Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region-optimised virtual coils.

High acceleration factors in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak-like artefacts from flow in the femoral blood vessels, possibly concealing important anatomical information. Region-optimised virtual (ROVir) coils is a beamforming-based framework to create virtual coils that maximise signal in a region of interest while minimising signal in a region of interference. In this study, the potential of removing femoral flow streak artefacts in prostate MRF using ROVir coils is demonstrated in silico and in vivo. The ROVir framework was applied to radial MRF k-space data in an automated pipeline designed to maximise prostate signal while minimising signal from the femoral vessels. The method was tested in 15 asymptomatic volunteers at 3 T. The presence of streaks was visually assessed and measurements of whole prostate T1, T2 and signal-to-noise ratio (SNR) with and without streak correction were examined. In addition, a purpose-built simulation framework in which blood flow through the femoral vessels can be turned on and off was used to quantitatively evaluate ROVir's ability to suppress streaks in radial prostate MRF. In vivo it was shown that removing selected ROVir coils visibly reduces streak-like artefacts from the femoral blood flow, without increasing the reconstruction time. On average, 80% of the prostate SNR was retained. A similar reduction of streaks was also observed in silico, while the quantitative accuracy of T1 and T2 mapping was retained. In conclusion, ROVir coils efficiently suppress streaking artefacts from blood flow in radial MRF of the prostate, thereby improving the visual clarity of the images, without significant sacrifices to acquisition time, reconstruction time and accuracy of quantitative values. This is expected to help enable T1 and T2 mapping of prostate cancer in clinically viable times, aiding differentiation between prostate cancer from noncancer and healthy prostate tissue.

A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness.

In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-MRSI data of the prostate in prostate cancer (PCa) patients. MCR-ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1 H-MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR-ALS and assigned to specific tissue types. Using these components, MCR-ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t-test, p < 0.001). This result was achieved including voxels with low-quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low- and high-risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR-ALS analysis of 1 H-MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR-ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients.

Added Value of [18F]PSMA-1007 PET/CT and PET/MRI in Patients With Biochemically Recurrent Prostate Cancer: Impact on Detection Rates and Clinical Management.

BACKGROUND: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) can change management in a large fraction of patients with biochemically recurrent prostate cancer (BCR). PURPOSE: To investigate the added value of PET to MRI and CT for this patient group, and to explore whether the choice of the PET paired modality (PET/MRI vs. PET/CT) impacts detection rates and clinical management. STUDY TYPE: Retrospective. SUBJECTS: 41 patients with BCR (median age [range]: 68 [55-78]). FIELD STRENGTH/SEQUENCE: 3T, including T1-weighted gradient echo (GRE), T2-weighted turbo spin echo (TSE) and dynamic contrast-enhanced GRE sequences, diffusion-weighted echo-planar imaging, and a T1-weighted TSE spine sequence. In addition to MRI, [18F]PSMA-1007 PET and low-dose CT were acquired on the same day. ASSESSMENT: Images were reported using a five-point Likert scale by two teams each consisting of a radiologist and a nuclear medicine physician. The radiologist performed a reading using CT and MRI data and a joint reading between radiologist and nuclear medicine physician was performed using MRI, CT, and PET from either PET/MRI or PET/CT. Findings were presented to an oncologist to create intended treatment plans. Intrareader and interreader agreement analysis was performed. STATISTICAL TESTS: McNemar test, Cohen's κ, and intraclass correlation coefficients. A P-value <0.05 was considered significant. RESULTS: 7 patients had positive findings on MRI and CT, 22 patients on joint reading with PET/CT, and 18 patients joint reading with PET/MRI. For overall positivity, interreader agreement was poor for MR and CT (κ = 0.36) and almost perfect with addition of PET (PET/CT κ = 0.85, PET/MRI κ = 0.85). The addition of PET from PET/CT and PET/MRI changed intended treatment in 20 and 18 patients, respectively. Between joint readings, intended treatment was different for eight patients. DATA CONCLUSION: The addition of [18F]PSMA-1007 PET/MRI or PET/CT to MRI and CT may increase detection rates, could reduce interreader variability, and may change intended treatment in half of patients with BCR. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

An optimized MALDI MSI protocol for spatial detection of tryptic peptides in fresh frozen prostate tissue.

