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CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors

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Abstract

Objectives

This study aimed to explore and validate the value of different radiomics models for differentiating benign and malignant parotid tumors preoperatively.

Methods

This study enrolled 388 patients with pathologically confirmed parotid tumors (training cohort: n = 272; test cohort: n = 116). Radiomics features were extracted from CT images of the non-enhanced, arterial, and venous phases. After dimensionality reduction and selection, radiomics models were constructed by logistic regression (LR), support vector machine (SVM), and random forest (RF). The best radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. A combined model was constructed by incorporating radiomics and clinical features. Model performances were assessed by ROC curve analysis, and decision curve analysis (DCA) was used to estimate the models’ clinical values.

Results

In total, 2874 radiomic features were extracted from CT images. Ten radiomics features were deemed valuable by dimensionality reduction and selection. Among radiomics models, the SVM model showed greater predictive efficiency and robustness, with AUCs of 0.844 in the training cohort; and 0.840 in the test cohort. Ultimate clinical features constructed a clinical model. The discriminatory capability of the combined model was the best (AUC, training cohort: 0.904; test cohort: 0.854). Combined model DCA revealed optimal clinical efficacy.

Conclusions

The combined model incorporating radiomics and clinical features exhibited excellent ability to distinguish benign and malignant parotid tumors, which may provide a noninvasive and efficient method for clinical decision making.

Key Points

  • The current study is the first to compare the value of different radiomics models (LR, SVM, and RF) for preoperative differentiation of benign and malignant parotid tumors.

  • A CT-based combined model, integrating clinical-radiological and radiomics features, is conducive to distinguishing benign and malignant parotid tumors, thereby improving diagnostic performance and aiding treatment.

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Abbreviations

AIC:

Akaike information criterion

ANOVA:

Analysis of variance

AUC:

Area under the curve

BCA:

Basal cell adenoma

CIs:

Confidence intervals

DCA:

Decision curve analysis

DICOM:

Digital Imaging and Communications in Medicine

FNAB:

Fine-needle aspiration biopsy

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

IBSI:

Image biomarker standardization initiative

ICC:

Intraclass correlation coefficient

IST:

Infiltration of surrounding tissues

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

mRMR:

Minimum redundancy maximum correlation

NPV:

Negative prediction value

ORs:

Odds ratios

PA:

Pleomorphic adenoma

PACS:

Picture archiving and communication systems

PPV:

Positive prediction value

RF:

Random forest

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM:

Support vector machine

WT:

Warthin tumor

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Acknowledgements

We thank the American Journal Experts (AJE) for their assistance with language editing.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China

    Yunlin Zheng, Di Zhou & Ming Wen

  2. GE Healthcare, Shanghai, 201203, People’s Republic of China

    Huan Liu

Authors
  1. Yunlin Zheng
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  2. Di Zhou
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  3. Huan Liu
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  4. Ming Wen
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Corresponding author

Correspondence to Ming Wen.

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Guarantor

The scientific guarantor of this publication is Ming Wen.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

One of the authors (Huan Liu) has significant statistical expertise and is identified as the statistical guarantor for the statistical analysis used in this study.

Informed consent

This study was approved by the institutional review board.

Ethical approval

Institutional Review Board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• performed at one institution

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Zheng, Y., Zhou, D., Liu, H. et al. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors. Eur Radiol 32, 6953–6964 (2022). https://doi.org/10.1007/s00330-022-08830-3

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  • DOI: https://doi.org/10.1007/s00330-022-08830-3

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