Highlights
- •Accurate preoperative differentiation between DCISM and DCIS can facilitate individualized treatment optimization.
- •A radiomics nomogram based on preoperative MR images demonstrated the best discrimination efficacy between DCISM and DCIS.
- •BPE was an independent clinical risk factor for differentiating DCISM from DCIS.
Rationale and Objectives
Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion
(DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization
and individualized risk assessment. The present study aims to build and validate a
radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)
that could distinguish DCISM from pure DCIS breast cancer.
Materials and Methods
MR images of 140 patients obtained between March 2019 and November 2022 at our institution
were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The
independent clinical risk factors were selected by multivariate logistic regression
to establish the clinical model. The optimal radiomics features were chosen by the
least absolute shrinkage and selection operator, and a radiomics signature was built.
The nomogram model was constructed by integrating the radiomics signature and independent
risk factors. The discrimination efficacy of our nomogram was assessed by using calibration
and decision curves.
Results
Six features were selected to construct the radiomics signature for distinguishing
DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration
and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval
[CI], 0.703–0.926, 0.848–0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672–0.989, 0.764–0.999)
sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544–0.801, 0.527–0.907).
The decision curve also demonstrated that the nomogram model exhibited good clinical
utility.
Conclusion
The proposed noninvasive MRI-based radiomics nomogram model showed good performance
in distinguishing DCISM from DCIS.
Abbreviations:
ACR (American College of Radiology), BI-RADS (Breast Imaging Reporting and Data System), BPE (background parenchymal enhancement), DBT (digital breast tomosynthesis), DCA (decision curve analysis), DCE-MRI (dynamic contrast-enhanced magnetic resonance imaging), DCIS (ductal carcinoma in situ), DCISM (DCIS with microinvasion), LASSO (least absolute shrinkage and selection operator), mRMR (minimum redundancy maximum relevance), NME (non-mass enhancement)Key Words
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Article info
Publication history
Published online: May 20, 2023
Accepted:
March 25,
2023
Received in revised form:
March 24,
2023
Received:
February 17,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
