Radiomics analysis has emerged as a promising approach to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on acquisition and pre-processing techniques used. In this study, we aim to investigate the effect of various image normalization techniques on robustness of radiomics features extracted from breast cancer patient MRI scans. MRI scans from the publicly available MAMA-MIA dataset and an internal breast MRI test set depicting triple negative breast cancer (TNBC) were used. We compared the effect of commonly used image normalization techniques on radiomics feature robustnessusing Concordance-Correlation-Coefficient (CCC) between multiple combinations of normalization approaches. We also trained machine learning-based prediction models of pathologic complete response (pCR) on radiomics after different normalization techniques were used and compared their areas under the receiver operating characteristic curve (ROC-AUC). For predicting complete pathological response from pre-treatment breast cancer MRI radiomics, the highest overall ROC-AUC was achieved by using a combination of three different normalization techniques indicating their potentially powerful role when working with heterogeneous imaging data. The effect of normalization was more pronounced with smaller training data and normalization may be less important with increasing abundance of training data. Additionally, we observed considerable differences between MRI data sets and their feature robustness towards normalization. Overall, we were able to demonstrate the importance of selecting and standardizing normalization methods for accurate and reliable radiomics analysis in breast MRI scans especially with small training data sets.

Tissue sodium concentration quantification of breast cancer tissue at 7.0-T MRI distinguished disease responsiveness after the initiation of chemotherapy treatment. Background Tissue sodium concentration (TSC) is elevated in breast cancer and can determine chemotherapy response. Purpose To test the feasibility of using a sodium 23 (23Na) MRI protocol at 7.0 T ...

In sodium (²³Na) magnetic resonance imaging (MRI), partial volume effects (PVE) are one of the most common causes of errors in the in vivo quantification of apparent tissue sodium concentration (aTSC). Advanced image reconstruction algorithms, such as compressed sensing (CS), have the potential to reduce PVE. This study aimed to investigate the feasibility of CS-based reconstruction techniques for improving image quality, and to assess whether differences in aTSC exist when images are reconstructed using different methods. In this study, three healthy participants and 12 female participants with breast cancer were examined on a 7T MRI scanner. ²³Na-MRI images were reconstructed using weighted total variation (wTV), directional total variation (dTV), anatomically guided total variation (AG-TV) and adaptive combine (ADC) methods. The consistency of tumor volume delineations based on sodium data was assessed using the Dice score, and aTSC quantification was performed for various image reconstruction methods. Pairwise comparisons of aTSC values between reconstruction methods (wTV, dTV, AG-TV, and ADC) were conducted using the Wilcoxon signed-rank test. All methods provided breast MRI images with well-preserved sodium signal and tissue structures. The mean Dice scores for wTV, dTV, and AG-TV were 65±11%, 72±9%, and 75±6%, respectively. Mean aTSC (in mmol/L) values in glandular tissue were 52.0±4.0 for wTV, 55.0±4.0 for dTV, 63±3.0 for AG-TV, and 69.0±3.0 for ADC reconstructions. In adipose tissue, mean values of aTSC were 32.0±4.0, 29.0±3.0, 19.0±2.0, and 23.0±3.0 for wTV, dTV, AG-TV, and ADC, respectively. In tumors, mean aTSC were as follows: 80.0±5.0 for wTV, 85.0±5.0 for dTV, 99.0±5.0 for AG-TV, and 105.0±7.0 for ADC. Post-hoc Wilcoxon signed-rank tests showed that AG-TV differed significantly from both wTV and dTV across all tissues (all p< 0.001), and that wTV also differed significantly from dTV (p=0.038 in gland, p=0.027 in adipose, and p=0.009 in tumor). Tumor appearance and aTSC estimations depend on the reconstruction method and parameter selection, likely due to differences in PVE reduction. Among the evaluated approaches, AG-TV showed the most favorable overall performance for ²³Na-MRI of breast tumors under the studied conditions.

Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at different field strengths such as 3T and 7T, remain challenging research tasks. Purpose: To address alignment challenges and enable consistent tumor segmentation across different MRI field strengths. Study type: Retrospective. Subjects: Nine female subjects with breast tumors were involved: six histologically proven invasive ductal carcinomas (IDC) and three fibroadenomas. Field strength/sequence: Imaging was performed at 3T and 7T scanners using post-contrast T1-weighted three-dimensional time-resolved angiography with stochastic trajectories (TWIST) sequence. Assessments: The method's performance for joint image registration and tumor segmentation was evaluated using several quantitative metrics, including signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized cross-correlation (NCC), Dice coefficient, F1 score, and relative sum of squared differences (rel SSD). Statistical tests: The Pearson correlation coefficient was used to test the relationship between the registration and segmentation metrics. Results: When calculated for each subject individually, the PSNR was in a range from 27.5 to 34.5 dB, and the SSIM was from 82.6 to 92.8%. The model achieved an NCC from 96.4 to 99.3% and a Dice coefficient of 62.9 to 95.3%. The F1 score was between 55.4 and 93.2% and the rel SSD was in the range of 2.0 and 7.5%. The segmentation metrics Dice and F1 Score are highly correlated (0.995), while a moderate correlation between NCC and SSIM (0.681) was found for registration. Data conclusion: Initial results demonstrate that the proposed method may be feasible in providing joint tumor segmentation and registration of MRI data acquired at different field strengths.