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Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis

  • Nuclear Medicine
  • Published:
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Abstract

Objectives

Pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP) are diseases with a highly analogous visual presentation that are difficult to distinguish by imaging. The purpose of this research was to create a radiomics-based prediction model using dual-time PET/CT imaging for the noninvasive classification of PDAC and AIP lesions.

Methods

This retrospective study was performed on 112 patients (48 patients with AIP and 64 patients with PDAC). All cases were confirmed by imaging and clinical follow-up, and/or pathology. A total of 502 radiomics features were extracted from the dual-time PET/CT images to develop a radiomics decision model. An additional 12 maximum intensity projection (MIP) features were also calculated to further improve the radiomics model. The optimal radiomics feature set was selected by support vector machine recursive feature elimination (SVM-RFE), and the final classifier was built using a linear SVM. The performance of the proposed dual-time model was evaluated using nested cross-validation for accuracy, sensitivity, specificity, and area under the curve (AUC).

Results

The final prediction model was developed from a combination of the SVM-RFE and linear SVM with the required quantitative features. The multimodal and multidimensional features performed well for classification (average AUC: 0.9668, accuracy: 89.91%, sensitivity: 85.31%, specificity: 96.04%).

Conclusions

The radiomics model based on 2-[18F]fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET/CT dual-time images provided promising performance for discriminating between patients with benign AIP and malignant PDAC lesions, which shows its potential for use as a diagnostic tool for clinical decision-making.

Key Points

• The clinical symptoms and imaging visual presentations of PDAC and AIP are highly similar, and accurate differentiation of PDAC and AIP lesions is difficult.

• Radiomics features provided a potential noninvasive method for differentiation of AIP from PDAC.

• The diagnostic performance of the proposed radiomics model indicates its potential to assist doctors in making treatment decisions.

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Materials availability

Requests for materials should be addressed to Chao Cheng, M.D. and Xiaodong Yang, Ph.D.

Abbreviations

2-[18F]FDG:

2-[18F]Fluoro-2-deoxy-D-glucose

2D:

Two dimensional

3D:

Three dimensional

AIP:

Autoimmune pancreatitis

CV:

Cross-validation

MIP:

Maximal intensity projection

PDAC:

Pancreatic ductal adenocarcinoma

PET/CT:

Positron emission tomography/computerized tomography

RI:

Mean retention index

ROC:

Receiver operating characteristic

ROI:

Region of interest

SUVmax :

Maximum standardized uptake value

SVM:

Support vector machine

VOI:

Volume of interest

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Acknowledgements

We thank to Yuquan Zhang for his helpful suggestions on this project and Fei Wang for the assistance in statistics and data visualization.

Funding

This study was funded by the National Natural Science Foundation of China (No. 61701492), the National Key Research and Development Program of China (No. 2016YFC0103502), the Wenzhou Science and Technology Foundation (No. ZS2017020), and as a medical artificial intelligence project by the Sun Yat-Sen Memorial Hospital (No.YXRGZN201905).

Author information

Authors and Affiliations

  1. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China

    Zhaobang Liu

  2. Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China

    Zhaobang Liu, Ming Li & Xiaodong Yang

  3. Department of Nuclear Medicine, Changhai Hospital, Second Military Medical University, 168 Changhai Road, Shanghai, 200433, China

    Changjing Zuo, Shengnan Ren, Ye Peng, Gaofeng Sun & Chao Cheng

  4. Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China

    Zehong Yang & Jun Shen

  5. Wenzhou People’s Hospital, Wenzhou Medical University, Wenzhou, 325041, China

    Xiaokai Yang

Authors
  1. Zhaobang Liu
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  2. Ming Li
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  11. Xiaodong Yang
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Corresponding authors

Correspondence to Chao Cheng or Xiaodong Yang.

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Guarantor

The scientific guarantor of this publication is Prof. Xiaodong Yang.

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 has significant statistical expertise.

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 other journals.

The previous work published in the journal Medical Physics was named “Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT.” Early work only focused on comparisons of feature selection strategies and classifiers using data from PET/CT single-time imaging. The number of patients was 45 AIP and 66 PDAC, which partially overlaps with our current case. Our current work is mainly to study the advantages of PET/CT dual-time imaging in distinguishing AIP and PDAC via radiomics analysis. Our number of patients is 48 AIP cases and 64 PDAC cases.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Liu, Z., Li, M., Zuo, C. et al. Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis. Eur Radiol 31, 6983–6991 (2021). https://doi.org/10.1007/s00330-021-07778-0

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