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Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.

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  • معلومة اضافية
    • المصدر:
      Publisher: Hindawi Country of Publication: United States NLM ID: 101277751 Publication Model: eCollection Cited Medium: Internet ISSN: 1748-6718 (Electronic) Linking ISSN: 1748670X NLM ISO Abbreviation: Comput Math Methods Med Subsets: MEDLINE
    • بيانات النشر:
      Publication: 2011-2024 : New York : Hindawi
      Original Publication: London : Taylor & Francis, c2006-
    • الموضوع:
      Deep Learning*; Breast Neoplasms/*diagnostic imaging ; Image Interpretation, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Triple Negative Breast Neoplasms/*diagnostic imaging; Adult ; Aged ; China ; Cohort Studies ; Computational Biology ; Contrast Media ; Female ; Humans ; Image Interpretation, Computer-Assisted/statistics & numerical data ; Magnetic Resonance Imaging/statistics & numerical data ; Middle Aged ; Principal Component Analysis ; ROC Curve ; Retrospective Studies ; Software
    • نبذة مختصرة :
      Competing Interests: The authors declare that they have no conflicts of interest.
      Purpose: To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC).
      Materials and Methods: This retrospective study included DCE-MRI images of 81 breast cancer patients (44 TNBC and 37 non-TNBC) from August 2018 to October 2019. The MR scans were achieved at a 1.5 T MR scanner. For each patient, the largest tumor mass was selected to analyze. Three-dimensional (3D) images of the regions of interest (ROIs) were automatically segmented on the third DCE phase by a deep learning segmentation model; then, the ROIs were checked and revised by 2 radiologists. DCE-MRI radiomics features were extracted from the 3D tumor volume. The patients were randomly divided into training ( N = 57) and test ( N = 24) cohorts. The machine learning classifier was built in the training dataset, and 5-fold cross-validation was performed on the training cohort to train and validate. The data of the test cohort were used to investigate the predictive power of the radiomics model in predicting TNBC and non-TNBC. The performance of the model was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
      Results: The radiomics model based on 15 features got the best performance. The AUC achieved 0.741 for the cross-validation, and 0.867 for the independent testing cohort.
      Conclusion: The radiomics model based on automatic image segmentation of DCE-MRI can be used to distinguish TNBC and non-TNBC.
      (Copyright © 2021 Mingming Ma et al.)
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    • الرقم المعرف:
      0 (Contrast Media)
    • الموضوع:
      Date Created: 20210823 Date Completed: 20211129 Latest Revision: 20240403
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC8371618
    • الرقم المعرف:
      10.1155/2021/2140465
    • الرقم المعرف:
      34422088