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Deep learning–assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study

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Abstract

Objectives

To develop deep learning–assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors.

Methods

Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model.

Results

MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001).

Conclusions

The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors.

Key Points

• The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors.

• Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model.

• The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.

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Abbreviations

APT:

Amide proton transfer

ASL:

Arterial spin labelling

AUC:

Area under the curve

BPTs:

Benign parotid tumors

CI:

Confidence interval

CNNs:

Convolutional neural networks

CT:

Computed tomography

FNA:

Fine-needle aspiration

Grad-CAM:

Gradient-weighted class activation mapping

HU:

Hounsfield unit

ICC:

Intraclass correlation coefficient

IDI:

Integrated discrimination improvement

IQR:

Interquartile range

IST:

Infiltration of surrounding tissues

IVIM:

Intravoxel incoherent motion

LASSO:

Least absolute shrinkage and selection operator

LM:

Lymphatic metastasis

ML:

Machine learning

MPTs:

Malignant parotid tumors

MRI:

Magnetic resonance imaging

NPV:

Negative predictive value

NRI:

Net reclassification index

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

ROI:

Region of interest

SGD:

Stochastic gradient descent

SMOTE:

Synthetic minority oversampling technique

SVM:

Support vector machine

USCBs:

Ultrasound-guided core biopsies

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Acknowledgements

This study received support from the Foundation of Science and Technology Bureau of Yuzhong District, Chongqing, China (Grant No. 20190111) and the Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxmX0020). We thank the American Journal Experts (AJE) for their assistance with language editing, and we appreciate the OnekeyAI platform and its developers, as well as all of the individuals who participated in these studies and each of the researchers and technicians who made this work possible.

Funding

This study received support from the Foundation of Science and Technology Bureau of Yuzhong District, Chongqing, China (Grant No. 20190111), and the Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxmX0020).

Author information

Author notes

  1. Qiang Yu and Youquan Ning have contributed equally to the study as co-first authors.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China

    Qiang Yu, Youquan Ning, Anran Wang, Jinming Gu, Quanjiang Li, Xinwei Chen, Fajin Lv & Juan Peng

  2. Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China

    Shuang Li & Qiang Yue

  3. Philips Healthcare, Chengdu, 610041, China

    Xiaodi Zhang

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  1. Qiang Yu
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Corresponding authors

Correspondence to Qiang Yue or Juan Peng.

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Guarantor

The scientific guarantor of this publication is Juan Peng.

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 (Juan Peng) has significant statistical expertise and is identified as the statistical guarantor for the statistical analysis used in this study.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Front Oncol. 2022;12:913898. https://doi.org/10.3389/fonc.2022.913898.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Yu, Q., Ning, Y., Wang, A. et al. Deep learning–assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study. Eur Radiol 33, 6054–6065 (2023). https://doi.org/10.1007/s00330-023-09568-2

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