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Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC).

Methods

In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.

Results

Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV9-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV9-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05).

Conclusions

CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery.

Key Points

• Compared with GTV, GPTV 3 , and GPTV 6 radiomics models, GPTV 9 radiomics model showed better prediction performance for LVI status in NSCLC.

• The radiomics nomogram based on GPTV 9 radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model.

• The radiomics nomogram had a wider scope of clinical application.

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Abbreviations

AUC:

Area under curve

CT:

Computed tomography

GPTV:

Gross peritumoral tumor volume

GTV:

Gross tumor volume

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

LVI:

Lymphovascular invasion

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

TCIA:

The Cancer Imaging Archive

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Funding

This study has received funding from Key Project of Nantong Science and Technology Bureau of China (grant number MS22021047) and Clinical Research Plan of SHDC (grant number SHDC2020CR3080B).

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Authors and Affiliations

  1. Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, 226361, People’s Republic of China

    Qiaoling Chen, Ting Xue, Hui Peng, Manman Li & Feng Feng

  2. Key Laboratory of Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, 226361, People’s Republic of China

    JingJing Shao

  3. GE Healthcare China, Shanghai, 210000, People’s Republic of China

    Shaofeng Duan

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  1. Qiaoling Chen
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  2. JingJing Shao
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  3. Ting Xue
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  6. Shaofeng Duan
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  7. Feng Feng
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Correspondence to Feng Feng.

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The scientific guarantor of this publication is Feng Feng.

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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 (Shaofeng Duan) has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Qiaoling Chen and JingJing Shao are the first authors.

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Chen, Q., Shao, J., Xue, T. et al. Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer. Eur Radiol 33, 947–958 (2023). https://doi.org/10.1007/s00330-022-09109-3

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  • DOI: https://doi.org/10.1007/s00330-022-09109-3

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