Diagnosis Of Laryngeal Cancer Research Articles

Self-Attention Mechanisms-Based Laryngoscopy Image Classification Technique for Laryngeal Cancer Detection.

The early diagnosis of laryngeal cancer (LCA) is crucial for prognosis, driving our search for an accurate, precise, and sensitive deep learning model to assist in LCA detection. We collected 5768 laryngoscopic images from 1462 patients and created the intelligent laryngeal cancer detection system (ILCDS) based on Swin-Transformer. Following training and validation, we assessed the ILCDS performance on the internal and external test sets and compared it with previous convolutional neural network (CNN) models and three professional laryngologists. The ILCDS outperformed the six CNNs, with the highest accuracy of 92.78% and an area under the curve (AUC) of 0.9732. Despite a slight drop in performance on external sets, the ILCDS maintained the best superiority, with 85.79% accuracy and an AUC of 0.9550. Surpassing professional laryngologists, the ILCDS achieved 92.00% accuracy. The ILCDS offers high accuracy and stability for LCA detection, reducing the burden on laryngologists.

Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis.

Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic review and meta-analysis to investigate diagnostic utility of deep learning in laryngoscopy. The study was performed according to the Primary Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We comprehensively retrieved articles from the PubMed, Scopus, Embase, and Web of Science up to July 14, 2024. Eligible studies with application of deep learning algorithm in laryngoscopy were assessed and enrolled by two independent investigators. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio with 95% confidence intervals (CIs) were calculated using a random effects model. We retained 9 eligible studies adding up to 106,175 endoscopic images for the meta-analysis. The pooled sensitivity and specificity to diagnose laryngeal cancer were 0.95(95% CI: 0.85-0.98) and 0.96 (95% CI: 0.91-0.98). The area under the curve of deep learning was 0.99 (95%CI: 0.97-0.99). Deep learning demonstrated excellent diagnostic efficacy in assessing laryngeal cancer with laryngoscope images in current studies, which manifests its potential of aiding endoscopist for laryngeal cancer diagnosis and clinical decision making.

Analysis of Risk Factors with Assessment of the Impact of the Microbiome on the Risk of Squamous Cell Carcinoma of the Larynx.

Introduction: Head and neck squamous cell carcinoma (HNSCC) ranks sixth among cancers in the world, and the 5-year survival rate ranges from 25% to 60%. The risk factors for HNSCC are primarily smoking, alcohol consumption and human papillomavirus (HPV). Data indicate that 15-20% of cancers are caused by infectious agents, 20-30% by smoking and 30-35% by unhealthy lifestyles, diet, lack of physical activity and obesity. Dysbiosis is a microbiome imbalance, which promotes oncogenesis by intensifying inflammatory processes and affecting the host's metabolism. Profiling the microbiome in various types of cancer is currently the subject of research and analysis. However, there is still little information on the correlation of the microbiome with HNSCC and its impact on oncogenesis, the course of the disease and its treatment. Objective: The aim of the study was to prospectively assess risk factors with assessment of the impact of the microbiome on the risk of squamous cell carcinoma of the larynx. The study included a group of 44 patients diagnosed with squamous cell carcinoma of the larynx and 30 patients from the control group. Results: In the control group, bacteria of the normal microbiome dominated-the genus Streptococcus, Gemella, Neisseria and Kingella. In the group of patients with laryngeal cancer, Prevotella, Clostridiales and Stomatobaculum were found significantly more often. Porphyromonas, Fusobacterium, Lactobacillus, Actinobacteria, Actinomyces and Shaalia odontolytica were also found at a higher percentage in the study group. Analyzing the phylum, Firmicutes dominated in the control group; there were statistically significantly more of them than in patients from the study group. Bacteroides and Bacillota were found significantly more often in patients with laryngeal cancer. Conclusions: The importance of the microbiome in oncology has been confirmed in many studies. Independent risk factors for laryngeal cancer were primarily a lower number of Firmicutes in the microbiome, but also an increased leukocyte level above 6.52 × 103/mm and a decreased total protein level below 6.9 g/dL. Prevotella, Clostridiales, Stomatobaculum, Porphyromonas, Fusobacterium, Lactobacillus, Actinobacteria, Actinomyces and Shaalia were considered to be the bacteria contributing to the development of laryngeal cancer. Streptococcus, Gemella, Neisserie and Kingella were considered to be protective bacteria. Moreover, the study confirmed the significant impact of smoking, alcohol consumption and poor oral hygiene on the development of laryngeal cancer. The microbiome, its identification and manipulation may constitute a breakthrough discovery for improving the diagnosis and oncological therapy of laryngeal cancer, and also of the entire group of HNSCC. Profiling the microbiome may allow for personalized therapy related to its modification. Assessing the microbiome of patients diagnosed with cancer may provide an opportunity to predict treatment response and effectiveness.

