4.6 Article

Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer

Journal

DIAGNOSTICS
Volume 12, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/diagnostics12102558

Keywords

radiomics; estrogen receptor; progesterone receptor; breast cancer; magnetic resonance imaging

Funding

  1. Research and development (R&D) foundation for major Science and Technology from Shenyang [19-112-4-105]
  2. Big data foundation for health care from China Medical University [HMB201902105]
  3. Natural Fund Guidance Plan from Liaoning [2019-ZD-0743]
  4. Shengjing Hospital of China Medical University

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This study aimed to establish and validate multiregional radiomic signatures (RSs) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for preoperative identification of estrogen receptor (ER) and progesterone receptor (PR) status in breast cancer. The results showed that multiregional RSs achieved the best discriminative performance for predicting ER and PR status, demonstrating promising results for preoperative evaluation in breast cancer patients.
Radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for breast estrogen receptor (ER) and progesterone receptor (PR) status evaluation. However, the radiomic features of peritumoral regions were not thoroughly analyzed. This study aimed to establish and validate the multiregional radiomic signatures (RSs) for the preoperative identification of the ER and PR status in breast cancer. A total of 443 patients with breast cancer were divided into training (n = 356) and validation (n = 87) sets. Radiomic features were extracted from intra- and peritumoral regions on six functional parametric maps from DCE-MRI. A two-sample t-test, least absolute shrinkage and selection operator regression, and stepwise were used for feature selections. Three RSs for predicting the ER and PR status were constructed using a logistic regression model based on selected intratumoral, peritumoral, and combined intra- and peritumoral radiomic features. The area under the receiver operator characteristic curve (AUC) was used to assess the discriminative performance of three RSs. The AUCs of intra- and peritumoral RSs for identifying the ER status were 0.828/0.791 and 0.755/0.733 in the training and validation sets, respectively. For predicting the PR status, intra- and peritumoral RSs resulted in AUCs of 0.816/0.749 and 0.806/0.708 in the training and validation sets, respectively. Multiregional RSs achieved the best AUCs among three RSs for evaluating the ER (0.851 and 0.833) and PR (0.848 and 0.763) status. In conclusion, multiregional RSs based on functional parametric maps from DCE-MRI showed promising results for preoperatively evaluating the ER and PR status in breast cancer patients. Further studies using a larger cohort from multiple centers are necessary to confirm the reliability of the established models before clinical application.

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