AimSebaceous tumours and keratoacanthomas can be associated with mismatch repair (MMR) deficiency and thus microsatellite instability (MSI). In such tumours, MSI phenotype could be an argument to search for an underlying Muir-Torre syndrome (MTS). MTS has been recognised as a variant of Lynch syndrome, characterised by a deficiency of the MMR proteins. In Lynch syndrome, the sensitivity and specificity of the techniques used to detect MSI is well described, which is not the case for skin tumours. In our hands, immunohistochemistry is a sensitive and specific method to detect MMR deficiency in those tumours. Contrasting with tumours of Lynch spectrum, sensitivity and specificity of molecular methods has not been extensively studied. This study aimed at evaluating two molecular methods to detect MSI phenotype in MTS associated tumours: a commonly used pentaplex PCR using Bethesda markers and the fully automated method using the Idylla MSI assay.MethodsA comparison between PCR, and Idylla was performed on 39 DNA extracted from cutaneous tumours. Immunohistochemistry was used as the gold standard to calculate sensitivity and specificity of both molecular techniques.ResultsConcordant results were found in 32 cases (82%) with pentaplex PCR and in 36 cases (92%) with Idylla. The sensitivity of pentaplex PCR to detect MSI phenotype was 76% whereas Idylla sensitivity was 90%.ConclusionIdylla is more performant than PCR, for the detection of MSI in MTS-associated tumours and is a reliable additional technique to help detecting MTS in these tumours.
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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