Pharyngeal mucosal closure in total laryngectomy: comparison between vertical and T-shaped closure.

The objective of this study was to compare the outcomes vertical and T-shaped pharyngoplasty closure techniques after total laryngectomy (TL) and to evaluate the factors associated with the development of pharyngocutaneous fistula. We performed a retrospective study that included patients with a histopathological diagnosis of laryngeal cancer that underwent TL between 2009 and 2021. Fifty-seven patients were included in the study. A total of 14 patients underwent a vertical closure of the neopharynx (24.6%), while 43 patients underwent a T-shaped closure (74.4%). Pharyngocutaneous fistula was the most common complication, observed in 40.4% of cases (n = 23). No difference in the rate of complications was observed between groups, with the exception of tracheal dehiscence which was reduced in patients with T-shaped closure (n = 2, 4.7% vs. n = 5, 35.7%, p = 0.002). Diabetes mellitus was more frequently observed in patients withthe development of pharyngocutaneous fistula (n = 7, 30.4% vs. n = 3, 8.8%, p = 0.03). Although complicationswere lower in the T-shaped closure group, we could not establish the superiority of either technique.

Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images

Laryngeal cancer exhibits a notable global health burden, with later-stage detection contributing to a low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses in the medical sector. It includes detecting and analysing abnormal or cancerous tissue from the larynx, an integral part of the vocal and respiratory systems. The computer-aided system makes use of artificial intelligence (AI) through deep learning (DL) and machine learning (ML) models, including convolution neural networks (CNN), for automated disease diagnoses and detection. Various DL and ML approaches are executed to categorize the extraction feature as healthy and cancerous tissues. This article introduces an automated Laryngeal Cancer Diagnosis using the Dandelion Optimizer Algorithm with Ensemble Learning (LCD-DOAEL) method on Biomedical Throat Region Image. The LCD-DOAEL method aims to investigate the images of the throat region for the presence of laryngeal cancer. In the LCD-DOAEL method, the Gaussian filtering (GF) approach is applied to eliminate the noise in the biomedical images. Besides, the complex and intrinsic feature patterns can be extracted by the MobileNetv2 model. Meanwhile, the DOA model carries out the hyperparameter selection of MobileNetV2 architecture. Finally, the ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), regularized extreme learning machine (ELM), and backpropagation neural network (BPNN) models, are utilized for the classification process. A comprehensive set of simulations is conducted on the biomedical image dataset to highlight the efficient performance of the LCD-DOAEL technique. The comparison analysis of the LCD-DOAEL method exhibited a superior accuracy outcome of 97.54% over other existing techniques.

