ABSTRACT
Background: Accurate preoperative assessment of tumor size is important in developing a surgical plan for breast cancer. The purpose of this study was to evaluate the accuracy of cone-beam breast computed tomography (CBBCT) and magnetic resonance imaging (MRI) in the assessment of tumor size and to analyze the factors influencing the discordance. Methods: In this retrospective study, patients with breast cancer who underwent preoperative contrast-enhanced CBBCT (CE-CBBCT) and dynamic contrast-enhanced MRI (DCE-MRI) and received a complete pathologic diagnosis from August 2020 to December 2021 were included, using the pathological result as the gold standard. Two radiologists assessed the CBBCT and MRI features and measured the tumor size with a 2-week washout period. Intraclass correlation coefficient (ICC) and Bland-Altman analyses were used to assess inter-observer reproducibility and agreement based on CBBCT, MRI and pathology. Univariate analyses of differences in clinical, pathological and CBBCT/MRI features between the concordant and discordant groups was performed using the t-test, Mann-Whitney U-test, Chi-squared test and Fisher's exact test. Multivariate analyses were used to identify factors associated with discordance of CBBCT/MRI with pathology. Results: A total of 115 female breast cancer patients (115 lesions) were included. All patients had a single malignant tumor of the unilateral breast. The reproducibility and the agreement ranged from moderate to excellent (ICC =0.607-0.983). Receiver operating characteristic (ROC) analyses showed that the cut-off values of CBBCT-pathology and MRI-pathology discordance were 2.25 and 2.65 cm, respectively. CBBCT/MRI-pathology concordance was significantly associated with the extent of pathology, lesion type, presence of calcification, human epidermal growth factor receptor 2 (HER2) status and fatty infiltration (P<0.05). In lesions containing calcification, the difference of CBBCT-pathology was significantly smaller than MRI-pathology (P=0.021). Non-mass enhancement (NME) was the main predictor of CBBCT- or MRI-pathology discordance [odds ratio (OR) =3.293-6.469, P<0.05], and HER2 positivity was a predictor of CBBCT-pathology discordance (OR =3.514, P=0.019). Conclusions: CBBCT and MRI have comparable accuracy in measurement of tumor size, and CBBCT is advantageous in assessing the size of calcified lesions. NME and HER2 positivity are significant predictors of CBBCT-pathology discordance. This suggests that CBBCT might serve as an alternative imaging technique to assess tumor size when patients do not tolerate MRI.
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 1st and 5th. 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.
