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Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20+ B cells in patients with pancreatic ductal adenocarcinoma

  • Pancreas
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Abstract

Purpose

To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20+ B lymphocyte expression in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

Overall, 189 patients with PDAC (n = 132 and n = 57 in the training and validation sets, respectively) underwent immunohistochemistry and radiomics feature extraction. The X-tile software was used to stratify them into groups with ‘high’ and ‘low’ CD20+ B lymphocyte expression levels. For each patient, 1409 radiomic features were extracted from volumes of interest and reduced using variance analysis and Spearman correlation analysis. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. Model performance was determined by its discriminative ability, calibration, and clinical utility.

Results

A log-rank test showed that the patients with high CD20+ B expression had significantly longer survival than those with low CD20+ B expression. The prediction model showed good discrimination in both the training and validation sets. For the training set, the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.82 (95% CI 0.74–0.89), 92.42%, 57.58%, 0.75, 0.69, and 0.88, respectively; whereas these values for the validation set were 0.84 (95% CI 0.72–0.93), 86.21%, 78.57%, 0.83, 0.81, and 0.85, respectively.

Conclusion

The MLP network classifier based on contrast-enhanced CT can accurately predict CD20+ B expression in patients with PDAC.

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Funding

This work was supported in part by the National Science Foundation for Scientists of China (Grant Nos. 81871352, 82171915, 82171930), Clinical Research Plan of SHDC (Grant No. SHDC2020CR4073), 234 Platform Discipline Consolidation Foundation Project (Grant No. 2019YPT001), Shanghai Science and Technology Innovation Action Plan Medical Innovation Research Project (Grant No. 20Y11912500), and the Natural Science Foundation of Shanghai Science and Technology Innovation Action Plan (Grant Nos. 21ZR1478500, 21Y11910300).

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Author notes

  1. Jieyu Yu and Qi Li are the joint first authors in the paper.

Authors and Affiliations

  1. Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China

    Jieyu Yu, Qi Li, Hao Zhang, Yinghao Meng, Chao Ma, Fang Liu, Xu Fang, Jing Li, Xiaochen Feng, Chengwei Shao, Yun Bian & Jianping Lu

  2. Department of Pathology, Changhai Hospital, Naval Medical University, Shanghai, China

    Yan Fang Liu & Hui Jiang

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  1. Jieyu Yu
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Correspondence to Yun Bian or Jianping Lu.

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Yu, J., Li, Q., Zhang, H. et al. Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20+ B cells in patients with pancreatic ductal adenocarcinoma. Abdom Radiol 47, 242–253 (2022). https://doi.org/10.1007/s00261-021-03285-4

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