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Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging

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

Abstract

Objectives

To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs).

Methods

A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis.

Results

Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75–0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69–0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02).

Conclusions

A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs.

Key Points

• The radiomic features could be associated with the TET pathophysiology.

• TNM staging and Radscore could independently stratify the risk of TETs.

• The nomogram model is more objective and more comprehensive than previous methods.

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Abbreviations

ADC:

Apparent diffusion coefficient

AJCC:

American Joint Committee on Cancer

AUC:

Area under the curve

DCA:

Decision curve analysis

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLZSM:

Gray-level zone size matrix

IASLC:

International Association for the Study of Lung Cancer

ICC:

Intraclass correlation coefficient

ITMIG:

Thymic malignancy interest group

KM:

Kaplan-Meier

LASSO:

Least Absolute Shrinkage and Selection Operator

ROC:

Receiver operating characteristic

ROI:

Regions of interest

TC:

Thymic carcinomas

TETs:

Thymic epithelial tumors

UICC:

Union for International Cancer Control

WHO:

World Health Organization

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Acknowledgments

This study was supported in part by Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology, and Toxicology Research of Zhejiang Province under Grant No. 2020E10021.

Funding

This study has received funding by the Clinical Science Research of Zhejiang University, China (No.YYJJ2019Z06). This study has received funding by the Natural Science Foundation of Zhejiang Province, China (No. LSY19H180009). This study has received funding by the Medical Health Science and Technology Commission of Zhejiang Province, China (No. 2019KY123). This study has received funding by the Medical Health Science and Technology Commission of Zhejiang Province, China (No. 2018KY582).

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Authors and Affiliations

  1. Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, Hangzhou, China

    Qijun Shen, Yanna Shan, Wen Xu, Guangzhu Hu, Wenhui Chen & Zhongxiang Ding

  2. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., 400C, Boston, MA, 02114, USA

    Qijun Shen & Wenli Cai

  3. Department of Radiology, First Affiliated Hospital, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China

    Zhan Feng

  4. GE Healthcare (China), Shanghai, China

    Peipei Pang

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  1. Qijun Shen
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Correspondence to Zhongxiang Ding or Wenli Cai.

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The scientific guarantor of this publication is Wenli Cai.

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One of the authors of this manuscript (Peipei Pang) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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Written informed consent was not required for this study because of the retrospective nature of the study.

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• multicenter study

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Wenli Cai and Zhongxiang Ding are equal contributors to the final manuscript and supervising all the data.

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Shen, Q., Shan, Y., Xu, W. et al. Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging. Eur Radiol 31, 423–435 (2021). https://doi.org/10.1007/s00330-020-07100-4

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  • DOI: https://doi.org/10.1007/s00330-020-07100-4

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