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Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model

  • Author Footnotes
    † Ni Xie and Xuhui Fan contributed equally to this work.
    Ni Xie
    Footnotes
    † Ni Xie and Xuhui Fan contributed equally to this work.
    Affiliations
    Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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  • Author Footnotes
    † Ni Xie and Xuhui Fan contributed equally to this work.
    Xuhui Fan
    Footnotes
    † Ni Xie and Xuhui Fan contributed equally to this work.
    Affiliations
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Road 100, Shanghai 200080, China

    R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China

    National Center for Translational Medicine (Shanghai), Shanghai, China
    Search for articles by this author
  • Haoran Xie
    Affiliations
    Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Search for articles by this author
  • Jiawei Lu
    Affiliations
    Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Search for articles by this author
  • Lanting Yu
    Affiliations
    Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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  • Hao Liu
    Affiliations
    Yizhun Medical AI Technology Co. Ltd., Beijing, China
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  • Author Footnotes
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
    Han Wang
    Correspondence
    Address correspondence to: Han Wang.
    Footnotes
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
    Affiliations
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Road 100, Shanghai 200080, China

    R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China

    National Center for Translational Medicine (Shanghai), Shanghai, China

    Jiading Branch of Shanghai General Hospital, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
    Xiaorui Yin
    Footnotes
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
    Affiliations
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Road 100, Shanghai 200080, China

    R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
    Baiwen Li
    Footnotes
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
    Affiliations
    Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    † Ni Xie and Xuhui Fan contributed equally to this work.
    ‡ Han Wang, Xiaorui Yin and Baiwen Li contributed equally to this work.
Published:October 14, 2022DOI:https://doi.org/10.1016/j.acra.2022.09.017

      Rationale and Objectives

      Pancreatic cancer is a common malignant tumor with a dismal prognosis. Preoperative differentiation of extrapancreatic extension (EPE) based on radiomics will facilitate treatment decision-making.

      Materials and Methods

      This research retrospectively recruited 156 patients from two medical centers. 122 patients from the center A were randomly divided into the training set and the internal test set in a 4:1 ratio. Additionally, 34 patients from the center B served as the external test set. Radiomics features were extracted from multiparametric MRI (MP-MRI), containing axial T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic contrast enhancement (DCE) sequences. The three-step method was used for feature extraction: SelecteKBest, least absolute shrinkage and selection operator (LASSO) algorithm, and recursive feature elimination based on random forest (RFE-RF). The model was constructed using six classifiers based on machine learning, and the classifier with the best performance was chosen. Finally, clinical factors associated with EPE were incorporated into the combined model.

      Results

      The classifier with the best performance was XGBoost, which obtained area under curve (AUC) values of 0.853 and 0.848 in the internal and external test sets, respectively. Through SelectKBest, the most relevant clinical factor for EPE was determined to be platelet, which was then added to the combined model, yielding AUC values of 0.880 and 0.848 in the internal and external test sets, respectively.

      Conclusion

      Radiomics models had the potential to noninvasively and accurately predict EPE before surgery. Additionally, it would add value to personalized precision treatment.

      Key words

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