支气管腔内超声图像评分系统在肺结节良恶性诊断中的应用

doi:10.3877/cma.j.issn.1674-6902.2021.02.005

中华肺部疾病杂志(电子版) ›› 2021, Vol. 14 ›› Issue (02) : 158 -163. doi: 10.3877/cma.j.issn.1674-6902.2021.02.005

论著

支气管腔内超声图像评分系统在肺结节良恶性诊断中的应用

王梦¹, 陈众众¹, 刘颖¹, 闫文锦¹, 李一然¹, 王玉秀¹, 许文景¹, 姚汉清¹, 朱湘平¹, 徐兴祥¹, 闵凌峰¹^,^†(

)

^1. 225001　扬州，扬州大学医学院附属苏北人民医院呼吸与危重症医学科·南京大学医学院附属苏北医院·大连医科大学附属苏北医院

收稿日期:2020-11-25 出版日期:2021-04-25
通信作者: 闵凌峰
基金资助:
国家自然科学基金资助项目(81870033)

Application value of the scoring system based on endobronchial ultrasound image in the diagnosis of pulmonary nodules

Meng Wang¹, Zhongzhong Chen¹, Ying Liu¹, Wenjin Yan¹, Yiran Li¹, Yuxiu Wang¹, Wenjing Xu¹, Hanqing Yao¹, Xiangping Zhu¹, Xingxiang Xu¹, Lingfeng Min¹^,^†()

^1. The Department of Respiratory and Critical Care Medicine of North Jiangsu People′s Hospital affiliated to Yangzhou University Medical College, North Jiangsu Hospital affiliated to Nanjing University Medical College, North Jiangsu Hospital affiliated to Dalian Medical University, Yangzhou 225001, China

Received:2020-11-25 Published:2021-04-25
Corresponding author: Lingfeng Min

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引用本文：

王梦, 陈众众, 刘颖, 闫文锦, 李一然, 王玉秀, 许文景, 姚汉清, 朱湘平, 徐兴祥, 闵凌峰. 支气管腔内超声图像评分系统在肺结节良恶性诊断中的应用[J]. 中华肺部疾病杂志(电子版), 2021, 14(02): 158-163.

Meng Wang, Zhongzhong Chen, Ying Liu, Wenjin Yan, Yiran Li, Yuxiu Wang, Wenjing Xu, Hanqing Yao, Xiangping Zhu, Xingxiang Xu, Lingfeng Min. Application value of the scoring system based on endobronchial ultrasound image in the diagnosis of pulmonary nodules[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2021, 14(02): 158-163.

目的

建立基于支气管腔内超声(EBUS)图像的评分系统从而判别肺结节良恶性。

方法

回顾性纳入自2018年5月1日至2020年3月1日期间于苏北人民医院气管镜室行气管镜检查的患者资料，所有患者胸部计算机断层扫描(computer tomography, CT)检查见肺部≤3 cm的肺结节而行常规支气管镜检查未见明显异常。使用超声探头引导下在病变处进行活检与刷检协助诊断，未能明确诊断者行进一步侵袭性操作或治疗后随访观察至少3个月获得最终诊断。分析镜下超声图像形态特征，包括病灶外形、边缘、边界、内部回声强弱、内部回声同质或异质、支气管充气征、不规则无回声区、同心圆影8种不同EBUS图像特点与病灶良恶性的关系，建立简易评分系统，使用SPSS软件分析处理数据。

结果

114例肺结节患者中，良性病变65例，恶性病变49例。EBUS图像中的三种图像特征，包括病灶圆形或类圆形外形、边缘不连续、病灶异质性，差异有统计学意义(P<0.05)；根据建立的简易评分系统，绘制ROC曲线，当评分以7为界点时，敏感度(65.3%)和特异度(78.5%)最高，以该点为最佳诊断点；当评分≥7时，诊断肺恶性病变的准确率较高。计算Kappa一致性：系数为0.441(95%CI为0.274～0.607，P<0.01)，具有中等强度一致性。

结论

EBUS图像特征可用于鉴别肺结节良恶性，基于该图像的评分系统在鉴别肺结节良恶性中有较好的应用价值。

关键词: 支气管腔内超声, 图像形态特征, 肺结节, 良恶性鉴别

Objective

To attempt to develop a simple scoring system based on images of endobronchial ultrasound (EBUS) to discriminate between benign and malignant pulmonary nodules.

