Subjects. This study received formal approval from the Ethical Committee of the University G. d’Annunzio of Chieti-Pescara, Italy; informed consent was waived by the same ethics committee that approved the study (Comitato Etico per la Ricerca Biomedica delle Province di Chieti e Pescara e dell’Università degli Studi “G. d’Annunzio” di Chieti e Pescara). The study was conducted according to ethical principles laid down by the latest version of the Declaration of Helsinki. A total of 62 patients who underwent clinically indicated breast MRI between January 2016 and May 2020 at our institution were retrospectively included. Inclusion criteria were 1) ER+/HER2- early breast cancer confirmed via biopsy, 2) MRI performed on a 1.5 T scanner, 3) availability of Oncotype DX RS.
MRI Protocol. All patients in this cohort underwent a clinically indicated breast MRI consisting of a standard T1-weighted (T1w), T2-weighted (T2w), diffusion-weighted imaging (DWI), and Dynamic Contrast Enhancement (DCE) acquisition performed using a 1.5 T MR scanner (Achieva, Philips Medical System, Best, the Netherlands) equipped with a dedicated phased-array breast coil. Detailed information regarding the DCE acquisition is described in Table 1.
Table 1
| T1-Weighted Post-Contrast 3D-FFE* |
Repetition time (msec) | 3000-5000 |
Echo time (msec) | 80 |
Section thickness (mm) | 2 |
Section gap (mm) | 0 |
Acquisition Matrix Size | 340x340 |
No. of signals acquired | 2 |
Field of view (mm) | 340x340 |
Sensitivity Encoding (SENSE) | Yes |
Acquisition Time (sec) | 54.3, 90** |
No. of sections | 167 |
*FFE=fast field echo **=first (“early”) and second (“peak”) contrast enhanced dynamic phase |
Imaging analysis. Whole-volume tumor manual segmentation of the tumor (T) was performed on the first (“early”) and second (“peak”) contrast-enhanced dynamic T1w images for each patient by two independent senior radiology residents. The software used for the segmentation was an open-source medical image computing platform, 3DSlicer Version 4.8 (www.3dslicer.org). To create the “tissue surrounding tumor” segmentations (TST), a “3dmask_tool” (AFNI) was used [48]. First, a 2 mm dilatation (“dilate”) and a 2 mm erosion (“erode”) were obtained from the CT of each patient. Then, the two masks were subtracted (“dilate” – “erode”) to obtain the TST which was 4 mm thick [49]. All the TST segmentations were then checked by the two readers and manually adjusted if necessary to include only the outer border of the tumor and the adjacent perivisceral tissue. T and TST are shown in Figure 1a.
Radiomic Features Extraction. The extraction of radiomic features from the masked (T and TST) T1w images was performed using PyRadiomics [50]. Reproducibility assessments of the features extracted by the two readers from the segmentations of all patients were performed. To avoid data heterogeneity bias and promote reproducibility, MR images and masks were resampled using 3 isotropic voxel dimensions (1x1x1 mm, 2x2x2 mm, 3x3x3 mm). For each segmentation and for each image resolution (1 mm, 2 mm, and 3 mm), ten built-in filters (Original, wavelet, Laplacian of Gaussian (LoG), square, square root, logarithm, exponential, Gradient, LBP2D, LBP3D) were applied, and seven feature classes (first-order statistics, shape descriptors, glcm, glrlm, ngtdm, gldm, glszm) were calculated, which resulted in a total of 1409 radiomic features for each image (Figure 1b). Prior to the machine learning analysis, all features were converted into z-scores relying on their subject distribution.
Machine Learning Analysis. A machine learning approach was used to exploit the radiomic features' multidimensionality and infer the risk of recurrence (high vs. low-intermedium). Two main strategies were implemented to address the large number of features extracted [51, 52]. The first approach reduced the number of used features by selecting only highly repeatable features between the masks delineated by the two radiologists (r > 0.95). The second approach leveraged the high collinearity among radiomic features. It then used a linear regression analysis to infer the risk of recurrence, thus employing a space dimension reduction procedure, namely the partial least square (PLS) regression [51–54]. PLS has one hyperparameter, namely the number of uncorrelated components to be used in the regression. Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure [54–56]. In nCV, data are divided into folds, and the model is trained on all data except one-fold in an iterative, nested manner. Whereas the outer loop estimates the model's performances among iterations (test), the inner loop evaluates the optimal hyperparameter (validation). If the number of folds equals the number of samples (one-fold per sample) the procedure is defined as leave-one-out nCV, an approach highly suited for medical applications where samples represent subjects [57–59]. The whole leave-one-out nCV PLS analysis was repeated multiple times for the following group of masks: a) DCE images (“early” and “peak”) in both T and TST, b) “peak” DCE in both T and TST, c) “early” DCE in both T and TST, d) “peak” DCE in T, e) “peak” DCE in TST, f) “early” DCE in T and g) “early” DCE in TST.
Reference Standard. A Recurrence Score >25 was considered to discriminate between low-intermediate (≤25) and high risk (>) of tumor recurrence [10, 13, 16, 21].