Premalignant Conditions of Larynx

Mehta, Nitika; Tabassum, Saima

doi:10.5772/intechopen.97870

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…[2][3][4] Early diagnosis of head and neck (HN) lesions is therefore essential to prevent the progression to invasive carcinoma. 5 Yet the categorization of HN dysplasias remains a contentious issue 6,7 due to the varying terminologies, grading methodologies, and low to moderate reproducibility among pathologists 6,8,9,10,11,12 (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…The distressing morbidity and mortality statistics associated with HNSCC are primarily due to late‐stage diagnoses and challenging treatment protocols 2‐4 . Early diagnosis of head and neck (HN) lesions is therefore essential to prevent the progression to invasive carcinoma 5 . Yet the categorization of HN dysplasias remains a contentious issue 6,7 due to the varying terminologies, grading methodologies, and low to moderate reproducibility among pathologists 6,8,9,10,11,12 (Table 1).…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Lubrano,

Bellahsen‐Harrar,

Berlemont

et al. 2023

Histopathology

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BackgroundDiagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow‐up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter‐ and intraobserver variability remains substantial, particularly for nonspecialized pathologists, exhibiting the need for new tools to support pathologists.MethodsIn this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of HN lesions following the 2022 WHO classification system. We created, for the first time, a large‐scale database of histological samples (>2000 slides) intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole‐slide images (WSI). We evaluated our model on both internal and external test sets and we defined and validated a new confidence score to assess the predictions that can be used to identify difficult cases.ResultsOur model demonstrated high classification accuracy across all lesion types on both internal and external test sets (respectively average area under the curve [AUC]: 0.878 (95% confidence interval [CI]: [0.834–0.918]) and 0.886 (95% CI: [0.813–0.947])) and the confidence score allowed for accurate differentiation between reliable and uncertain predictions.ConclusionOur results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying HN squamous lesions by limiting variability and detecting ambiguous cases, taking us one step closer to a wider adoption of AI‐based assistive tools.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Lubrano,

Bellahsen‐Harrar,

Berlemont

et al. 2023

Histopathology

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“…One reason for these figures is the late diagnosis of invasive lesions and their dysplastic counterparts. Early detection of dysplasia is essential in preventing invasive carcinomas 4 , and accurate grading is decisive as the grade remains the most important prognostic factor for the biological behavior of disease, guiding the physicians in their care strategy 5 . Pathological examination is the gold standard diagnostic method 6 but poses many challenges.…”

Section: Introductionmentioning

confidence: 99%

Evaluating AI-Assistance for Pathologists in Diagnosing and Grading Laryngeal Lesions

Bellahsen-Harrar,

Lubrano,

Lépine

et al. 2023

Preprint

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Importance: Diagnosis of head and neck squamous dysplasias and carcinomas is challenging, with a moderate inter-rater agreement. Nowadays, new artificial intelligence (AI) models are developed to automatically detect and grade lesions, but their contribution to the performance of pathologists has not been assessed. Objective: To evaluate the contribution of our AI tool in assisting pathologists in diagnosing squamous dysplasia and carcinoma in the head and neck region. Design, Setting, and Participants: We evaluated the effectiveness of our previously described AI model, which combines an automatic classification of laryngeal and pharyngeal squamous lesions with a confidence score, on a panel of eight pathologists coming from different backgrounds and with different levels of experience on a subset of 115 slides. Main Outcomes and Measures: The main outcome was the inter-rater agreement, measured by the weighted linear kappa. Other outcomes on diagnostic efficiency were assessed using paired t tests. Results: AI-Assistance significantly improved the inter-rater agreement (linear kappa 0.73 with assistance versus 0.675 without assistance, p < 0.001) Moreover, the agreement was even better on high confidence predictions (linear kappa 0.8 vs 0.73, p < 0.001). This improvement was particularly strong for non-HN specialised pathologists and younger pathologists. Hence, the AI-Assistance enabled the pathologists panel to perform on par with the expert panel described in the literature. Conclusions and Relevance: Our AI-Assistance is of great value for helping pathologists in the difficult task of diagnosing squamous dysplasias and carcinomas, improving for the first time the inter-rater agreement. It demonstrates the possibility of a truly Augmented Pathology in complex tasks such as the classification of head and neck squamous lesions.

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Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract

Lubrano

Bellahsen-Harrar

Berlemont³

et al. 2022

Preprint

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Diagnosis of head and neck squamous dysplasia and carcinomas is critical for patient care, cure and follow-up. It can be challenging, especially for intraepithelial lesions. Even though the last WHO classification simplified the grading of dysplasia with only two grades (except for oral or oropharyngeal lesions), the inter and intra-observer variability remains substantial, especially for non-specialized pathologists. In this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of head and neck squamous lesions following the 2022 WHO classification system for the hypopharynx, larynx, trachea and parapharyngeal space. We created, for the first time, a large scale database of histological samples intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole slides images. A dual blind review was carried out to define a gold standard test set on which our model was able to classify lesions with high accuracy on every class (average AUC: 0.878 (95% CI: [0.834-0.918])). Finally, we defined a confidence score for the model predictions, which can be used to identify ambiguous or difficult cases. When the algorithm is applied as a screening tool, such cases can then be submitted to pathologists in priority. Our results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying head and neck squamous lesions.

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Premalignant Conditions of Larynx

Cited by 3 publications

References 26 publications

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Evaluating AI-Assistance for Pathologists in Diagnosing and Grading Laryngeal Lesions

Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract

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