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논문 기본 정보

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저자정보
Zhou Huiqin (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Fan Wenjun (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Qin Danxue (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Liu Peiqiang (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Gao Ziang (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Lv Hao (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Zhang Wei (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Xiang Rong (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan) Xu Yu (Department of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan China.Research Institute of Otolaryngology-Head and Neck Surgery Renmin Hospital of Wuhan University Wuhan)
저널정보
대한천식알레르기학회(구 대한알레르기학회) Allergy, Asthma & Immunology Research Allergy, Asthma & Immunology Research Vol.15 No.1
발행연도
2023.1
수록면
67 - 82 (16page)
DOI
10.4168/aair.2023.15.1.67

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Purpose: Chronic rhinosinusitis with nasal polyps (CRSwNP) can be classified into eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (non-eCRSwNP) by tissue biopsy, which is difficult to perform preoperatively. Clinical biomarkers have predictive value for the classification of CRSwNP. We aimed to evaluate the application of artificial neural network (ANN) modeling in distinguishing different endotypes of CRSwNP based on clinical biomarkers. Methods: Clinical parameters were collected from 109 CRSwNP patients, and their predictive ability was analyzed. ANN and logistic regression (LR) models were developed in the training group (72 patients) and further tested in the test group (37 patients). The output variable was the diagnosis of eCRSwNP, defined as tissue eosinophil count > 10 per high-power field. The receiver operating characteristics curve was used to assess model performance. Results: A total of 15 clinical features from 60 healthy controls, 60 eCRSwNP and 49 non-eCRSwNP were selected as candidate predictors. Nasal nitric oxide levels, peripheral eosinophil absolute count, total immunoglobulin E, and ratio of bilateral computed tomography scores for the ethmoid sinus and maxillary sinus were identified as important features for modeling. Two ANN models based on 4 and 15 clinical features were developed to predict eCRSwNP, which showed better performance, with the area under the receiver operator characteristics significantly higher than those from the respective LR models (0.976 vs. 0.902, P = 0.048; 0.970 vs. 0.845, P = 0.011). All ANN models had better fits than single variable prediction models (all P < 0.05), and ANN model 1 had the best predictive performance among all models. Conclusions: Machine learning models assist clinicians in predicting endotypes of nasal polyps before invasive detection. The ANN model has the potential to predict eCRSwNP with high sensitivity and specificity, and is superior to the LR model. ANNs are valuable for optimizing personalized patient management.
#Machine learning #neural network models #biomarker #eosinophils #nasal polyps

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