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Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To develop and evaluate a radiomics nomogram for differentiating the malignant risk of gastrointestinal stromal tumours (GISTs).

Methods

A total of 222 patients (primary cohort: n = 130, our centre; external validation cohort: n = 92, two other centres) with pathologically diagnosed GISTs were enrolled. A Relief algorithm was used to select the feature subset with the best distinguishing characteristics and to establish a radiomics model with a support vector machine (SVM) classifier for malignant risk differentiation. Determinant clinical characteristics and subjective CT features were assessed to separately construct a corresponding model. The models showing statistical significance in a multivariable logistic regression analysis were used to develop a nomogram. The diagnostic performance of these models was evaluated using ROC curves. Further calibration of the nomogram was evaluated by calibration curves.

Results

The generated radiomics model had an AUC value of 0.867 (95% CI 0.803–0.932) in the primary cohort and 0.847 (95% CI 0.765–0.930) in the external cohort. In the entire cohort, the AUCs for the radiomics model, subjective CT findings model, clinical index model and radiomics nomogram were 0.858 (95% CI 0.807–0.908), 0.774 (95% CI 0.713–0.835), 0.759 (95% CI 0.697–0.821) and 0.867 (95% CI 0.818–0.915), respectively. The nomogram showed good calibration.

Conclusions

This radiomics nomogram predicted the malignant potential of GISTs with excellent accuracy and may be used as an effective tool to guide preoperative clinical decision-making.

Key Points

• CT-based radiomics model can differentiate low- and high-malignant-potential GISTs with satisfactory accuracy compared with subjective CT findings and clinical indexes.

• Radiomics nomogram integrated with the radiomics signature, subjective CT findings and clinical indexes can achieve individualised risk prediction with improved diagnostic performance.

• This study might provide significant and valuable background information for further studies such as response evaluation of neoadjuvant imatinib and recurrence risk prediction.

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Abbreviations

AFIP:

Armed forces institute of pathology

AUC:

Area under curve

CI:

Confidence interval

EVFDM:

Enlarged vessels feeding or draining the mass

GISTs:

Gastrointestinal stromal tumours

ICCs :

Inter- and intraclass correlation coefficients

NCCN:

National comprehensive cancer network

NIH :

National institutes of health

OR :

Odds ratio

SVM:

Support vector machine

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Funding

This study has received funding by the State’s Key Project of Research and Development Plan (2017YFC0108300 and 2017YFC0108303) and JSPS KAKENHI Grant (17H00867 and 26108006).

Author information

Author notes

  1. Tao Chen and Zhenyuan Ning contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, No.1838, North Guangzhou Avenue, Guangzhou, 510515, Guangdong Province, China

    Tao Chen, Yanfeng Hu, Hao Liu, Jiang Yu & Guoxin Li

  2. Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Tao Chen, Holger R. Roth & Kensaku Mori

  3. School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong Province, China

    Zhenyuan Ning & Yu Zhang

  4. Medical Image Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China

    Lili Xu, Wei Xiong, Xixi Zhao & Yikai Xu

  5. Department of General Surgery, Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou, 510080, Guangdong Province, China

    Xingyu Feng & Yong Li

  6. Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong Province, China

    Shuai Han

Authors
  1. Tao Chen
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  2. Zhenyuan Ning
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  3. Lili Xu
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  4. Xingyu Feng
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  5. Shuai Han
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  6. Holger R. Roth
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  7. Wei Xiong
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  8. Xixi Zhao
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  9. Yanfeng Hu
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  10. Hao Liu
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  11. Jiang Yu
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  12. Yu Zhang
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  13. Yong Li
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  14. Yikai Xu
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  15. Kensaku Mori
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  16. Guoxin Li
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Corresponding authors

Correspondence to Tao Chen or Guoxin Li.

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Guarantor

The scientific guarantor of this publication is Tao Chen.

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 has significant statistical expertise: Hao Liu, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou 510515, Guangdong Province, China. Hao Liu has completed postdoctoral research of cancer epidemiological statistics in Heidelberg Cancer Center, Germany. Hao Liu specialises in statistical analysis and provided statistical advice in this study.

Informed consent

Written informed consent or substitute was obtained from all patients in this study.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicentre study

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Chen, T., Ning, Z., Xu, L. et al. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol 29, 1074–1082 (2019). https://doi.org/10.1007/s00330-018-5629-2

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  • DOI: https://doi.org/10.1007/s00330-018-5629-2

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