Development and Validation of an MRI Radiomics-Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer

Figures & data

Figure 1 The flow diagram of the study.

Table 1 Clinical and Tumor Features in the Training and Validation Cohorts

Characteristics	Training Cohort (N = 618)	Validation Cohort (N = 283)
Median age (years)	53.28	49.86
Race
White	465	173
Black	108	93
Others*	36	8
Not available	9	9
Menopausal status
Pre	250	147
Post	356	132
Not available	12	4
Tumor staging (size)
T1	302	95
T2	246	145
T3	59	31
T4	8	12
Not available	3	0
ER status
Positive	536	137
Negative	82	146
PR status
Positive	486	103
Negative	132	180
HER2 status
Positive	89	66
Negative	529	217

Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor; *Includes Asian, Native, Hispanic.

Table 2 Radiomics Features Selection from the CTE in the Training Cohort

Radiomics Features	Coefficient
Volume_cu_mm_Tumor	−0.2952
Inf_mea_of_corr2_Tumor	−0.3053
Max_Enhancement_from_char_curv	−0.2096
BreastDensity_T1	−0.0853
Grouping_based_variance_of_washout_slope_3D_tumor_Group_1	0.9801
Grouping_based_variance_of_washout_slope_2D_tumorSlice_Group_2	−0.1994
Grouping_based_proportion_of_tumor_voxels_2D_tumorSlice_Group_1	−0.2213
Grouping_based_proportion_of_tumor_voxels_2D_tumorSlice_Group_2	0.0376
Grouping_based_variance_of_peak_enhancement_slope_3D_tissue_PostCon_Group_3	0.0850
Grouping_based_variance_of_washin_slope_3D_tissue_PostCon_Group_2	0.1022
Grouping_based_variance_of_washout_slope_3D_tissue_PostCon_Group_1	0.0532
Mean_norm_DHOG_max_timepoint_binsize_6_with_filling_Tumor	0.1349
SER_Washout_tumor_vol_cu_mm	−0.1489
SER_map_Correlation2_tumor	−0.0687
SER_map_Sum_of_Squares_variance_tumor	−0.1204
PE_map_Cluster_Prominence_tumor	0.1228
WashinRate_map_Correlation2_tumor	0.3493
WashinRate_map_Max_Probability_tumor	0.1759
WashinRate_map_information_measure_correlation1_tumor	−0.0017
WashinRate_map_skewness_tumor	−0.3948
SER_map_skewness_tissue_T1	0.2037
Peak_SER_tissue_PostCon	0.2229
SER_map_Cluster_Shade_tissue_PostCon	−0.3002
PE_map_kurtosis_tissue_PostCon	−0.2192
WashinRate_map_Autocorrelation_tissue_PostCon	0.1409
WashinRate_map_Cluster_Shade_tissue_PostCon	−0.0817

Figure 2 The LASSO regression model was used to select radiomics features. (A) LASSO coefficient profiles of the 26 radiomics features. A coefficient profile plot was generated versus the selected log (λ) value using ten-fold cross-validation, where optimal λ resulted in 8 features with nonzero coefficients. (B) The 26 radiomics features’ LASSO coefficient profiles. The log (λ) sequence was used to create a coefficient profile plot. Using 10-fold cross-validation, the dotted vertical line was drawn at the value chosen.

Table 3 Performance of the Radiomics Model in the Training and Validation Cohorts

	Training Cohort (n=630)	Validation Cohort (n=271)
Radiomics model	AUC (95% CI) SEN SPE ACC	AUC (95% CI) SEN SPE ACC
	0.761 (0.728–0.794) 0.771 0.601 0.717	0.722 (0.664–0.777) 0.763 0.624 0.720

Abbreviations: AUC, area under the curve; SEN, sensitivity; SPE, specificity; ACC, accuracy; 95% CI, 95% confidence interval.

Figure 3 The receiver operator characteristic curves of the radiomics model. (A) The radiomics model of ROC curves in the training cohort. (B) The radiomics model of ROC curves in the validation cohort.

Figure 4 The decision curve analysis (DCA) of the radiomics model. (A) The radiomics model of DCA in the training cohort. (B) The radiomics model of DCA in the validation cohort.