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Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study

  • Head-Neck-ENT Radiology
  • Published:
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Abstract

Purpose

To develop and validate a dual-energy CT (DECT)–based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC).

Methods

A total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed.

Results

The tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree.

Conclusion

The nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation.

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Availability of data and material

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Code availability

All statistical analysis was conducted using an online R software (v.3.4.1; http://www.Rproject.org). ICC was calculated using “lme4” package. LASSO regression analysis was performed using the “glmnet” package. Multivariate logistic regression and nomogram construction were performed using the “rms” package. ROC curves were plotted using the “pROC” package. Calibration curve and Hosmer–Lemeshow test were conducted using the “ModelGood” package.

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Acknowledgements

We would like to thank Dr. Zhoushe Zhao from GE Healthcare China for providing technical support regarding radiomics analysis. We also acknowledge Professor Vincent Chong at the National University of Singapore for the revision of this manuscript and language polishing.

Funding

This study was funded by the Beijing Municipal Administration of Hospitals’Ascent Plan (DFL20190203), Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201704), and High Level Health Technical Personnel of Bureau of Health in Beijing (2014–2-005).

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Author notes

  1. The first two authors contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, DongJiaoMinXiang Street, Dongcheng District, Beijing, 100730, China

    Zheng Li, Zhaohui Liu, Xiaoxia Qu & Junfang Xian

  2. Pharmaceutical Diagnostics, Precision Health Institute, GE Healthcare China, Beijing, 100176, China

    Yan Guo & Sicong Wang

  3. Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, 410011, China

    Yajun Li

  4. Department of Radiology, Eye Ear Nose and Throat Hospital of Fudan University, Shanghai, 200031, China

    Yucheng Pan

  5. Department of Diagnostic Radiology, General Hospital of Eastern Theater Command/Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China

    Longjiang Zhang

  6. Imaging Center, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, 530021, China

    Danke Su

  7. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China

    Qian Yang

  8. Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China

    Xiaofeng Tao

  9. Department of Radiology, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, China

    Qiang Yue

Authors
  1. Zheng Li
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  2. Zhaohui Liu
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  3. Yan Guo
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  10. Qian Yang
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  11. Xiaofeng Tao
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Contributions

Writing—original draft perparation: Zheng Li; writing—revising it critically: Zhaohui Liu; methodology: Yan Guo and Sicong Wang; acquisition of data: Xiaoxia Qu, Yajun Li, Yucheng Pan, Longjiang Zhang, Danke Su, Qian Yang, Xiaofeng Tao, and Qiang Yue; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: Junfang Xian.

Corresponding author

Correspondence to Junfang Xian.

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Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics approval

All procedures performed in the study were approved by institutional review board in accordance with the ethical standards and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Written informed consent was waived because of the retrospective nature of the study.

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Written informed consent for publication was obtained from all participants.

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Cite this article

Li, Z., Liu, Z., Guo, Y. et al. Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study. Neuroradiology 64, 361–369 (2022). https://doi.org/10.1007/s00234-021-02860-2

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  • DOI: https://doi.org/10.1007/s00234-021-02860-2

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