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Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma

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

Abstract

Objectives

To develop and validate an optimal model based on the 1-mm-isotropic-3D contrast-enhanced StarVIBE MRI sequence combined with clinical risk factors for predicting survival in patients with esophageal squamous cell carcinoma (ESCC).

Methods

Patients with ESCC at our institution from 2015 to 2017 participated in this retrospective study based on prospectively acquired data, and were randomly assigned to training and validation groups at a ratio of 7:3. Random survival forest (RSF) and variable hunting methods were used to screen for radiomics features and LASSO-Cox regression analysis was used to build three models, including clinical only, radiomics only and combined clinical and radiomics models, which were evaluated by concordance index (CI) and calibration curve. Nomograms and decision curve analysis (DCA) were used to display intuitive prediction information.

Results

Seven radiomics features were selected from 434 patients, combined with clinical features that were statistically significant to construct the predictive models of disease-free survival (DFS) and overall survival (OS). The combined model showed the highest performance in both training and validation groups for predicting DFS ([CI], 0.714, 0.729) and OS ([CI], 0.730, 0.712). DCA showed that the net benefit of the combined model and of the clinical model is significantly greater than that of the radiomics model alone at different threshold probabilities.

Conclusions

We demonstrated that a combined predictive model based on MR Rad-S and clinical risk factors had better predictive efficacy than the radiomics models alone for patients with ESCC.

Key Points

• Magnetic resonance–based radiomics features combined with clinical risk factors can predict survival in patients with ESCC.

• The radiomics nomogram can be used clinically to predict patient recurrence, DFS, and OS.

• Magnetic resonance imaging is highly reproducible in visualizing lesions and contouring the whole tumor.

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Abbreviations

AIC:

Akaike Information Criterion

AUC:

Area under curve

CI:

Concordance index

DCA:

Decision curve analysis

DFS:

Disease-free survival

EC:

Esophageal cancer

ESCC:

Esophageal squamous cell carcinoma

EUS:

Endoscopic ultrasound

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

LASSO:

Least absolute shrinkage and selection operator

NGTDM:

Neighboring gray tone difference matrix

OS:

Overall survival

pCR:

Pathological complete response

Rad-S:

Radiomics scores

RF:

Random forest

ROC:

Receiver operating characteristic

ROI:

Region of interest

RSF:

Random survival forest

SUR:

Standard uptake ratio

SUV:

Standard uptake value

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Funding

This study has received funding by the Projects of the General Programs of the National Natural Science Foundation of China (No.81972802), Natural Science Foundation of Henan Province (No.182300410355), Henan Province Medical Science and Technology Research Program Provincial Department to jointly build key projects (No.SBGJ202002021), Special funding of the Henan Health Science and Technology Innovation Talent Project (No.YXKC2020011), Henan Province focuses on research and development and promotion (No.212102310133), Innovation Scientists and Technicians Troop Construction Projects of Henan Province (No.20160913), the Province-Ministry Co-construction Project of Health Committee of Henan Province (No.SB201901108) and Youth Talent Project of Henan Youth Health Science and Technology Innovation Foundation (No.YXKC2020022).

Author information

Author notes

  1. Funing Chu and Yun Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China

    Funing Chu, Zhengyan Jia, Zhaoqi Wang, Shuang Lu, Ping Li, Yuanli Zhang, Yubo Liao, Mingzhe Xu, Xiaoqiang Yao, Shuting Wang, Cuicui Liu, Hongkai Zhang & Jinrong Qu

  2. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China

    Yun Liu, Chenglong Wang & Guang Yang

  3. Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, Jiangsu Province, 210029, China

    Qiuping Liu & Yudong Zhang

  4. Henan Province Institute for Medical Equipment Testing, Zhengzhou, Henan, 450000, People’s Republic of China

    Weijia Li

  5. MR Scientific Marketing, Siemens Healthineers, Xi’an, 710065, China

    Shaoyu Wang

  6. MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China

    Xu Yan

  7. Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205-2196, USA

    Ihab R. Kamel

  8. Department of Thoracic surgery, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, Henan, China

    Haibo Sun

Authors
  1. Funing Chu
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  2. Yun Liu
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Corresponding author

Correspondence to Jinrong Qu.

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Guarantor

The scientific guarantor of this publication is Jinrong Qu.

Conflict of interest

Two authors (Shaoyu Wang and Xu Yan) of this manuscript are employees of Siemens Healthineers. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Chu, F., Liu, Y., Liu, Q. et al. Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma. Eur Radiol 32, 5930–5942 (2022). https://doi.org/10.1007/s00330-022-08776-6

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  • DOI: https://doi.org/10.1007/s00330-022-08776-6

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