Inhomogeneous static magnetic field-induced distortion correction applied to diffusion weighted MRI of the breast at 3T.

To evaluate the performance of an advanced method for correction of inhomogeneous static magnetic field induced distortion in echo-planar imaging (EPI), applied to diffusion-weighted MRI (DWI) of the breast.An algorithm for distortion correction based on the symmetry of the distortion induced by static field inhomogeneity when the phase encoding polarity is reversed was evaluated in 36 data sets of patients who received an MRI examination that included DWI (b = 0 and 700 s/mm(2) ) and an extra b = 0 s/mm(2) sequence with opposite phase encoding polarity. The decrease of the L2 -square norm after correction between opposed phase encoding b = 0 images was calculated. Mattes mutual information between b = 0 images and fat-suppressed T2 -weighted images was calculated before and after correction.The L2 -square norm between different phase encoding polarities for b = 0 images was reduced 94.3% on average after distortion correction. Furthermore, Mattes mutual information between b = 0 images and fat-suppressed T2 -weighted images increased significantly after correction for all cases (P < 0.001).Geometric distortion correction in DWI of the breast results in higher similarity of DWI to anatomical non-EPI T2 -weighted images and would potentially allow for a more reliable lesion segmentation mapping among different MRI modalities

Metabolic characterization of triple negative breast cancer

Background The aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ERpos/PgRpos/HER-2pos status which for the sake of simplicity is called triple positive breast cancer (TPBC). Methods The study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data. Results Choline levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or “Glutamine addiction” has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2pos tumors, which support Glycine as potential marker for tumor aggressiveness. Conclusions Metabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes

Inhomogeneous static magnetic field-induced distortion correction applied to diffusion weighted MRI of the breast at 3T.

To evaluate the performance of an advanced method for correction of inhomogeneous static magnetic field induced distortion in echo-planar imaging (EPI), applied to diffusion-weighted MRI (DWI) of the breast.An algorithm for distortion correction based on the symmetry of the distortion induced by static field inhomogeneity when the phase encoding polarity is reversed was evaluated in 36 data sets of patients who received an MRI examination that included DWI (b = 0 and 700 s/mm(2) ) and an extra b = 0 s/mm(2) sequence with opposite phase encoding polarity. The decrease of the L2 -square norm after correction between opposed phase encoding b = 0 images was calculated. Mattes mutual information between b = 0 images and fat-suppressed T2 -weighted images was calculated before and after correction.The L2 -square norm between different phase encoding polarities for b = 0 images was reduced 94.3% on average after distortion correction. Furthermore, Mattes mutual information between b = 0 images and fat-suppressed T2 -weighted images increased significantly after correction for all cases (P < 0.001).Geometric distortion correction in DWI of the breast results in higher similarity of DWI to anatomical non-EPI T2 -weighted images and would potentially allow for a more reliable lesion segmentation mapping among different MRI modalities

Peripheral blood cells inform on the presence of breast cancer: A population‐based case–control study

Tumor–host interactions extend beyond the local microenvironment and cancer development largely depends on the ability of malignant cells to hijack and exploit the normal physiological processes of the host. Here, we established that many genes within peripheral blood cells show differential expression when an untreated breast cancer (BC) is present, and harnessed this fact to construct a 50-gene signature that distinguish BC patients from population-based controls. Our results were derived from a series of large datasets within our unique population-based Norwegian Women and Cancer cohort that allowed us to investigate the influence of medications and tumor characteristics on our blood-based test, and were further tested in two external datasets. Our 50-gene signature contained cytostatic signals including the specific suppression of the immune response and medications influencing transcription involved in those processes were identified as confounders. Through analysis of the biological processes differentially expressed in blood, we were able to provide a rationale as to why the systemic response of the host may be a reliable marker of BC, characterized by the underexpression of both immune-specific pathways and “universal” cell programs driven by MYC (i.e., metabolism, growth and cell cycle). In conclusion, gene expression of peripheral blood cells is markedly perturbed by the specific presence of carcinoma in the breast and these changes simultaneously engage a number of systemic cytostatic signals emerging connections with immune escape of BC

Peripheral blood cells inform on the presence of breast cancer: A population‐based case–control study

Tumor–host interactions extend beyond the local microenvironment and cancer development largely depends on the ability of malignant cells to hijack and exploit the normal physiological processes of the host. Here, we established that many genes within peripheral blood cells show differential expression when an untreated breast cancer (BC) is present, and harnessed this fact to construct a 50-gene signature that distinguish BC patients from population-based controls. Our results were derived from a series of large datasets within our unique population-based Norwegian Women and Cancer cohort that allowed us to investigate the influence of medications and tumor characteristics on our blood-based test, and were further tested in two external datasets. Our 50-gene signature contained cytostatic signals including the specific suppression of the immune response and medications influencing transcription involved in those processes were identified as confounders. Through analysis of the biological processes differentially expressed in blood, we were able to provide a rationale as to why the systemic response of the host may be a reliable marker of BC, characterized by the underexpression of both immune-specific pathways and “universal” cell programs driven by MYC (i.e., metabolism, growth and cell cycle). In conclusion, gene expression of peripheral blood cells is markedly perturbed by the specific presence of carcinoma in the breast and these changes simultaneously engage a number of systemic cytostatic signals emerging connections with immune escape of BC

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Background Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. Purpose To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Study Type Prospective. SUBJECTS Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Field Strength/Sequence Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Assessment Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Statistical Tests Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann–Whitney tests were performed for univariate comparisons. Results For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Data Conclusion Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. Level of Evidence: 3 Technical Efficacy: Stage

Variation in tumor cell content as described by PCA of the choline region.

<p>(A) The score plot of the choline-containing metabolite region of the spectra, colored according to the tumor cell content (%) of the corresponding biopsies. (B) The corresponding loading profile of PC1 explaining 71.3% of the total variation of the data.</p

HR MAS spectra and illustration of typical features observed in the corresponding HES images (200X).

<p>(A) Invasive ductal carcinoma with an estimated tumor content of 80% in the analysed biopsy. (B) Invasive mucinous carcinoma with an estimated tumor content of 60% in the analysed biopsy. (C) Normal breast tissue (adjacent to tumor). No tumor cells were detected, and the HES image shows the typical feature of normal terminal lobular duct units. The poor signal to noise ratio in this spectrum is probably due to the high level of connective tissue (85%).</p