysis. In this systematic review, we provide an overview of the current literature on the function and the potential diagnostic and prognostic role of tissue and circulating miRNAs in LSCC. Full article

(1) Background: Early-stage glottic cancer is easily missed on magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) may improve diagnostic accuracy. Therefore, our aim was to assess the value of adding diffusion-weighted imaging with background body signal suppression (DWIBS) to pre-therapeutic MRI staging. (2) Methods: Two radiologists with 8 and 13 years of experience, blinded to each other’s findings, initially interpreted only standard MRI, later DWIBS alone, and afterward, standard MRI + DWIBS in 41 patients with histopathologically proven pT1a laryngeal cancer of the glottis. (3) Results: Detectability rates with standard MRI, DWIBS only, and standard MRI + DWIBS were 68–71%, 63–66%, and 73–76%, respectively. Moreover, interobserver reliability was calculated as good (κ = 0.712), very good (κ = 0.84), and good (κ = 0.69) for standard MRI, DWIBS only, and standard MRI + DWIBS, respectively. (4) Conclusions: Standard MRI, DWIBS alone, and standard MRI + DWIBS showed an encouraging detection rate, as well as distinct interobserver reliability in the diagnosis of early-stage laryngeal cancer when compared to the definitive histopathologic report. Full article