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Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

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Abstract

Objectives

This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features.

Methods

The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results.

Results

The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035–1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911–18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438–5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583–0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806–0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808–0.896) (sensitivity = 74.3%, specificity = 85.8%).

Conclusion

The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC.

Key Points

• The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583–0.699), 0.851 (95% CI 0.806–0.896), and 0.852 (95% CI 0.808–0.896).

• Tumor size, density, and lobulation were independent predictive markers for high-grade patterns.

• The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.

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Abbreviations

AC:

Adenocarcinoma

AI:

Artificial intelligence

ATS:

American Thoracic Society

ERS:

European Respiratory Society

HGP:

High-grade pattern

HRCT:

High-resolution CT

IAC:

Invasive adenocarcinoma

IASLC:

International Association for the Study of Lung Cancer

ICC:

Intraclass correlation coefficient

LNM:

Lymph node metastasis

ML:

Machine learning

n-HGP:

Non-high-grade pattern

SN:

Solid nodule

SSN:

Subsolid nodule

STAS:

Tumor spread through air spaces

TNM:

Tumor node metastasis

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Funding

This work was supported by Major Research Plan of the National Natural Science Foundation of China (Grant No. 92059206), National Science Foundation of China (82071990), National Science Foundation of China (81571629), National Science Foundation of China (81301218), Project of Shanghai Science and Technology Commission (19411965200), Policy Guidance Project of Major Science and Technology Plan for Social Development of Xiaoshan District (No. 2021309), Hangzhou Agricultural and Social Development Scientific Research Guidance Project (20220919Y078), and Zhejiang Provincial Health Commission Youth Innovation Project (2023RC252).

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Author notes

  1. Hao Dong and Le-Kang Yin contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Department of Radiology, First People’s Hospital of Xiaoshan District, Zhejiang, Hangzhou, China

    Hao Dong, Yong-Gang Qiu, Xin-Bin Wang & Cun-Cheng Lou

  2. Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Le-Kang Yin & Xiao-Dan Ye

  3. Shanghai Institute of Medical Imaging, Shanghai, China

    Le-Kang Yin & Xiao-Dan Ye

  4. Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China

    Le-Kang Yin & Xiao-Dan Ye

  5. Department of Pathology, First People’s Hospital of Xiaoshan District, Zhejiang, Hangzhou, China

    Jun-Jie Yang

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Correspondence to Xiao-Dan Ye.

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Guarantor

The scientific guarantor of this publication is Xiao-dan Ye.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Two of the authors (Hao Dong and Xiao-Dan Ye) have significant statistical expertise.

Informed consent

Our hospital ethics committee approved this retrospective study and waived patient informed consent.

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Institutional review board approval was obtained.

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• retrospective

• diagnostic study

• performed at two institutions

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Dong, H., Yin, LK., Qiu, YG. et al. Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study. Eur Radiol 33, 3931–3940 (2023). https://doi.org/10.1007/s00330-022-09379-x

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