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MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland

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Abstract

Objectives

Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.

Methods

A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.

Results

Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.

Conclusions

The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.

Key Points

• Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities.

• A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.

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Abbreviations

3-D:

Three-dimensional

ANOVA:

Analysis of variance

BPGT:

Benign parotid gland tumors

CI:

Confidence interval

DCA:

Decision curve analysis

DLI:

Deep lobe involved

FNA:

Fine needle aspiration

fs-T2WI:

Fat-saturated T2-weighted images

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

ICC:

Inter-/intra-class correlation coefficient

IST:

Infiltration of surrounding tissue

LASSO:

Least absolute shrinkage and selection operator

MPGT:

Malignant parotid gland tumors

NGTDM:

Neighboring gray-tone difference matrix

Nomo-score:

Nomogram score

OR:

Odds ratio

Rad-score:

Radiomics score

SI:

Signal intensity

T1WI:

T1-Weighted images

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Acknowledgments

We thank Nicole Okoh, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Funding

No funding was received for this study.

Author information

Author notes

  1. Ying-mei Zheng and Jian Li contributed equally to this work.

Authors and Affiliations

  1. Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China

    Ying-mei Zheng

  2. Department of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan Road, Futian District, Shenzhen, 518000, China

    Jian Li

  3. Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China

    Song Liu, Jiu-fa Cui, Jin-feng Zhan, Jing Pang, Rui-zhi Zhou, Xiao-li Li & Cheng Dong

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  1. Ying-mei Zheng
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Corresponding author

Correspondence to Cheng Dong.

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Guarantor

The scientific guarantor of this publication is Wen-jian Xu.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

One of the authors (Jian Li) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study/observational

• multicenter study

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Ying-mei Zheng and Jian Li are collaborative first author

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Zheng, Ym., Li, J., Liu, S. et al. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. Eur Radiol 31, 4042–4052 (2021). https://doi.org/10.1007/s00330-020-07483-4

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  • DOI: https://doi.org/10.1007/s00330-020-07483-4

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