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Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC).

Methods

One training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient’s 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated.

Results

The radiomics signatures were significantly associated with NSCLC patients’ survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients’ survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one.

Conclusions

The radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine.

Key Points

• We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance.

• The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features.

• Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients’ survival compared with clinical TNM staging alone.

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Abbreviations

ANOVA:

Analysis of variance

C-index:

The Harrell concordance index

CCC:

Lin’ concordance correlation coefficient

CT:

Computed tomography

GLRLM:

Gray-level run length matrix

HR:

Hazard ratio

LASSO:

Least absolute shrinkage and selection operator

NSCLC:

Non-small cell lung cancer

Rad-score:

Radiomics score

TNM:

Tumor-node-metastasis

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Funding

This study has received funding by the National Key Research and Development Program of China (Grant No. 2017YFA0205202) and partially funded by the National Natural Science Foundation of China (Grant No. U1401255 and 61672422).

Author information

Authors and Affiliations

  1. School of Life Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, China

    Lifeng Yang, Jingbo Yang & Liyu Huang

  2. Department of Radiology, Medical Center Boulevard, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

    Xiaobo Zhou & Weiling Zhao

  3. Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, China

    Tao Wang

  4. Department of Radiology, Guangdong General Hospital, Guangzhou, 510080, Guangdong, China

    Jian Zhuang

  5. Key Laboratory of Molecular Imaging, Chinese Academy of Science, Beijing, 100080, China

    Jie Tian

Authors
  1. Lifeng Yang
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  2. Jingbo Yang
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  3. Xiaobo Zhou
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  4. Liyu Huang
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  5. Weiling Zhao
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  6. Tao Wang
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  7. Jian Zhuang
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  8. Jie Tian
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Corresponding authors

Correspondence to Liyu Huang or Jie Tian.

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Guarantor

The scientific guarantor of this publication is Liyu Huang.

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

Jun Li kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was not required for this study because the data were obtained from the collection of NSCLC-Radiomics in The Cancer Imaging Archive (TCIA, URL: http://www.cancerimagingarchive.net/).

Ethical approval

Institutional Review Board approval was not required because the data were obtained from the collection of NSCLC-Radiomics in The Cancer Imaging Archive (TCIA, URL: http://www.cancerimagingarchive.net/). It is a public database and we added the reference of data in our study.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Yang, L., Yang, J., Zhou, X. et al. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. Eur Radiol 29, 2196–2206 (2019). https://doi.org/10.1007/s00330-018-5770-y

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  • DOI: https://doi.org/10.1007/s00330-018-5770-y

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