基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值

doi:10.19401/j.cnki.1007-3639.2023.08.005

中国癌症杂志 ›› 2023, Vol. 33 ›› Issue (8): 768-775.doi: 10.19401/j.cnki.1007-3639.2023.08.005

基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值

董浩¹(), 邱勇刚¹, 汪鑫斌¹, 杨俊杰², 楼存诚¹, 叶晓丹³()

1.杭州市萧山区第一人民医院放射科，浙江杭州 311200
2.杭州市萧山区第一人民医院病理科，浙江杭州 311200
3.复旦大学附属中山医院放射科，上海 200032
4.上海医学影像研究所，上海 200032
5.复旦大学附属中山医院肿瘤中心，上海 200032

收稿日期:2023-02-03 修回日期:2023-05-12 出版日期:2023-08-30 发布日期:2023-09-01
通信作者: 叶晓丹（ORCID: 0000-0003-3059-2740），主任医师。 E-mail:yuanyxd@163.com
作者简介:董浩（ORCID: 0000-0003-3345-5506），硕士研究生在读。
基金资助:
国家自然科学基金(81571629);国家自然科学基金(82071990);上海市科学技术委员会项目(19411965200);浙江省医药卫生科技计划项目(2023RC252);杭州市农业与社会发展科研引导项目(20220919Y078);杭州市萧山区社会发展重大科技计划政策引导项目(2021309)

Predictive value of logistic regression model based on high-resolution CT signs for high-grade pattern in stage ⅠA lung adenocarcinoma

DONG Hao¹(), QIU Yonggang¹, WANG Xinbin¹, YANG Junjie², LOU Cuncheng¹, YIN Lekang³(), YE Xiaodan³^,⁴^,⁵()

1. Department of Radiology, First People's Hospital of Xiaoshan District, Hangzhou 311200, Zhejiang Province, China
2. Department of Pathology, First People's Hospital of Xiaoshan District, Hangzhou 311200, Zhejiang Province, China
3. Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
4. Shanghai Institute of Medical Imaging, China
5. Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai 200032,China

Received:2023-02-03 Revised:2023-05-12 Published:2023-08-30 Online:2023-09-01
Contact: YIN Lekang, YE Xiaodan E-mail:yuanyxd@163.com

摘要/Abstract

摘要：

背景与目的：研究表明，当存在高级别组织学模式（微乳头状和实体模式）时，肺腺癌患者的预后明显较差，往往需要更积极的治疗方式，术前确定浸润性肺腺癌中是否存在任何高级别模式（high-grade pattern，HGP）可以帮助预测患者的预后并确定治疗策略。本研究旨在建立基于高分辨率计算机体层成像（computed tomography，CT）征象的logistic回归模型预测ⅠA期肺腺癌的HGP。方法：回顾性分析经病理学检查证实为ⅠA期肺腺癌的443例患者（445个病灶）的临床、病理学及影像学资料。根据病理学检查结果有无HGP将445个病灶分成两组：HGP组（n=88个）和非HGP（non-HGP，n-HGP）组（n=357个）。患者的临床病理学资料包括年龄、性别、吸烟史、肿瘤位置、分期及病理生长方式等。CT 影像学上观察病灶大小、密度、形状、毛刺征、分叶征、空泡征、空气支气管征、胸膜凹陷征等。两组间定量参数比较采用Mann-Whitney U检验，计数资料采用χ²检验或Fisher确切概率法。采用单因素结合多因素logistic回归分析筛选独立预测因子，并根据多因素logistic回归分析结果分别构建临床模型、CT模型及临床-CT模型，模型间诊断效能的比较采用Delong检验。结果：单因素分析中HGP组与n-HGP组之间年龄、性别、吸烟史、肿瘤大小、密度、形状、毛刺、分叶征、胸膜牵拉差异有统计学意义（P<0.05），多因素logistic 回归分析结果显示肿瘤大小（P = 0.040；OR = 1.063，95% CI：1.003 ~ 1.126）、密度（P<0.001；OR = 8.249，95% CI：4.244 ~ 16.034）、分叶征（P = 0.001；OR = 3.101，95% CI：1.598 ~ 6.021）是HGP的独立预测因素，临床模型、CT模型、临床-CT模型预测HGP的曲线下面积（area under curve，AUC）值分别为0.634、0.838及0.834。结论：肿瘤大小、密度与分叶征是ⅠA期肺腺癌HGP的独立预测因子。基于高分辨率CT征象的logistic回归模型具有较好的诊断效能，可以为临床诊断及制订外科治疗方案提供一定的参考依据。

关键词: 肺腺癌, 高级别模式, 预测模型, 高分辨率CT

Abstract:

