Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

PLoS One. 2021 Mar 1;16(3):e0247074. doi: 10.1371/journal.pone.0247074. eCollection 2021.

Abstract

Objective: To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.

Materials and methods: A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.

Results: The 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).

Conclusion: Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Axilla
  • Breast Neoplasms / diagnostic imaging*
  • Breast Neoplasms / metabolism
  • Breast Neoplasms / pathology*
  • Breast Neoplasms / surgery
  • Cohort Studies
  • Contrast Media / pharmacokinetics*
  • Female
  • Humans
  • Image Processing, Computer-Assisted*
  • Lymph Nodes / diagnostic imaging*
  • Lymph Nodes / pathology
  • Lymphatic Metastasis
  • Magnetic Resonance Imaging*
  • Middle Aged
  • Models, Biological*
  • ROC Curve
  • Retrospective Studies
  • Tissue Distribution

Substances

  • Contrast Media

Grants and funding

This work was supported by Project of Sichuan Medical Association (www.sma.org.cn) Grant# S17067 (Y.Y. L),Sichuan Science and Technology Program (http://kjt.sc.gov.cn/) Grant# 2021YFS0075 (J. R), and Sichuan Science and Technology Program(http://kjt.sc.gov.cn/) Grant# 2021YFS0225 (P. Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. XZ received support in the form of a salary from GE Healthcare. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.