MALDI MS imaging (MSI) is a powerful analytical tool for spatial peptide detection in heterogeneous tissues. Proper sample preparation is crucial to achieve high quality, reproducible measurements. Here we developed an optimized protocol for spatially resolved proteolytic peptide detection with MALDI time-of-flight MSI of fresh frozen prostate tissue sections. The parameters tested included four different tissue washes, four methods of protein denaturation, four methods of trypsin digestion (different trypsin densities, sprayers, and incubation times), and five matrix deposition methods (different sprayers, settings, and matrix concentrations). Evaluation criteria were the number of detected and excluded peaks, percentage of high mass peaks, signal-to-noise ratio, spatial localization, and average intensities of identified peptides, all of which were integrated into a weighted quality evaluation scoring system. Based on these scores, the optimized protocol included an ice-cold EtOH+H2 O wash, a 5 min heating step at 95°C, tryptic digestion incubated for 17h at 37°C and CHCA matrix deposited at a final amount of 1.8 µg/mm2 . Including a heat-induced protein denaturation step after tissue wash is a new methodological approach that could be useful also for other tissue types. This optimized protocol for spatial peptide detection using MALDI MSI facilitates future biomarker discovery in prostate cancer and may be useful in studies of other tissue types.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival.

BACKGROUND: Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer. METHODS: DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT. RESULTS: DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049). CONCLUSION: Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study.

BACKGROUND: The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease. METHODS: From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs). RESULTS: Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women. CONCLUSIONS: We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples.

Metabolic profiling is widely used for large-scale association studies, based on biobank material. The main obstacle to the translation of metabolomic findings into clinical application is the lack of standardization, making validation in independent cohorts challenging. One reason for this is sensitivity of metabolites to preanalytical conditions. We present a systematic investigation of the effect of delayed centrifugation on the levels of NMR-measured metabolites and lipoproteins in serum and plasma samples. Blood was collected from 20 anonymous donors, of which 10 were recruited from an obesity clinic. Samples were stored at room temperature until centrifugation after 30 min, 1, 2, 4, or 8 h, which is within a realistic time scenario in clinical practice. The effect of delaying centrifugation on plasma and serum metabolic concentrations, and on concentrations of lipoprotein subfractions, was investigated. Our results show that lipoproteins are only minimally affected by a delay in centrifugation while metabolite levels are more sensitive to a delay. Metabolites significantly increased or decreased in concentration depending on delay duration. Further, we describe differences in the stability of serum and plasma, showing that plasma is more stable for metabolites, while lipoprotein subfractions are equally stable for both types of matrices.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T.

PURPOSE: The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value. MATERIALS AND METHODS: Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm2 ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm2 ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times. RESULTS: Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2p = 100 ms) and 60 % (T2p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm2 was around 8% lower than at b = 0 s/mm2 (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01). CONCLUSION: The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.

OBJECTIVE: Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). MATERIALS AND METHODS: T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. RESULTS: AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). DISCUSSION: All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s.

Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort.

Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions. Twenty informed, consented patients with confirmed benign breast lesions underwent repeated DWI (3 T) using: sagittal single-shot spin-echo echo planar imaging, bipolar encoding, TR/TE: 11,600/86 ms, FOV: 180 x 180 mm, matrix: 90 x 90, slices: 60 x 2.5 mm, iPAT: GRAPPA 2, fat suppression, and 13 b-values: 0-700 s/mm2 . A phase-reversed scan (b = 0 s/mm2 ) was acquired for distortion correction. Voxel-wise repeat-measures coefficients of variation (CoVs) were derived for monoexponential (apparent diffusion coefficient [ADC]), biexponential (intravoxel incoherent motion: f, D, D*) and stretched exponential (α, DDC) across the parameter histograms for lesion regions of interest (ROIs). Goodness-of-fit for each representation was assessed by Bayesian information criterion. The volume of interest (VOI) definition was repeatable (CoV 13.9%). Within lesions, and across both visits and the cohort, there was no dominant best-fit model, with all representations giving the best fit for a fraction of the voxels. Diffusivity measures from the signal representations (ADC, D, DDC) all showed good repeatability (CoV < 10%), whereas parameters associated with pseudodiffusion (f, D*) performed poorly (CoV > 50%). The stretching exponent α was repeatable (CoV < 12%). This pattern of repeatability was consistent over the central part of the parameter percentiles. Assumptions often made in diffusion studies about analysis choices will influence the detectability of changes, potentially obscuring useful information. No single signal representation prevails within or across lesions, or across repeated visits; parameter robustness is therefore a critical consideration. Our results suggest that stretched exponential representation is more repeatable than biexponential, with pseudodiffusion parameters unlikely to provide clinically useful biomarkers.

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.