Exosomes multiplex profiling, a promising strategy for early diagnosis of laryngeal cancer

BackgroundExosomes are nanosized vesicles released from all cells into surrounding biofluids, including cancer cells, and represent a very promising direction in terms of minimally invasive approaches to early disease detection. They carry tumor-specific biological contents such as DNA, RNA, proteins, lipids, and sugars, as well as surface molecules that are able to pinpoint the cellular source. By the above criteria, exosomes may be stratified according to the presence of tissue and disease-specific signatures and, due to their stability in such biofluids as plasma and serum, they represent an indispensable source of vital clinical insights from liquid biopsies, even at the earliest stages of cancer. Therefore, our work aimed to isolate and characterize LCa patients’ derived exosomes from serum by Flow Cytometry in order to define a specific epitope signature exploitable for early diagnosis.MethodsCirculating exosomes were collected from serum collected from 30 LCa patients and 20 healthy volunteers by the use of antibody affinity method exploiting CD63 specific surface marker. Membrane epitopes were then characterized by Flow cytometry multiplex analysis and compared between LCa Patients and Healthy donors. Clinical data were also matched to obtain statistical correlation.ResultsA distinct overexpression of CD1c, CD2, CD3, CD4, CD11c, CD14, CD20, CD44, CD56, CD105, CD146, and CD209 was identified in LCa patients compared to healthy controls, correlating positively with tumor presence. Conversely, CD24, CD31, and CD40, though not overexpressed in tumor samples, showed a significant correlation with nodal involvement in LCa patients (p < 0.01).ConclusionThis approach could allow us to set up a cost-effective and less invasive liquid biopsy protocol from a simple blood collection in order to early diagnose LCa and improve patients’ outcomes and quality of life.Graphical

Operative techniques in otolaryngology: Head and neck surgery. Geisinger Medical Center experience for whole mount laryngectomy specimen

The American Cancer Society estimates approximately 12,380 new cases of laryngeal cancer (0.7% of all cases) with approximately 3820 people dying from the disease. There is 3.86:1 male to female ratio and represent third most prevalent upper aerodigestive tract site, following the oral and pharyngeal regions. Typically affects patients in the fifth decade of life. Accurate diagnosis and management are crucial for patient survival. This study examines the pathological features of laryngectomy specimens and their significance in the diagnosis and management of laryngeal cancer. Our institution's intraoperative evaluation of margins and surgical procedures for laryngeal cancer treatment are also discussed. The study found that gross pathology and histomorphology of laryngeal specimens after laryngectomy are the most accurate methods for determining the extent of tumor involvement. The majority of our procedures involve total or partial laryngectomy. The significance of surgical pathology in the diagnosis and management of laryngeal cancer is highlighted in this article.

Autoantibodies in laryngeal cancer: detection and role as a biomarker.

Diagnostic role of autoantibodies (AAb) as serological biomarkers has not been specifically investigated in laryngeal cancer (LC) previously. The study investigates the presence of anti-LC AAbs and their potential as a biomarker for early diagnosis of LC, to improve patient outcome. Anti-LC AAb levels were investigated in LC patients (n = 30) and healthy individuals (n = 30) by indirect enzyme-linked immunosorbent assay (ELISA). Patient AAb levels were analyzed with various clinical factors, primarily tumor stage. AAb levels were significantly higher in LC patients than in the control group (P = .019). The diagnostic performance of AAb-level testing for LC detection presented a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 70% each. The positive likelihood (LR+) and negative likelihood (LR-) ratios were 2.33 and 0.43, respectively. AAb levels were independent of cancer stage (P = .708), duration since first appearance of symptoms (P = .228), duration of medical attention (P = .231), and degree of risk-factor exposure (P = .478). Significant level of AAbs could be detected among LC patients with good diagnostic performance, irrespective of stage. Thus, anti-LC AAbs reflect potential to be utilized as predictive biomarkers in early diagnostics of LC.

Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images.

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.

