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Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma

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Abstract

Objective

The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).

Methods

Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method.

Results

In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80).

Conclusion

The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.

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Author information

Author notes

  1. Shuai REN and Hui-juan TANG contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China

    Shuai Ren, Rui Zhao & Zhong-qiu Wang

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

    Shuai Ren & Rong Chen

  3. Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China

    Hui-juan Tang

  4. Department of Clinical and Molecular Sciences, Marche Polytechnic University, Ancona, 60126, Italy

    Hui-juan Tang

  5. GE Healthcare, Shanghai, 201203, China

    Shao-feng Duan

Authors
  1. Shuai Ren
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  2. Hui-juan Tang
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  3. Rui Zhao
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  4. Shao-feng Duan
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Corresponding authors

Correspondence to Shuai Ren or Zhong-qiu Wang.

Ethics declarations

We declare that we have no conflicts of interest.

Additional information

This study was supported by grants from the Administration of Traditional Chinese Medicine of Jiangsu Province (No. ZD201907), the National Natural Science Foundation of China (No. 82171925 and No. 81771899), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX20_1477), the China Scholarship Council (No. 201909077001), the Jiangsu Provincial Key Research and Development Program (No. BE2017772), and the Developing Program for High-level Academic Talent in Jiangsu Hospital of TCM (No. y2018rc04).

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Ren, S., Tang, Hj., Zhao, R. et al. Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma. CURR MED SCI 42, 217–225 (2022). https://doi.org/10.1007/s11596-022-2535-2

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  • DOI: https://doi.org/10.1007/s11596-022-2535-2

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