Kim D-K, Lim H-S, Eun K M, Seo Y, Kim J K, Kim Y S, Kim M-K, Jin S, Han S C, Kim D W
Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital and Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea; Division of Big Data and Artificial Intelligence, Hallym University College of Medicine, Chuncheon, Republic of Korea.
Department of Otorhinolaryngology-Head and Neck Surgery, Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Rhinology. 2021 Apr 1;59(2):173-180. doi: 10.4193/Rhin20.373.
Neutrophils present as major inflammatory cells in refractory chronic rhinosinusitis with nasal polyps (CRSwNP), regardless of the endotype. However, their role in the pathophysiology of CRSwNP remains poorly understood. We investigated factors predicting the surgical outcomes of CRSwNP patients with focus on neutrophilic localization.
We employed machine-learning methods such as the decision tree and random forest models to predict the surgical outcomes of CRSwNP. Immunofluorescence analysis was conducted to detect human neutrophil elastase (HNE), Bcl-2, and Ki-67 in NP tissues. We counted the immunofluorescence-positive cells and divided them into three groups based on the infiltrated area, namely, epithelial, subepithelial, and perivascular groups.
On machine learning, the decision tree algorithm demonstrated that the number of subepithelial HNE-positive cells, Lund-Mackay (LM) scores, and endotype (eosinophilic or non-eosinophilic) were the most important predictors of surgical outcomes in CRSwNP patients. Additionally, the random forest algorithm showed that, after ranking the mean decrease in the Gini index or the accuracy of each factor, the top three ranking factors associated with surgical outcomes were the LM score, age, and number of subepithelial HNE-positive cells. In terms of cellular proliferation, immunofluorescence analysis revealed that Ki-67/HNE-double positive and Bcl-2/HNE-double positive cells were significantly increased in the subepithelial area in refractory CRSwNP.
Our machine-learning approach and immunofluorescence analysis demonstrated that subepithelial neutrophils in NP tissues had a high expression of Ki-67 and could serve as a cellular biomarker for predicting surgical outcomes in CRSwNP patients.
无论鼻息肉难治性慢性鼻-鼻窦炎（CRSwNP）的内型如何，中性粒细胞都是主要的炎症细胞。然而，它们在CRSwNP病理生理学中的作用仍知之甚少。我们以中性粒细胞定位为重点，研究了预测CRSwNP患者手术结果的因素。
我们采用决策树和随机森林模型等机器学习方法来预测CRSwNP的手术结果。进行免疫荧光分析以检测鼻息肉组织中的人中性粒细胞弹性蛋白酶（HNE）、Bcl-2和Ki-67。我们对免疫荧光阳性细胞进行计数，并根据浸润区域将它们分为三组，即上皮组、上皮下组和血管周围组。
在机器学习方面，决策树算法表明，上皮下HNE阳性细胞数量、Lund-Mackay（LM）评分和内型（嗜酸性或非嗜酸性）是CRSwNP患者手术结果的最重要预测因素。此外，随机森林算法显示，在对基尼指数或每个因素的准确性的平均下降进行排序后，与手术结果相关的前三个因素是LM评分、年龄和上皮下HNE阳性细胞数量。在细胞增殖方面，免疫荧光分析显示，难治性CRSwNP患者上皮下区域的Ki-67/HNE双阳性和Bcl-2/HNE双阳性细胞显著增加。
我们的机器学习方法和免疫荧光分析表明，鼻息肉组织中的上皮下中性粒细胞Ki-67表达较高，可作为预测CRSwNP患者手术结果的细胞生物标志物。
