The impact of laryngeal biopsy on voice outcomes: a pilot study

Bergström, Liza; Ward, Elizabeth C.; Finizia, Caterina

doi:10.15761/ohns.1000108

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…It should be emphasized that directed biopsy and histopathology remains the gold standard for the final diagnosis of laryngeal lesions. However, biopsy is a mentally and physically demanding procedure for the patient and may cause vocal fold or laryngeal dysfunction [ 5 , 12 ]; therefore, less invasive diagnostic methods with a sensitivity and specificity close to histopathology results are being sought.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis

Žůrek

Jasak

Niemczyk

et al. 2022

JCM

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Background: Early diagnosis of laryngeal lesions is necessary to begin treatment of patients as soon as possible to preserve optimal organ functions. Imaging examinations are often aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of this study is to investigate diagnostic utility of AI in laryngeal endoscopy. Methods: Five databases were searched for studies implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity, and specificity. Results: All 11 studies included presented an overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997). The accuracy was significantly higher in studies using a larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue were 0.91 and 0.97, respectively. The same values for differentiation between benign and malignant lesions were 0.91 and 0.94, respectively. The comparison of the effectiveness of AI models assessing narrow band imaging and white light endoscopy images revealed no statistically significant differences (p = 0.409 and 0.914). Conclusion: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis

Žůrek

Jasak

Niemczyk

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…It should be emphasized that directed biopsy and histopathology remains the gold standard for final diagnosing of laryngeal lesions. However, biopsy is a mentally and physically demanding procedure for the patient and may cause vocal fold or laryngeal dysfunction [5, 12], therefore less invasive diagnostic methods of sensitivity and specificity closest to histopathology results are still searched.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in laryngeal endoscopy: Systematic Review and Meta-Analysis

Žůrek

Rzepakowska

Jasak

et al. 2022

Preprint

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Background: Early and proper diagnosis of laryngeal lesions is necessary to begin treatment of the patient as soon as possible with the possibility of preserve organ functions. Imaging examinations are oft aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of the study is to investigate of the diagnostic utility of AI in laryngeal endoscopy. Methods: Five electronic databases (PubMed, Embase, Cochrane, Scopus, Web of Science) were searched for studies published before October 15, 2021 implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity and specificity. Results: All 13 included studies presented overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997) and the number of images used to build and evaluate the models ranged from 120 to 24,667. The accuracy was significantly higher in studies using larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue (8 studies) was 0.91 (95% CI: 0.83-0.98) and 0.97 (95% CI: 0.96-0.99), respectively. The same values for differentiation between benign and malignant lesions (7 studies) were 0.91 (95% CI: 0.86-0.96) and 0.95 (95% CI: 0.90-0.99), respectively. The analysis was extended to a comparison of sensitivity and specificity of AI models assessing Narrow Band Imaging (3 studies) and white light endoscopy images (4 studies). The results were similar for both methods, no subgroup effect was revealed (p = 0.406 for sensitivity and p = 0.817 for specificity). Conclusions: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity. AI enhanced diagnostic tools should be introduced into everyday clinical work. The performance of AI diagnoses increases efficacy with the size of the image database when using similar standards for evaluating images. The multicentre cooperation should concentrate on creation of huge database of laryngeal lesions images and implement their sharing, which allows building AI modes with the best performance, based on vast amount of images for learning and testing.

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Laryngeal cytology: A cytological, histological, P16 and human papillomavirus study

Demırel¹,

Erkul²,

Güngör³

et al. 2019

Cytopathology

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BackgroundInvasive diagnostic methods, such as punch biopsies, have a potential to produce undesirable side effects in the larynx, such as scarring and vocal dysfunction. This study is an attempt to assess the diagnostic potential of cytology to efficiently diagnose premalignant and malignant laryngeal lesions, while sparing patients the risk of complications of punch biopsies.MethodsLaryngeal smears, using endocervical‐type brushes, and punch biopsies were procured from each patient. Smears were prepared and the brush was cut and put in Surepath preservative solution for cytological analysis and human papillomavirus (HPV) DNA testing. A Real‐TM Quant kit that detects 14 HPV types was used for genotyping. Immunohistochemical staining for p16 was performed on cytological and histological specimens.ResultsCytological diagnosis was correct in 84.6%, 100% and 100% of cases with a histological diagnosis of squamous cell carcinomas, high‐grade squamous intraepithelial lesions and benign lesions, respectively. However, cytological interpretation was correct only in 25% of low‐grade squamous intraepithelial lesions. HPV DNA test was positive in only one case, which was a laryngeal polyp. Testing for p16 was negative in all the cytological and histological material.ConclusionLaryngeal cytology is a useful diagnostic tool in establishing the diagnosis of high‐grade squamous epithelial cell abnormalities. Recognition of low‐grade lesions, however, is challenging. HPV genotyping and p16 staining do not seem to be helpful ancillary techniques in cytological material procured from the larynx.

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The impact of laryngeal biopsy on voice outcomes: a pilot study

Cited by 5 publications

References 19 publications

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis

Artificial Intelligence in laryngeal endoscopy: Systematic Review and Meta-Analysis

Laryngeal cytology: A cytological, histological, P16 and human papillomavirus study

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