Background and purpose: Studies have shown that when high-grade histological patterns (micropapillary and solid patterns) are present, patients with lung adenocarcinoma have a significantly poorer prognosis and often require more aggressive treatment modalities, and preoperative determination of the presence of any high-grade patterns (HGP) in invasive lung adenocarcinoma can help predict patient prognosis and determine treatment strategies. The aim of the study was to establish a logistic regression model based on high-resolution CT signs to predict the HGP of stage ⅠA lung adenocarcinoma. Methods: The clinical, pathological and imaging data of 443 patients (445 lesions) with stage ⅠA lung adenocarcinoma confirmed by pathology diagnosis from First People's Hospital of Xiaoshan District (Oct. 2018 to Mar. 2021) and Zhongshan Hospital of Fudan University (Jan. 2018 to Dec. 2020) were retrospectively analyzed. The 445 lesions were divided into two groups according to the presence or absence of HGP in pathological findings: HGP (n=88) and non-HGP (n-HGP) (n=357). The clinical and pathological data of the patients included age, gender, smoking history, tumor location, stage and pathological growth pattern. On CT imaging, the size, density, shape, burr sign, lobulation sign, vacuole sign, air bronchus sign and pleural depression sign were observed. Mann-Whitney U test was used to compare quantitative parameters between the two groups, and χ² test or Fisher's exact test was used for enumeration of data. The independent predictors were screened by univariate combined with multivariate logistic regression analysis, and the clinical model, CT model and clinical-CT model were constructed according to the results of multivariate logistic regression analysis. DeLong test was used to compare the diagnostic efficacy between models. Results: In the univariate analysis, there were significant differences in age, gender, smoking history, tumor size, density, shape, burr, lobulation sign and pleural traction between the HGP group and the n-HGP group (P<0.05). Multivariate logistic regression analysis showed tumor size (P=0.04; OR=1.063, 95% CI: 1.003-1.126), density (P<0.001; OR=8.249, 95% CI: 4.244-16.034), lobulation sign (P=0.001; OR=3.101, 95% CI: 1.598-6.021) were independent predictors of HGP, and the area under curve (AUC) values of clinical model, CT model and clinical-CT model for predicting HGP were 0.634, 0.838 and 0.834, respectively. Conclusion: Tumor size, density and lobulation sign are independent predictors of HGP in stage ⅠA lung adenocarcinoma. The logistic regression model based on high-resolution CT signs has good diagnostic performance and can provide a certain reference for clinical diagnosis and surgical treatment.

Key words: Lung adenocarcinoma, High-grade pattern, Prediction model, High-resolution CT

中图分类号:

R734.2

董浩, 邱勇刚, 汪鑫斌, 杨俊杰, 楼存诚, 叶晓丹. 基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值[J]. 中国癌症杂志, 2023, 33(8): 768-775.

DONG Hao, QIU Yonggang, WANG Xinbin, YANG Junjie, LOU Cuncheng, YIN Lekang, YE Xiaodan. Predictive value of logistic regression model based on high-resolution CT signs for high-grade pattern in stage ⅠA lung adenocarcinoma[J]. China Oncology, 2023, 33(8): 768-775.

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参考文献 24

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Item	Total	HGP (n=88)	n-HGP (n=357)	P value
Age/year	58±11	57±12	61±9	0.037^△
Gender				0.015^#
Male	158	41	117
Female	287	47	240
Smoking history				0.003^#
No	390	69	321
Yes	55	19	36
Location				0.401^#
Left upper lobe	108	22	86
Left lower lobe	69	17	52
Right upper lobe	148	29	119
Right middle lobe	36	9	27
Right lower lobe	84	11	73
Clinical T staging				<0.001^#
T_1a	121	10	111
T_1b	258	54	204
T_1c	66	24	42
Pure or mix pattern				0.008^*
Pure	27	0	27
Mix	418	88	330
Major histological patterns				<0.001^*
Lepidic predominant	52	1	51
Acinar predominant	348	71	280
Papillary predominant	32	6	26
Micropapillary	4	4	0
Solid predominant	9	9	0

Item	Case n	SE	Kappa value	95% CI
Density	443	0.012	0.983	0.955-1.000
Shape	440	0.013	0.972	0.942-0.994
Lobulation	422	0.022	0.892	0.847-0.933
Spiculation	430	0.018	0.924	0.886-0.956
Vacuole	427	0.048	0.791	0.685-0.837
Air bronchogram	435	0.015	0.955	0.926-0.982
Pleural indentation	442	0.008	0.986	0.968-1.000

Item	Total	HGP (n=88)	n-HGP (n=357)	P value
Tumor size D/mm	14±5	17±5	14±5	<0.001^△
Density				<0.001^#
SSN	377	45	332
SN	68	43	25
Shape				0.003^#
Round or oval	129	14	115
Irregular	316	74	242
Lobulation				<0.001^#
No	287	25	262
Yes	158	63	95
Spiculation				<0.001^#
No	296	32	264
Yes	149	56	93
Vacuole				0.764^#
No	406	81	325
Yes	39	7	32
Air bronchogram				0.162^#
No	242	42	200
Yes	203	46	157
Pleural indentation				<0.001^#
No	256	33	223
Yes	189	55	134

Item	AUC (95% CI)	Sensitivity/%	Specificity/%
CT model	0.838 (0.788-0.889)	70.5	84.9
Tumor size	0.695 (0.636-0.755)	75.0	56.4
Density	0.709 (0.640-0.778)	48.9	97.0
Shape	0.582 (0.519-0.644)	84.1	32.2
Lobulation	0.725 (0.664-0.785)	71.6	73.4
Spiculation	0.688 (0.624-0.752)	63.6	73.9
Pleural indentation	0.625 (0.560-0.690)	62.5	62.5

基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值

Predictive value of logistic regression model based on high-resolution CT signs for high-grade pattern in stage ⅠA lung adenocarcinoma

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