OBJECTIVES: To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. MATERIALS AND METHODS: Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. RESULTS: The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. CONCLUSION: An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.

Serum levels of inflammation-related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers.

Angiogenesis is necessary for tumor growth and has been targeted in breast cancer; however, it is unclear which patients will respond and benefit from antiangiogenic therapy. We report noninvasive monitoring of patient response to neoadjuvant chemotherapy given alone or in combination with anti-vascular endothelial growth factor (bevacizumab) in a randomized clinical trial. At four time points during neoadjuvant chemotherapy ± bevacizumab of receptor tyrosine-protein kinase erbB-2-negative breast cancers, we measured metabolites and inflammation-related markers in patient's serum. We report significant changes in the levels of several molecules induced by bevacizumab, the most prominent being an increase in pentraxin 3 (PTX3) and von Willebrand factor (VWF). Serum levels of AXL, VWF and pulmonary and activation-regulated cytokine (PARC/CCL18) reflected response to chemotherapy alone or in combination with bevacizumab. We further analyzed serum cytokines in relation to tumor characteristics such as gene expression, tumor metabolites and tumor infiltrating leukocytes. We found that VWF and growth-differentiation factor 15 tumor mRNA levels correlated with their respective serum protein levels suggesting that these cytokines may be produced by tumors and outflow to the bloodstream while influencing the tumor microenvironment locally. Finally, we used binomial logistic regression which allowed to predict patient's response using only 10 noninvasive biomarkers. Our study highlights the potential of monitoring circulating levels of cytokines and metabolites during breast cancer therapy.

Simultaneous Detection of Zinc and Its Pathway Metabolites Using MALDI MS Imaging of Prostate Tissue.

Levels of zinc, along with its mechanistically related metabolites citrate and aspartate, are widely reported as reduced in prostate cancer compared to healthy tissue and are therefore pointed out as potential cancer biomarkers. Previously, it has only been possible to analyze zinc and metabolites by separate detection methods. Through matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI), we were for the first time able to demonstrate, in two different sample sets (n = 45 and n = 4), the simultaneous spatial detection of zinc, in the form of ZnCl3-, together with citrate, aspartate, and N-acetylaspartate on human prostate cancer tissues. The reliability of the ZnCl3- detection was validated by total zinc determination using laser ablation inductively coupled plasma MSI on adjacent serial tissue sections. Zinc, citrate, and aspartate were correlated with each other (range r = 0.46 to 0.74) and showed a significant reduction in cancer compared to non-cancer epithelium (p < 0.05, log2 fold change range: -0.423 to -0.987), while no significant difference between cancer and stroma tissue was found. Simultaneous spatial detection of zinc and its metabolites is not only a valuable tool for analyzing the role of zinc in prostate metabolism but might also provide a fast and simple method to detect zinc, citrate, and aspartate levels as a biomarker signature for prostate cancer diagnostics and prognostics.

Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations.

PURPOSE: To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm2 in benign and malignant breast lesions. METHODS: Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm2 ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated. RESULTS: The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at bmax = 600 s/mm2 already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction. CONCLUSION: Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

Stromal Collagen Content in Breast Tumors Correlates With In Vivo Diffusion-Weighted Imaging: A Comparison of Multi b-Value DWI With Histologic Specimen From Benign and Malignant Breast Lesions.

BACKGROUND: Increased deposition and reorientation of stromal collagen fibers are associated with breast cancer progression and invasiveness. Diffusion-weighted imaging (DWI) may be sensitive to the collagen fiber organization in the stroma and could provide important biomarkers for breast cancer characterization. PURPOSE: To understand how collagen fibers influence water diffusion in vivo and evaluate the relationship between collagen content and the apparent diffusion coefficient (ADC) and the signal fractions of the biexponential model using a high b-value scheme. STUDY TYPE: Prospective. SUBJECTS/SPECIMENS: Forty-five patients with benign (n = 8), malignant (n = 36), and ductal carcinoma in situ (n = 1) breast tumors. Lesions and normal fibroglandular tissue (n = 9) were analyzed using sections of formalin-fixed, paraffin-embedded tissue stained with hematoxylin, erythrosine, and saffron. FIELD STRENGTH/SEQUENCE: MRI (3T) protocols: Protocol I: Twice-refocused spin-echo echo-planar imaging with: echo time (TE) 85 msec; repetition time (TR) 9300/11600 msec; matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm3 ; b-values: 0 and 700 s/mm2 . Protocol II: Stejskal-Tanner spin-echo echo-planar imaging with: TE: 88 msec; TR: 10600/11800 msec, matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm3 ; b-values [0, 200, 600, 1200, 1800, 2400, 3000] s/mm2 . ASSESSMENT: Area fractions of cellular and collagen content in histologic sections were quantified using whole-slide image analysis and compared with the corresponding DWI parameters. STATISTICAL TESTS: Correlations were assessed using Pearson's r. Univariate analysis of group median values was done using the Mann-Whitney U-test. RESULTS: Collagen content correlated with the fast signal fraction (r = 0.67, P < 0.001) and ADC (r = 0.58, P < 0.001) and was lower (P < 0.05) in malignant lesions than benign and normal tissues. Cellular content correlated inversely with the fast signal fraction (r = -0.67, P < 0.001) and ADC (r = -0.61, P < 0.001) and was different (P < 0.05) between malignant, benign, and normal tissues. DATA CONCLUSION: Our findings suggest stromal collagen content increases diffusivity observed by MRI and is associated with higher ADC and fast signal fraction of the biexponential model. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1868-1878.