Endoscopic autofluorescence imaging in the diagnosis of premalignant lesions and laryngeal/hypopharyngeal cancer

The purpose of the study was to assess the diagnostic value of endoscopic autofluorescence imaging (AFI) in patients with premalignant lesions and primary laryngeal/hypopharyngeal cancer.Material and Methods. The diagnostic value of AFI was assessed in 53 patients with chronic hyperplastic laryngeal/hypopharyngeal lesions and 48 patients with laryngeal/hypopharyngeal cancer.Results. The inclusion of video laryngoscopy with AFI in the algorithm for examining patients with chronic diseases of the upper respiratory tract made it possible to significantly improve the diagnostic efficacy of endoscopic examination in patients with premalignant lesions (high grade dysplasia) of the laryngeal mucosa. Video laryngoscopy with AFI was found to achieve higher sensitivity, specificity and accuracy rates than white light video laryngoscopy (87.5, 96.9 and 92.5 vs 50.0, 96.0 and 71.7 %, respectively, p&lt;0.05). The video laryngoscopy with AFI allowed identification of not only pathological changes in the laryngeal and hypopharyngeal mucosa but also their malignant potential, as well as identification of areas for targeted biopsy. In 9 (18.8 %) laryngeal cancer patients, the standard white light video laryngoscopy demonstrated difficulties in the differential diagnosis of laryngeal cancer (n=4) and in assessing the extent of laryngeal cancer (n=5). In 3 out of 4 diagnostically difficult cases, video laryngoscopy with AFI made it possible to confirm the diagnosis of laryngeal cancer and in 5 cases to accurately determine the extent of hypopharyngeal cancer.Conclusion. The addition of standard videolaryngoscopy with a hightech technique of autofluorescence endoscopy makes it possible to accurately diagnose premalignant and malignant lesions of the larynx and hypopharynx.

Tobacco Cessation Following Laryngeal Cancer Diagnosis Predicts Response to Treatment and Laryngectomy-Free Survival.

To determine the effect of tobacco cessation following laryngeal cancer diagnosis on response to first-line therapy, laryngectomy-free survival, and overall survival in patients who were current smokers at the time of diagnosis. Retrospective, case-control study. OU Stephenson Cancer Center, National Cancer Institute-Designated Cancer Center. We included 140 patients diagnosed with laryngeal squamous cell carcinoma, who were current smokers at the time of diagnosis, and were treated with first-line definitive radiation or chemo/radiation with the intent to cure. The association between patient characteristics and treatment response was assessed using the χ2 test and logistic regression analysis. Survival outcomes were analyzed using Kaplan-Meier methods and Cox proportional-hazards models. Of the 140 current smokers, 61 patients (45%) quit smoking prior to treatment initiation. In adjusted logistic regression analysis, quitters had 3.7 times higher odds of achieving a complete response to first-line therapy than active smokers (odds ratio: 3.694 [1.575-8.661]; P = .003). In the adjusted Cox proportional-hazards model, quitters were 54% less likely to require salvage laryngectomy within 7 years of diagnosis than active smokers (hazard ratio: 0.456 [0.246-0.848]; P = .013). Quitters had a statistically significant increase in 7-year overall survival compared to active smokers (P = .02). This is the first study to show that in newly diagnosed laryngeal cancer patients who are current smokers at the time of diagnosis, tobacco cessation significantly increases therapy response, laryngectomy-free survival, and overall survival. These data stress the importance of systematically incorporating tobacco cessation programs into laryngeal cancer treatment plans.

The Effectiveness of White Light Endoscopy Combined With Narrow Band Imaging Technique Using Ni Classification in Detecting Early Laryngeal Carcinoma in 114 Patients: Our Clinical Experience

