Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How Immunohistochemistry and AI Algorithms Can Help?

Medientyp: E-Artikel

Titel: Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How Immunohistochemistry and AI Algorithms Can Help?

Beteiligte: Lépine, Charles; Klein, Paul; Voron, Thibault; Mandavit, Marion; Berrebi, Dominique; Outh-Gauer, Sophie; Péré, Hélène; Tournier, Louis; Pagès, Franck; Tartour, Eric; Le Meur, Thomas; Berlemont, Sylvain; Teissier, Natacha; Carlevan, Mathilde; Leboulanger, Nicolas; Galmiche, Louise; Badoual, Cécile

Erschienen: Frontiers Media SA, 2021

Sprache: Nicht zu entscheiden

DOI: 10.3389/fonc.2021.596499

ISSN: 2234-943X

Schlagwörter: Cancer Research ; Oncology

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Beschreibung: Juvenile-onset recurrent respiratory papillomatosis (JoRRP) is a condition characterized by the repeated growth of benign exophytic papilloma in the respiratory tract. The course of the disease remains unpredictable: some children experience minor symptoms, while others require multiple interventions due to florid growth. Our study aimed to identify histologic severity risk factors in patients with JoRRP. Forty-eight children from two French pediatric centers were included retrospectively. Criteria for a severe disease were: annual rate of surgical endoscopy ≥ 5, spread to the lung, carcinomatous transformation or death. We conducted a multi-stage study with image analysis. First, with Hematoxylin and eosin (HE) digital slides of papilloma, we searched for morphological patterns associated with a severe JoRRP using a deep-learning algorithm. Then, immunohistochemistry with antibody against p53 and p63 was performed on sections of FFPE samples of laryngeal papilloma obtained between 2008 and 2018. Immunostainings were quantified according to the staining intensity through two automated workflows: one using machine learning, the other using deep learning. Twenty-four patients had severe disease. For the HE analysis, no significative results were obtained with cross-validation. For immunostaining with anti-p63 antibody, we found similar results between the two image analysis methods. Using machine learning, we found 23.98% of stained nuclei for medium intensity for mild JoRRP vs. 36.1% for severe JoRRP (p = 0.041); and for medium and strong intensity together, 24.14% for mild JoRRP vs. 36.9% for severe JoRRP (p = 0.048). Using deep learning, we found 58.32% for mild JoRRP vs. 67.45% for severe JoRRP (p = 0.045) for medium and strong intensity together. Regarding p53, we did not find any significant difference in the number of nuclei stained between the two groups of patients. In conclusion, we highlighted that immunochemistry with the anti-p63 antibody is a potential biomarker to predict the severity of the JoRRP.

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