Relative Enhanced Diffusivity in Prostate Cancer: Protocol Optimization and Diagnostic Potential.

BACKGROUND: Relative enhanced diffusivity (RED) is a potential biomarker for indirectly measuring perfusion in tissue using diffusion-weighted magnetic resonance imaging (MRI) with 3 b values. PURPOSE: To optimize the RED MRI protocol for the prostate, and to investigate its potential for prostate cancer (PCa) diagnosis. STUDY TYPE: Prospective. POPULATION: Ten asymptomatic healthy volunteers and 35 patients with clinical suspicion of PCa. SEQUENCE: 3T T2 - and diffusion-weighted MRI with b values: b = 0, 50, [100], 150, [200], 250, [300], 400, 800 s/mm2 (values in brackets were only used for patients). ASSESSMENT: Monte Carlo simulations were performed to assess noise sensitivity of RED as a function of intermediate b value. Volunteers were scanned 3 times to assess repeatability of RED. Patient data were used to investigate RED's potential for discriminating between biopsy-confirmed cancer and healthy tissue, and between true and false positive radiological findings. STATISTICAL TESTS: Within-subject coefficient of variation (WCV) to assess repeatability and receiver-operating characteristic curve analysis and logistic regression to assess diagnostic performance of RED. RESULTS: The repeatability was acceptable (WCV = 0.2-0.3) for all intermediate b values tested, apart from b = 50 s/mm2 (WCV = 0.3-0.4). The simulated RED values agreed well with the experimental data, showing that an intermediate b value between 150-250 s/mm2 minimizes noise sensitivity in both peripheral zone (PZ) and transition zone (TZ). RED calculated with the b values 0, 150 and 800 s/mm2 was significantly higher in tumors than in healthy tissue in both PZ (P < 0.001, area under the curve [AUC] = 0.85) and PZ + TZ (P < 0.001, AUC = 0.84). RED was shown to aid apparent diffusion coefficient (ADC) in differentiating between false-positive findings and true-positive PCa in the PZ (AUC; RED = 0.71, ADC = 0.74, RED+ADC = 0.77). DATA CONCLUSION: RED is a repeatable biomarker that may have value for prostate cancer diagnosis. An intermediate b value in the range of 150-250 s/mm2 minimizes the influence of noise and maximizes repeatability. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1900-1910.

Effect of exercise training on cardiac metabolism in rats with heart failure.

Objectives. Heart failure (HF) impairs resting myocardial energetics, myocardial mitochondrial performance, and maximal oxygen uptake (VO2max). Exercise training is included in most rehabilitation programs and benefits HF patients. However, the effect of exercise intensity on cardiac mitochondrial respiration and concentrations of the key bioenergetic metabolites phosphocreatine (PCr), adenosine triphosphate (ATP), and inorganic phosphate (Pi) is unclear. This study aimed to investigate the effects of exercise training at different intensities in rats with HF. Methods. Rats underwent myocardial infarction or sham operations and were divided into three subgroups: sedentary, moderate intensity, or high intensity. The impact of HF and 6 weeks of exercise training on energy metabolism was evaluated by 31P magnetic resonance spectroscopy and mitochondrial respirometry. The concentrations of PCr, ATP, and Pi were quantified by magnetic resonance spectroscopy. VO2max was measured by treadmill respirometry. Results. Exercise training increased VO2max in sham and HF. PCr/ATP ratio was reduced in HF (p < .01) and remained unchanged by exercise training. PCr concentration was significantly lower in HF compared to sham (p < .01). Moderate and high-intensity exercise training increased ATP in HF and sham. HF impaired complex I (CI) and complex II (p = .034) respiration. High-intensity exercise training recovered CI respiration in HF rats compared to HF sedentary (p = .014). Conclusions. Exercise training improved cardiac performance, as indicated by increased VO2max and higher exercise capacity, without changing the myocardial PCr/ATP ratio. These observations suggest that the PCr/ATP biomarker is not suited to evaluate the beneficial effects of exercise training in the heart. The exact mechanisms require further investigations, as exercise training did increase ATP levels and CI respiration.

