Wang Mingjie, Hou Lizhen, Zhou Bing, Li Yunchuan, Cui Shunjiu, Huang Qian, Sun Yan
Department of Otolaryngology Head and Neck Surgery,Beijing Tongren Hospital,Capital Medical University,Key Laboratory of Otolaryngology Head and Neck Surgery,Ministry of Education(Capital Medical University.
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2021 Jul;35(7):627-632. doi: 10.13201/j.issn.2096-7993.2021.07.011.
To explore the risk factors of malignant transformation of sinonasal inverted papilloma (SNIP), and to improve the accuracy of preoperative diagnosis of tumor. The clinical data of 89 patients with sinonasal inverted papilloma (SNIP group, n=60) and malignant transformation of sinonasal inverted papilloma (IP-SCC, =29) were analyzed retrospectively. Clinical symptoms, medical history, endoscopic examination, characteristic of sinonasal CT scan and MR imaging were collected and compared between two groups. Then the indicators with significant differences between the two groups were used for binary logistic regression analysis. The logistic regression model was established to predict the malignant transformation risk factors of inverted papilloma and the prediction ability of the regression model was estimated. The significant differences between the two groups were: symptoms, including nasal obstruction, purulent mucus, blood in the nasal discharge; long-term smoking history; tumor attached with purulent mucus; CT scan showing bone destruction of the orbital wall and skull base; MR Imaging showing convoluted cerebriform pattern (CCP) sign, intraorbital involvement, and dural enhancement of the skull base. The results of logistic regression analysis showed that the risk factors of malignant transformation of SNIP were blood in the nasal discharge, long-term smoking history, tumor with purulent discharge, orbital wall destruction on CT scan, disappearance of CCP and orbital involvement on MRI. The accuracy rate of regression model for predicting malignant transformation of IP is 75.0%, and the accuracy rate for benign inverted papilloma is 96.7%, and the overall accuracy of the model is 89.8%. The risk factors for predicting malignant transformation of SNIP are blood in the nasal discharge, long-term smoking history, tumor with purulent discharge, orbital wall destruction on CT scan, and disappearance of CCP sign and orbital involvement on MRI. It's necessary to analyze all of clinical factors in order to improve the accuracy of preoperative diagnosis of sinonasal inverted papilloma.
探讨鼻腔鼻窦内翻性乳头状瘤(SNIP)恶变的危险因素,提高肿瘤术前诊断的准确性。回顾性分析89例鼻腔鼻窦内翻性乳头状瘤患者(SNIP组,n = 60)及鼻腔鼻窦内翻性乳头状瘤恶变(IP - SCC组,n = 29)的临床资料。收集两组患者的临床症状、病史、鼻内镜检查、鼻窦CT扫描及磁共振成像(MR)特征并进行比较。然后将两组间有显著差异的指标进行二元logistic回归分析。建立logistic回归模型以预测内翻性乳头状瘤恶变的危险因素,并评估回归模型的预测能力。两组间的显著差异有:症状,包括鼻塞、脓性黏液、涕中带血;长期吸烟史;肿瘤附着脓性黏液;CT扫描显示眶壁及颅底骨质破坏;MR成像显示脑回样(CCP)征、眶内受累及颅底硬膜强化。logistic回归分析结果显示,SNIP恶变的危险因素为涕中带血、长期吸烟史、肿瘤有脓性分泌物、CT扫描眶壁破坏、MR成像CCP征消失及眶内受累。回归模型预测IP恶变的准确率为75.0%,预测良性内翻性乳头状瘤的准确率为96.7%,模型总体准确率为89.8%。预测SNIP恶变的危险因素为涕中带血、长期吸烟史、肿瘤有脓性分泌物、CT扫描眶壁破坏、MR成像CCP征消失及眶内受累。为提高鼻腔鼻窦内翻性乳头状瘤术前诊断的准确性,有必要综合分析所有临床因素。