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Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How
Charles Lépine1,2, Paul Klein3, Thibault Voron1
1INSERM-U970, PARCC, Université de Paris, Paris, France.
Insights
Juvenile-onset recurrent respiratory papillomatosis (JoRRP) severity can be predicted using p63 antibody immunostaining. This biomarker shows potential for identifying children at risk for severe JoRRP, aiding in early intervention.
Area of Science:
- Pediatric Otolaryngology
- Oncology
- Digital Pathology
Background:
- Juvenile-onset recurrent respiratory papillomatosis (JoRRP) presents unpredictably in children, ranging from mild symptoms to aggressive disease requiring extensive medical intervention.
- Identifying histologic risk factors is crucial for predicting JoRRP severity and tailoring patient management.
Purpose of the Study:
- To identify histologic severity risk factors in pediatric patients diagnosed with JoRRP.
- To evaluate the utility of deep learning and machine learning in analyzing digital slides and immunohistochemistry for predicting JoRRP severity.
Main Methods:
- Retrospective analysis of 48 pediatric patients with JoRRP from two French centers.
- Hematoxylin and eosin (HE) slide analysis using deep learning to identify morphological patterns.
- Immunohistochemistry for p53 and p63, quantified using machine learning and deep learning workflows.
Main Results:
- Deep learning analysis of HE slides did not yield significant results for predicting severity.
- Immunohistochemistry for p63 showed a significant correlation with JoRRP severity, with higher p63 staining intensity in severe cases (p=0.041 to 0.048 for machine learning, p=0.045 for deep learning).
- No significant differences in p53 staining were observed between mild and severe JoRRP groups.
Conclusions:
- p63 immunostaining is a potential biomarker for predicting the severity of Juvenile-onset recurrent respiratory papillomatosis.
- Automated image analysis, particularly using machine learning and deep learning, can effectively quantify immunohistochemical staining for biomarker assessment in JoRRP.
Abstract:
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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