Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models.

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  • Additional Information
    • Source:
      Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9213011 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-3080 (Electronic) Linking ISSN: 09609776 NLM ISO Abbreviation: Breast Subsets: MEDLINE
    • Publication Information:
      Publication: 2002- : Amsterdam : Elsevier
      Original Publication: Edinburgh ; New York : Churchill Livingstone, c1992-
    • Subject Terms:
      Breast Neoplasms*/diagnostic imaging ; Breast Neoplasms*/drug therapy ; Breast Neoplasms*/pathology ; Neoadjuvant Therapy*/methods ; Axilla* ; Lymph Nodes*/diagnostic imaging ; Lymph Nodes*/pathology; Humans ; Female ; Middle Aged ; Adult ; Chemotherapy, Adjuvant ; Machine Learning ; Lymphatic Metastasis/diagnostic imaging ; Algorithms ; Aged ; Multiparametric Magnetic Resonance Imaging/methods ; Sensitivity and Specificity ; Predictive Value of Tests ; Treatment Outcome ; Retrospective Studies ; Magnetic Resonance Imaging/methods ; Area Under Curve ; Radiomics
    • Abstract:
      Purpose: Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.
      Materials and Methods: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.
      Results: Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.
      Conclusions: Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.
      Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose.
      (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
    • Contributed Indexing:
      Keywords: Axillary lymph node; Breast cancer; Multi-parametric magnetic resonance imaging; Neoadjuvant chemotherapy; Radiomics
    • Publication Date:
      Date Created: 20240502 Date Completed: 20240718 Latest Revision: 20240718
    • Publication Date:
      20240719
    • Accession Number:
      PMC11070644
    • Accession Number:
      10.1016/j.breast.2024.103737
    • Accession Number:
      38696854