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Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models

  • Pancreas
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

A log-combined model was developed to predict the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN) based on clinical and radiomic features extracted from multiparametric magnetic resonance imaging (MRI).

Materials and methods

A total of 111 patients with pathologically confirmed pSPN who underwent preoperative plain and contrast-enhanced MRI were included, and divided into an invasive group (n = 34) and non-invasive group (n = 77). Clinical features and laboratory data related to pSPN invasive behavior were analyzed. Regions of interest were delineated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) to extract radiomic features. Correlation analysis was performed for these features, followed by L1_based feature selection (C = 0.15). A logistic regression algorithm was used to construct models based on each of the four sequences and a log-combined model was used to integrate the sequences. A receiver operating characteristic (ROC) curve was plotted to evaluate the model performance, and the Brier score was used to assess the overall accuracy of the model predictions.

Results

The area under the ROC curve was 0.68, 0.73, 0.71, and 0.49 for Log-T1WI, Log-T2WI, Log-DWI, and Log-CE models, respectively, and 0.81 for the log-combined model. The accuracy, precision, sensitivity, and specificity of the log-combined model were 0.77, 0.88, 0.75, and 0.78, respectively. The best performance was obtained with the log-combined model with a Brier score of 0.18. Tumor location was identified as a significant clinical feature in comparison between the two groups (p < 0.05), and invasive pSPN was more frequent in the tail of the pancreas.

Conclusion

The log-combined model based on multiparametric MRI and clinical features can be used as a non-invasive diagnostic tool for preoperative prediction of pSPN invasive behavior and to facilitate the development of individualized treatment strategies and monitoring management plans.

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Authors and Affiliations

  1. Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China

    Xiuqun Liang, Xiaohui Guan & Ying Liu

  2. Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China

    Wenguang He, Zhan Feng & Zhi Li

  3. Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China

    Chencui Huang

  4. Department of Radionuclide, The People’s Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radionuclide, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China

    Zeyong Sun

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  1. Xiuqun Liang
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Liang, X., He, W., Huang, C. et al. Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models. Abdom Radiol 47, 3782–3791 (2022). https://doi.org/10.1007/s00261-022-03639-6

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