Lei Wang1, Weihua Wu1, Yunqian Hu2, Jiajun Teng3, Runbo Zhong3, Baohui Han3, Jiayuan Sun4. 1. Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China. 2. Department of Respiration, Shanghai East Hospital, Shanghai Tongji University, Shanghai, China. 3. Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China. 4. Department of Endoscopy and Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China. Electronic address: jysun1976@163.com.
Abstract
BACKGROUND: Intrathoracic lymph node sampling by endobronchial ultrasonography-guided transbronchial needle aspiration (EBUS-TBNA) has become a standard of care in staging lung cancer. This study aimed to assess the efficacy of utilizing the individual sonographic features of lymph nodes for predicting metastasis in lung cancer patients. METHODS: From January 2010 to May 2012, we retrospectively studied 459 metastatic lymph nodes in 298 lung cancer patients and 176 reactive lymph nodes in 90 patients with nonspecific inflammation. Digital videos of the lymph nodes were obtained during EBUS-TBNA and categorized according to the following characteristics: size, shape, margin, central hilar structure, echogenicity, necrosis sign, matting, calcification, and vascular patterns. The sonographic findings were compared with the final pathology results and clinical follow-up. RESULTS: Multivariate analysis revealed five independent predictive factors for lymph node metastasis: long axis, round shape, absence of central hilar structure, presence of matting, and nonhilar vascular pattern perfusion. An aggregate score system based on the odds ratio was developed and reduced the criteria to four factors: presence of matting, nonhilar vascular pattern perfusion, absence of central hilar structure, and round shape. It showed at least two of four independent predictive factors could obtain the best performance for predicting metastatic lymph nodes and yield a high sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of 93.03%, 55.68%, 84.55%, 75.38%, and 82.68%, respectively. CONCLUSIONS: Sonographic features of the EBUS images can differentiate metastatic from reactive lymph nodes, so it may help predict intrathoracic lymph nodes metastasis in lung cancer patients.
BACKGROUND: Intrathoracic lymph node sampling by endobronchial ultrasonography-guided transbronchial needle aspiration (EBUS-TBNA) has become a standard of care in staging lung cancer. This study aimed to assess the efficacy of utilizing the individual sonographic features of lymph nodes for predicting metastasis in lung cancerpatients. METHODS: From January 2010 to May 2012, we retrospectively studied 459 metastatic lymph nodes in 298 lung cancerpatients and 176 reactive lymph nodes in 90 patients with nonspecific inflammation. Digital videos of the lymph nodes were obtained during EBUS-TBNA and categorized according to the following characteristics: size, shape, margin, central hilar structure, echogenicity, necrosis sign, matting, calcification, and vascular patterns. The sonographic findings were compared with the final pathology results and clinical follow-up. RESULTS: Multivariate analysis revealed five independent predictive factors for lymph node metastasis: long axis, round shape, absence of central hilar structure, presence of matting, and nonhilar vascular pattern perfusion. An aggregate score system based on the odds ratio was developed and reduced the criteria to four factors: presence of matting, nonhilar vascular pattern perfusion, absence of central hilar structure, and round shape. It showed at least two of four independent predictive factors could obtain the best performance for predicting metastatic lymph nodes and yield a high sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of 93.03%, 55.68%, 84.55%, 75.38%, and 82.68%, respectively. CONCLUSIONS: Sonographic features of the EBUS images can differentiate metastatic from reactive lymph nodes, so it may help predict intrathoracic lymph nodes metastasis in lung cancerpatients.
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