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Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma

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Abstract

Purpose

To investigate the value of texture analysis on unenhanced computed tomography (CT) to potentially differentiate mass-forming pancreatitis (MFP) from pancreatic ductal adenocarcinoma (PDAC).

Methods

A retrospective study consisting of 109 patients (30 MFP patients vs 79 PDAC patients) who underwent preoperative unenhanced CT between January 2012 and December 2017 was performed. Synthetic minority oversampling technique (SMOTE) algorithm was adopted to reconstruct and balance MFP and PDAC samples. A total of 396 radiomic features were extracted from unenhanced CT images. Mann–Whitney U test and minimum redundancy maximum relevance (MRMR) methods were used for the purpose of dimension reduction. Predictive models were constructed using random forest (RF) method, and were validated using leave group out cross-validation (LGOCV) method. Diagnostic performance of the predictive model, including sensitivity, specificity, accuracy, positive predicting value (PPV), and negative predicting value (NPV), was recorded.

Results

We applied 200% of SMOTE to MFP and PDAC patients, resulting in 90 MFP patients compared with 120 PDAC patients. Dimension reduction steps yielded 30 radiomic features using Mann–Whitney U test and MRMR methods. Ten radiomic features were retained using RF method. Four most predictive parameters, including GreyLevelNonuniformity_angle90_offset1, VoxelValueSum, HaraVariance, and ClusterProminence_AllDirection_offset1_SD, were used to generate the predictive model with preferable 92.2% sensitivity, 94.2% specificity, 93.3% accuracy, 92.2% PPV, and 94.2% NPV. Finally, in LGOCV analysis, a high pooled mean sensitivity, specificity, and accuracy (82.6%, 80.8%, and 82.1%, respectively) indicate a relatively reliable and stable predictive model.

Conclusions

Unenhanced CT texture analysis can be a promising noninvasive method in discriminating MFP from PDAC.

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Acknowledgements

We thank all authors for their continuous and excellent support with patient data collection, imaging analysis, statistical analysis, and valuable suggestions for the article.

Funding

This study was supported by Jiangsu provincial key research and development program (BE2017772), the National Natural Science Foundation of China (81771899), and Innovative Development Foundation of Department in Jiangsu Hospital of TCM (Y2019CX27).

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Author notes

  1. Shuai Ren and Rui Zhao contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China

    Shuai Ren, Rui Zhao, Jingjing Zhang, Kai Guo, Xiaoyu Gu & Zhongqiu Wang

  2. The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China

    Shuai Ren

  3. GE Healthcare China, Shanghai, 201203, China

    Shaofeng Duan

  4. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA

    Shuai Ren & Rong Chen

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  1. Shuai Ren
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Correspondence to Zhongqiu Wang.

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Ren, S., Zhao, R., Zhang, J. et al. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol 45, 1524–1533 (2020). https://doi.org/10.1007/s00261-020-02506-6

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