An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer

Zengjie Wu; Qing Lin; Haibo Wang; Guanqun Wang; Guangming Fu; Tiantian Bian

doi:10.1016/j.acra.2023.03.038

An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer

Acad Radiol. 2023 Sep:30 Suppl 2:S71-S81. doi: 10.1016/j.acra.2023.03.038. Epub 2023 May 20.

Authors

Zengjie Wu¹, Qing Lin², Haibo Wang², Guanqun Wang³, Guangming Fu³, Tiantian Bian⁴

Affiliations

¹ Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Z.W.).
² Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Q.L., H.W., T.B.).
³ Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (G.W., G.F.).
⁴ Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Q.L., H.W., T.B.). Electronic address: tiantianbian5001@qdu.edu.cn.

PMID: 37211478
DOI: 10.1016/j.acra.2023.03.038

Abstract

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.

Keywords: Breast cancer; Ductal carcinoma in situ; Microinvasion; Nomogram; Radiomics.

Publication types

Randomized Controlled Trial

MeSH terms

Breast Neoplasms* / diagnostic imaging
Carcinoma, Intraductal, Noninfiltrating* / diagnostic imaging
Female
Humans
Magnetic Resonance Imaging / methods
Nomograms
Retrospective Studies
Risk Factors