Methods

data of patients who undergone bronchoscopy in the Bronchoscopy Room of North Jiangsu People′s Hospital during May 1, 2018 and March 1, 2020 were retrospectively included. all of the patients′chest CT scan showed pulmonary nodules ≤3 cm, while routine bronchoscopy showed no obvious abnormalities. Ultrasound probe guided biopsy and brush examination were used to assist in the diagnosis of the disease. Patients with no definite diagnosis were further subjected to invasive surgeries or observations after the treatment for at least 3 months to obtain the final diagnosis. Eight different EBUS image characteristics including lesion shape, margin, border, internal echo strength, homogeneous, or heterogeneous internal echoe, air bronchogram, irregular anechoic area, concentric circles were observed, and the relationship between these image characteristics and the nature of pulmonary nodules were analyzed, a simple scoring system was established, datas were analyzed using SPSS software.

Results

Among 114 patients with pulmonary nodules, 65 were benign and 49 were malignant. Three image features in EBUS images, including round or round shape of lesions, discontinuous margin, and heterogeneous echogenicity, with statistically significant (P<0.05). According to the simple scoring system established, ROC curve was drawn. When the score was set at 7, the sensitivity (65.3%) and specificity (78.5%) were the highest, and this was the best point for diagnosis. When the score is≥7, the diagnosis of pulmonary malignant lesions was more accurate. Kappa consistency was calculated with a coefficient of 0.441 (95%CI 0.274-0.607, P<0.01), showing moderate strength consistency.

Conclusion

EBUS can provide image characteristic information to differentiate the nature of pulmonary nodules, and the scoring system based on the images has a good application value in differentiating benign from malignant pulmonary nodules.

Key words: Endobronchial ultrasound, Image characteristics, Pulmonary nodules, Differentiate the nature of pulmonary nodules

图1 支气管腔内超声回声特征图片

表1 肺结节的8种超声回声特征单因素分析

回声特征		良性	恶性	P值
病灶外形^a	0	37	17	0.019
	1	28	32
病灶边缘^a	0	43	18	0.002
	1	22	31
病灶边界	0	36	22	0.268
	1	29	27
病灶内部回声强弱	0	32	16	0.076
	1	33	33
病变内部回声同质或异质^a	0	44	13	0.000
	1	21	36
病灶内支气管充气征	0	25	11	0.069
	1	40	38
病灶内不规则无回声区	0	49	30	0.105
	1	16	19
同心圆影	0	30	16	0.146
	1	35	33

表1 肺结节的8种超声回声特征单因素分析

回声特征		良性	恶性	P值
病灶外形^a	0	37	17	0.019
	1	28	32
病灶边缘^a	0	43	18	0.002
	1	22	31
病灶边界	0	36	22	0.268
	1	29	27
病灶内部回声强弱	0	32	16	0.076
	1	33	33
病变内部回声同质或异质^a	0	44	13	0.000
	1	21	36
病灶内支气管充气征	0	25	11	0.069
	1	40	38
病灶内不规则无回声区	0	49	30	0.105
	1	16	19
同心圆影	0	30	16	0.146
	1	35	33

表2 超声特征对肺部良恶性结节的预测价值

回声表现	敏感性(%)	特异性(%)	阳性预测值(%)	阴性预测值(%)
外形^a	65.3	56.9	53.3	68.5
边缘^b	63.3	66.2	58.5	70.5
异质性^c	73.5	67.7	63.2	77.2
外形或边缘或异质性	91.8	24.6	47.9	80.0
外形和边缘和异质性	36.7	95.4	85.7	66.7

表2 超声特征对肺部良恶性结节的预测价值

回声表现	敏感性(%)	特异性(%)	阳性预测值(%)	阴性预测值(%)
外形^a	65.3	56.9	53.3	68.5
边缘^b	63.3	66.2	58.5	70.5
异质性^c	73.5	67.7	63.2	77.2
外形或边缘或异质性	91.8	24.6	47.9	80.0
外形和边缘和异质性	36.7	95.4	85.7	66.7

表3 根据评分系统计算各例肺结节总分

病理类型	例数	图像评分
病理类型	例数	1	2	3	4	5	6	7	8	9	10
良性	65	5	6	9	10	11	10	9	3	2	0
恶性	49	0	0	4	1	5	7	10	13	6	3
总　计	114	5	6	13	11	16	17	19	16	8	3

表3 根据评分系统计算各例肺结节总分

病理类型	例数	图像评分
病理类型	例数	1	2	3	4	5	6	7	8	9	10
良性	65	5	6	9	10	11	10	9	3	2	0
恶性	49	0	0	4	1	5	7	10	13	6	3
总　计	114	5	6	13	11	16	17	19	16	8	3

图2 评分系统判别肺结节性质的ROC曲线

表4 评判方程所得结果与病理结果比较

病理类型	例数	图像评分
病理类型	例数	<7	≥7	P值
良性	65	51	14	<0.01
恶性	49	17	32
总计	114	68	46

表4 评判方程所得结果与病理结果比较

病理类型	例数	图像评分
病理类型	例数	<7	≥7	P值
良性	65	51	14	<0.01
恶性	49	17	32
总计	114	68	46

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