By displaying tumor-specific neoangiogenesis, narrow band imaging (NBI), a novel imaging approach, enhances the diagnosis of head and neck cancers and makes it more accurate OBJECTIVE: To determine the effect of NBI in combination with white light endoscopy (WLE) for diagnosis of preneoplastic or neoplastic laryngeal cancers according to Ni classification and to conclude if higher Ni classification and tumor stage are related. We enrolled 114 patients with various laryngeal cancer between December 2018 and June 2021. Patients were examined with WLE and NBI. Squamous cell carcinoma (SCC) accounted for 46 cases, benign lesions 30 cases, and nondysplastic, low-grade, and severe dysplasias for 38 cases. Based on characteristics of the intraepithelial papillary capillary loop (IPCL), endoscopic NBI results were divided into five categories (I, II, III, IV, and V). Type I-IV are regarded to be benign, while type V is considered to be cancerous. An incisional biopsy was conducted to assess histopathology, and the histopathology was compared to the NBI results. We assessed the negative predictive value (NPV), positive predictive value (PPV), specificity, and sensitivity for WLE alone and WLE combined with NBI. Analyses were conducted using SPSS software version 26. The WLE combined with NBI showed excellent sensitivity (96%) compared to WLE (86.4%). Specificity was higher in the WLE combined with NBI (96.4%) than WLE alone (91.7%). WLE combined with NBI saw a NPV of 89% as compared with WLE with 88%. WLE and WLEin combination with NBI, recorded a PPV of 90% and 98%, respectively. The accuracy of detecting laryngeal cancer increases when WLE and NBI are combined. Combined NBI with WLE remains highly sensitive to early glottis cancer. Accuracy of preoperative NBI was high. In the diagnosis of laryngeal cancer, a higher Ni classification closely correlates with the late stages of the glottis tumor.

Identification of biomarkers in laryngeal cancer by weighted gene co-expression network analysis.

Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC. We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis. WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (r=-0.23, P<0.05; r=-0.33, P<0.05), while the black and brown gene modules demonstrated negative associations with cancer status (r=-0.39, P<0.05; r=-0.50, P<0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included CHRNB4, FOXL2, KCNG1, LOC440173, ADAMTS15, BMP2, FAP, and KIAA1644. This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.

Profile and characteristic of laryngeal cancer in Radiotherapy Department of Hasan Sadikin General Hospital

Background: A laryngeal cancer diagnosis is often in the advanced stage, while the screening has not much done at this time. Studies on laryngeal cancer that uses radiotherapy are also still difficult to find, especially in Indonesia. Purposes: This study aimed to describe patients' profiles with laryngeal cancer who received radiotherapy treatment in the 2018 to 2019 period. Methods: This research was a descriptive quantitative with a cross-sectional study conducted at Radiotherapy Department, Hasan Sadikin General Hospital. Results: The patient's medical record was collected, and 38 patients with laryngeal cancer were found. Most of the patients are male (86,8%), ranging from age 51-60 (55,3%), work as staff office/traders (21,1%), and have a smoking history (52%). The chief complaint is often in the form of hoarseness (76,3%), the location of the cancer is No Other Specified (31,6%), most of the cells were squamous cell carcinoma (86,8%), and the stage was mainly in stage IV (60,5%). Surgery with radiotherapy is the most common management (50%), and surgery that is commonly used is total laryngectomy (36,8%). Some patients also have a tracheostomy before management (36,8%). Radiotherapy is more often for adjuvant purposes (52,6%). Conclusions: Laryngeal cancer is still uncommon, but we need to increase awareness and screening because of the advanced stage that more commonly occurs.

Second Primary Lung Cancer after Diagnosis of Laryngeal Cancer – A Case Report

Second Primary Lung Cancer after Diagnosis of Laryngeal Cancer – A Case Report

The Effect of Coffee in the Prevention of Oral Mucositis Associated with Head and Neck Radiotherapy.

This study investigates the effect of coffee on the management of oral mucositis associated with head and neck radiotherapy. Twenty-nine patients who underwent radiotherapy for the first time due to head and neck cancer between March 2019 and February 2020 were included in the experimental study. One cup/day of Turkish coffee (6 mg) was given to the patients in the intervention group every day for 3 weeks from the first day of radiotherapy. Data monitoring of both groups was performed once a week for 3 weeks. Most of the patients participating in the study were at local stage (65.2%), and 72.4% underwent head and neck radiotherapy with the diagnosis of nasopharynx and larynx cancer. Although the development rate of oral mucositis was lower in the intervention group, no statistically significant difference was determined (p > 0.05). In the repeated follow-up, the quality of life scores was found to be similar in both groups. We concluded that coffee application is not an effective approach in the prevention of oral mucositis associated with head and neck radiotherapy. Further studies with larger sample sizes are needed to determine the prophylactic effect of coffee in the management of oral mucositis.

