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

doi:10.19401/j.cnki.1007-3639.2023.08.005

Abstract

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

CLC Number:

R734.2

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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References 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