Frontiers in Oncology (2020-10-01)

Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

  • Ning Mao,
  • Yi Dai,
  • Fan Lin,
  • Heng Ma,
  • Shaofeng Duan,
  • Haizhu Xie,
  • Wenlei Zhao,
  • Nan Hong

DOI
https://doi.org/10.3389/fonc.2020.541849
Journal volume & issue
Vol. 10

Abstract

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PurposeThis study aimed to establish and validate a radiomics nomogram based on dynamic contrast-enhanced (DCE)-MRI for predicting axillary lymph node (ALN) metastasis in breast cancer.MethodThis retrospective study included 296 patients with breast cancer who underwent DCE-MRI examinations between July 2017 and June 2018. A total of 396 radiomics features were extracted from primary tumor. In addition, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the features. Radiomics signature and independent risk factors were incorporated to build a radiomics nomogram model. Calibration and receiver operator characteristic (ROC) curves were used to confirm the performance of the nomogram in the training and validation sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA).ResultsThe radiomics signature consisted of three ALN-status-related features, and the nomogram model included the radiomics signature and the MR-reported lymph node (LN) status. The model showed good calibration and discrimination with areas under the ROC curve (AUC) of 0.92 [95% confidence interval (CI), 0.87–0.97] in the training set and 0.90 (95% CI, 0.85–0.95) in the validation set. In the MR-reported LN-negative (cN0) subgroup, the nomogram model also exhibited favorable discriminatory ability (AUC, 0.79; 95% CI, 0.70–0.87). DCA findings indicated that the nomogram model was clinically useful.ConclusionsThe MRI-based radiomics nomogram model could be used to preoperatively predict the ALN metastasis of breast cancer.

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