Assessing Treatment Response and Prognosis by Serum and Tissue Metabolomics in Breast Cancer Patients.

Patients with locally advanced breast cancer have a worse prognosis compared to patients with localized tumors and require neoadjuvant treatment before surgery. The aim of this study was to characterize the systemic metabolic effect of neoadjuvant chemotherapy in patients with large primary breast cancers and to relate these changes to treatment response and long-term survival. This study included 132 patients with large primary breast tumors randomized to receive neoadjuvant chemotherapy with or without the addition of the antiangiogenic drug Bevacizumab. Tumor biopsies and serum were collected before and during treatment and, serum additionally 6 weeks after surgery. Samples were analyzed by nuclear magnetic resonance spectroscopy (NMR). Correlation analysis showed low correlations between metabolites measured in cancer tissue and serum. Multilevel partial least squares discriminant analysis (PLS-DA) showed clear changes in serum metabolite levels during treatment (p-values ≤ 0.001), including unfavorable changes in lipid levels. PLS-DA revealed metabolic differences between tissue samples from survivors and nonsurvivors collected 12 weeks into treatment with an accuracy of 72% (p-value = 0.005); however, this was not evident in serum samples. Our results demonstrate a potential clinical application for serum-metabolomics for patient monitoring during and after treatment, and indicate potential for tissue NMR spectroscopy for predicting patient survival.

Effect of Repeated Freeze-Thaw Cycles on NMR-Measured Lipoproteins and Metabolites in Biofluids.

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze-thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9%  for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze-thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform.

Accuracy of breast cancer lesion classification using intravoxel incoherent motion diffusion-weighted imaging is improved by the inclusion of global or local prior knowledge with bayesian methods.

BACKGROUND: Diffusion-weighted MRI (DWI) has potential to noninvasively characterize breast cancer lesions; models such as intravoxel incoherent motion (IVIM) provide pseudodiffusion parameters that reflect tissue perfusion, but are dependent on the details of acquisition and analysis strategy. PURPOSE: To examine the effect of fitting algorithms, including conventional least-squares (LSQ) and segmented (SEG) methods as well as Bayesian methods with global shrinkage (BSP) and local spatial (FBM) priors, on the power of IVIM parameters to differentiate benign and malignant breast lesions. STUDY TYPE: Prospective patient study. SUBJECTS: 61 patients with confirmed breast lesions. FIELD STRENGTH/SEQUENCE: DWI (bipolar SE-EPI, 13 b values) was included in a clinical MR protocol including T2 -weighted and dynamic contrast-enhanced MRI on a 3T scanner. ASSESSMENT: The IVIM model was fitted voxelwise in lesion regions of interest (ROIs), and derived parameters were compared across methods within benign and malignant subgroups (correlation, coefficients of variation). Area under receiver operator characteristic curves (ROC AUCs) were calculated to determine discriminatory power of parameter combinations from all fitting methods. STATISTICAL TESTS: Kruskal-Wallis, Mann-Whitney, Pearson correlation. RESULTS: All methods provided useful IVIM parameters; D was well-correlated across all methods (r > 0.8), with a wider range for f and D* (0.3-0.7). Fitting methods gave detectable differences in parameters, but all showed increased f and decreased D in malign lesions. D was the most discriminatory single parameter, with LSQ performing least well (AUC 0.83). In general, ROC AUCs were maximized by the inclusion of pseudodiffusion parameters, and by the use of Bayesian methods incorporating prior information (maximum AUC of 0.92 for BSP). DATA CONCLUSION: DWI performs well at classifying breast lesions, but careful consideration of analysis procedure can improve performance. D is the most discriminatory single parameter, but including pseudodiffusion parameters (f and D*) increases ROC AUC. Bayesian methods outperformed conventional least-squares and segmented fitting methods for breast lesion classification. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1478-1488.