Feasibility analysis of magnetic resonance imaging-based radiomics features for preoperative prediction of nuclear grading of ductal carcinoma in situ

Meng-Ran Zhao; Wen-Juan Ma; Xiang-Chao Song; Zhi-Jun Li; Zhen-Zhen Shao; Hong Lu; Rui Zhao; Yi-Jun Guo; Zhao-Xiang Ye; Pei-Fang Liu

doi:10.21037/gs-23-132

Feasibility analysis of magnetic resonance imaging-based radiomics features for preoperative prediction of nuclear grading of ductal carcinoma in situ

Gland Surg. 2023 Sep 25;12(9):1209-1223. doi: 10.21037/gs-23-132. Epub 2023 Sep 19.

Authors

Meng-Ran Zhao^#¹, Wen-Juan Ma^#², Xiang-Chao Song¹, Zhi-Jun Li¹, Zhen-Zhen Shao², Hong Lu², Rui Zhao², Yi-Jun Guo², Zhao-Xiang Ye^#¹, Pei-Fang Liu^#²

Affiliations

¹ Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
² Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.

^# Contributed equally.

Abstract

Background: The nuclear grading of ductal carcinoma in situ (DCIS) affects its clinical risk. The aim of this study was to investigate the possibility of predicting the nuclear grading of DCIS, by magnetic resonance imaging (MRI)-based radiomics features. And to develop a nomogram combining radiomics features and MRI semantic features to explore the potential role of MRI radiomic features in the assessment of DCIS nuclear grading.

Methods: A total of 156 patients (159 lesions) with DCIS and DCIS with microinvasive (DCIS-MI) were enrolled in this retrospective study, with 112 lesions included in the training cohort and 47 lesions included in the validation cohort. Radiomics features were extracted from Dynamic contrast-enhanced MRI (DCE-MRI) phases 1^st and 5^th. After feature selection, radiomics signature was constructed and radiomics score (Rad-score) was calculated. Multivariate analysis was used to identify MRI semantic features that were significantly associated with DCIS nuclear grading and combined with Rad-score to construct a Nomogram. Receiver operating characteristic curves were used to evaluate the predictive performance of Rad-score and Nomogram, and decision curve analysis (DCA) was used to evaluate the clinical utility.

Results: In multivariate analyses of MRI semantic features, larger tumor size and heterogeneous enhancement pattern were significantly associated with high-nuclear grade DCIS (HNG DCIS). In the training cohort, Nomogram had an area under curve (AUC) of 0.879 and Rad-score had an AUC of 0.828. Similarly, in the independent validation cohort, Nomogram had an AUC value of 0.828 and Rad-score had an AUC of 0.772. In both the training and validation cohorts, Nomogram had a significantly higher AUC value than Rad-score (P<0.05). DCA confirmed that Nomogram had a higher net clinical benefit.

Conclusions: MRI-based radiomic features can be used as potential biomarkers for assessing nuclear grading of DCIS. The nomogram constructed by radiomic features combined with semantic features is feasible in discriminating non-HNG and HNG DCIS.

Keywords: Breast cancer; ductal carcinoma in situ (DCIS); machine learning; magnetic resonance imaging (MRI); radiomics.