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Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT

  • Chest
  • Published:
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Abstract

Objectives

This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT.

Methods

A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately.

Results

Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts.

Conclusions

We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment.

Key Points

• Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions.

• Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to privacy but are available from the corresponding author on reasonable request.

Abbreviations

AUC:

Area under the curve

CT:

Computed tomography

HU :

Hounsfield Unit

IQR :

Inter-quartile range

NPV :

Negative predictive values

PPD:

Purified protein derivative

PPV:

Positive predictive values

SD :

Standard deviation

SHSMU:

Second Hospital of Shanxi Medical University

TMUCIH:

Tianjin Medical University Cancer Institute and Hospital

VA:

Veterans Affairs

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Acknowledgements

I wish to devote this paper to my newly born daughter Cui Can, who illuminates my world.

Funding

This work was supported by the Chinese National Key Research and Development Project (Grant No. 2021YFC2500400 and Grant No.2021YFC2500402), Tianjin Key Medical Discipline(Specialty) Construction Project (TJYXZDXK-009A).

Author information

Author notes

  1. Xiaonan Cui, Sunyi Zheng, and Wenjia Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People’s Republic of China

    Xiaonan Cui, Shuxuan Fan, Weijie Zhu & Zhaoxiang Ye

  2. Artificial Intelligence and Biomedical Image Analysis Lab, School of Engineering, Westlake University, Hangzhou, People’s Republic of China

    Sunyi Zheng

  3. Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, People’s Republic of China

    Sunyi Zheng

  4. Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China

    Wenjia Zhang, Feipeng Song & Xu Liu

  5. Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, People’s Republic of China

    Jing Wang

Authors
  1. Xiaonan Cui
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Corresponding author

Correspondence to Zhaoxiang Ye.

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Guarantor

The scientific guarantor of this publication is Dr. Zhaoxiang 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

One of the authors (Jing Wang) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

The institutional ethics committee board of Tianjin Medical University Cancer Institute & Hospital approved this study (No. bc2021327).

Methodology

• retrospective

• diagnostic study

• multicenter study

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Cui, X., Zheng, S., Zhang, W. et al. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT. Eur Radiol 33, 4734–4745 (2023). https://doi.org/10.1007/s00330-023-09432-3

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  • DOI: https://doi.org/10.1007/s00330-023-09432-3

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