Clinical diagnostic value of amino acids in laryngeal squamous cell carcinomas.

Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis. We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification. Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.

The role of miRNAs in laryngeal cancer pathogenesis and therapeutic resistance - A focus on signaling pathways interplay.

Laryngeal cancer (LC)is the malignancy of the larynx (voice box). The majority of LC are squamous cell carcinomas. Many risk factors were reported to be associated with LC as tobacco use, obesity, alcohol intake, human papillomavirus (HPV) infection, and asbestos exposure. Besides, epigenetics as non-coding nucleic acids also have a great role in LC. miRNAs are short nucleic acid molecules that can modulate multiple cellular processes by regulating the expression of their genes. Therefore, LC progression, apoptosis evasions, initiation, EMT, and angiogenesis are associated with dysregulated miRNA expressions. miRNAs also could have some vital signaling pathways such as mTOR/P-gp, Wnt/-catenin signaling, JAK/STAT, KRAS, and EGF. Besides, miRNAs also have a role in the modulation of LC response to different therapeutic modalities. In this review, we have provided a comprehensive and updated overview highlighting the microRNAs biogenesis, general biological functions, regulatory mechanisms, and signaling dysfunction in LC carcinogenesis, in addition to their clinical potential for LC diagnosis, prognosis, and chemotherapeutics response implications.

The role of digital tomosynthesis in laryngeal cancer: comparison with radiography and computed tomography

Digital tomosynthesis is a relatively new imaging modality that is already used in the diagnosis of breast cancer and has shown promising results in evaluating patients with pulmonary, osteoarticular, and other pathologies. However, up to date, there are no published studies related to the usefulness of digital tomosynthesis in the evaluation of patients with laryngeal cancer. The purpose of this study was to evaluate the clinical role of digital tomosynthesis in the diagnosis of laryngeal cancer and compare the imaging results with those obtained by digital radiography and computed tomography. Material and methods. The study was carried out between 2015 and 2019 at the Institute of Oncology in the Republic of Moldova and included 253 consecutive patients with laryngeal cancer referred to the Institute of Oncology during this period. All patients underwent digital radiography and digital tomosynthesis investigations. In 41 patients who provided written informed consent, computed tomography was performed. The results of all imaging investigations were compared. Results. The statistical analysis revealed a high degree of agreement and a strong linear correlation between the data obtained with digital tomosynthesis and computed tomography, as well as concordance correlation coefficients for different parameters between 0.63 and 1.0 (mean value = 0.82±0.11). For comparison, the concordance correlation coefficients for the same parameters obtained for digital radiography versus computed tomography ranged between 0.08 and 0.93 (mean value = 0.43±0.25). An updated imaging algorithm that includes digital tomosynthesis has also been proposed for investigating patients with suspected laryngeal cancer. Conclusions. The study demonstrated the usefulness of digital tomosynthesis for the evaluation of patients with laryngeal cancer. When compared to computed tomography, which is considered the gold standard, digital tomosynthesis revealed a much higher performance compared to digital radiography. Considering the availability of low-dose protocols for digital tomosynthesis, the modality might also be helpful for laryngeal cancer screening in a high-risk population. However, new studies are also required to confirm our findings and define the place of digital tomosynthesis in the imaging algorithm for patients with laryngeal cancer.

CLO23-054: The Role of miRNA 21 and miRNA 101 in Diagnosis, Prognosis and Survival of Laryngeal Cancer

CLO23-054: The Role of miRNA 21 and miRNA 101 in Diagnosis, Prognosis and Survival of Laryngeal Cancer