Zhou Huiqin, Fan Wenjun, Qin Danxue, Liu Peiqiang, Gao Ziang, Lv Hao, Zhang Wei, Xiang Rong, 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, China.
Allergy Asthma Immunol Res. 2023 Jan;15(1):67-82. doi: 10.4168/aair.2023.15.1.67.
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.
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.
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, = 0.048; 0.970 vs. 0.845, = 0.011). All ANN models had better fits than single variable prediction models (all < 0.05), and ANN model 1 had the best predictive performance among all models.
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.
伴有鼻息肉的慢性鼻-鼻窦炎（CRSwNP）可通过组织活检分为嗜酸性粒细胞性CRSwNP（eCRSwNP）和非嗜酸性粒细胞性CRSwNP（非eCRSwNP），但术前难以进行。临床生物标志物对CRSwNP的分类具有预测价值。我们旨在评估人工神经网络（ANN）模型在基于临床生物标志物区分CRSwNP不同亚型中的应用。
收集109例CRSwNP患者的临床参数，并分析其预测能力。在训练组（72例患者）中建立ANN和逻辑回归（LR）模型，并在测试组（37例患者）中进一步测试。输出变量为eCRSwNP的诊断，定义为每高倍视野组织嗜酸性粒细胞计数>10个。采用受试者工作特征曲线评估模型性能。
从60例健康对照、60例eCRSwNP和49例非eCRSwNP中总共选择15项临床特征作为候选预测指标。鼻一氧化氮水平、外周嗜酸性粒细胞绝对计数、总免疫球蛋白E以及筛窦和上颌窦双侧计算机断层扫描评分的比值被确定为建模的重要特征。开发了基于4项和15项临床特征的两个ANN模型来预测eCRSwNP，其表现更好，受试者工作特征曲线下面积显著高于各自的LR模型（0.976对0.902，P = 0.048；0.970对0.845，P = 0.011）。所有ANN模型的拟合度均优于单变量预测模型（均P < 0.05），且ANN模型1在所有模型中预测性能最佳。
机器学习模型有助于临床医生在进行侵入性检测之前预测鼻息肉的亚型。ANN模型有潜力以高灵敏度和特异性预测eCRSwNP，且优于LR模型。人工神经网络对于优化个性化患者管理具有重要价值。
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