Incorporating Spatial and Spectral Saturation Modules Into MR Fingerprinting.

In this work, we introduce spatial and chemical saturation options for artefact reduction in magnetic resonance fingerprinting (MRF) and assess their impact on T and T mapping accuracy. An existing radial MRF pulse sequence was modified to enable spatial and chemical saturation. Phantom experiments were performed to demonstrate flow artefact reduction and evaluate the accuracy of the T and T maps.

Stability in fecal metabolites amid a diverse gut microbiome composition: a one-month longitudinal study of variability in healthy individuals.

An extensive network of microbial-host interactions exists in the gut, making the gut microbiome a complex ecosystem to untangle. The microbial composition and the fecal metabolites are important readouts to investigate intricate microbiota-diet-host interplay. However, this ecosystem is dynamic, and it is of interest to understand the degree and timescale of changes occurring in the gut microbiota, during disease as well as in healthy individuals.

Sources of variation in the serum metabolome of female participants of the HUNT2 study.

The aim of this study was to explore the intricate relationship between serum metabolomics and lifestyle factors, shedding light on their impact on health in the context of breast cancer risk. Detailed metabolic profiles of 2283 female participants in the Trøndelag Health Study (HUNT study) were obtained through nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS).We show that lifestyle-related variables can explain up to 30% of the variance in individual metabolites.

Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes.

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic.

Quantification of multiple steroid hormones in serum and human breast cancer tissue by liquid chromatography-tandem mass spectrometry analysis.

Introduction: Systemic and local steroid hormone levels may function as novel prognostic and predictive biomarkers in breast cancer patients. We aimed at developing a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the simultaneous measurement of multiple, biologically pivotal steroid hormones in human serum and breast cancer tissue.

Methods: The quantitative method consisted of liquid-liquid extraction, Sephadex LH-20 chromatography for tissue extracts, and analysis of steroid hormones by liquid-chromatography-tandem mass spectrometry.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer.

Introduction: The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods: We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

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.

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) 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.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer.

Purpose: Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.

Experimental Design: Breast cancer patients (27) underwent multi-parametric 3T MRI at four time points during treatment.

Label-set impact on deep learning-based prostate segmentation on MRI.

Background: Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance.

Pelvic PET/MR attenuation correction in the image space using deep learning.

Introduction: The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction.

Methods: A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction.

Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres.

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa).

Patients And Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search.

The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.

Purpose: To evaluate the reproducibility of radiomics features derived via different pre-processing settings from paired T2-weighted imaging (T2WI) prostate lesions acquired within a short interval, to select the setting that yields the highest number of reproducible features, and to evaluate the impact of disease characteristics (i.e., clinical variables) on features reproducibility.

Association of serum cortisol and cortisone levels and risk of recurrence after endocrine treatment in breast cancer.

Metabolic reprogramming in breast cancer involves changes in steroid hormone synthesis and metabolism. Alterations in estrogen levels in both breast tissue and blood may influence carcinogenesis, breast cancer growth, and response to therapy. Our aim was to examine whether serum steroid hormone concentrations could predict the risk of recurrence and treatment-related fatigue in patients with breast cancer.

The association between circulating lipoprotein subfractions and lipid content in coronary atheromatous plaques assessed by near-infrared spectroscopy.

Background: Lipid content in coronary atheromatous plaques, measured by near-infrared spectroscopy (NIRS), can predict the risk of future coronary events. Biomarkers that reflect lipid content in coronary plaques may therefore improve coronary artery disease (CAD) risk assessment.

Purpose: We aimed to investigate the association between circulating lipoprotein subfractions and lipid content in coronary atheromatous plaques in statin-treated patients with stable CAD undergoing percutaneous coronary intervention.

Lipoprotein subfraction profiling in the search of new risk markers for myocardial infarction: The HUNT study.

Background: Traditional biomarkers used to measure risk of myocardial infarction (MI) only explain a modest proportion of the incidence. Lipoprotein subfractions have the potential to improve risk prediction of MI.

Aim: We aimed to identify lipoprotein subfractions that were associated with imminent MI risk.

Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study.

Background: The serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available.

Methods: Women participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected (=453 case-control pairs).

Primary Treatment Effects for High-Grade Serous Ovarian Carcinoma Evaluated by Changes in Serum Metabolites and Lipoproteins.

High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest ovarian cancer subtype. Despite advances in treatment, the overall prognosis remains poor. Regardless of efforts to develop biomarkers to predict surgical outcome and recurrence risk and resistance, reproducible indicators are scarce.

CROPro: a tool for automated cropping of prostate magnetic resonance images.

Purpose: To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images.

Approach: CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.

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.

Metabolic profiling of colorectal cancer organoids: A comparison between high-resolution magic angle spinning magnetic resonance spectroscopy and solution nuclear magnetic resonance spectroscopy of polar extracts.

Patient-derived cancer cells cultured in vitro are a cornerstone of cancer metabolism research. More recently, the introduction of organoids has provided the research community with a more versatile model system. Physiological structure and organization of the cell source tissue are maintained in organoids, representing a closer link to in vivo tumor models.

Small LDL subfractions are associated with coronary atherosclerosis despite no differences in conventional lipids.

Lipoprotein subfractions currently represent a new source of cardiovascular disease (CVD) risk markers that may provide more information than conventional lipid measures. We aimed to investigate whether lipoprotein subfractions are associated with coronary atherosclerosis in patients without prior known CVD. Fasting serum samples from 60 patients with suspected coronary artery disease (CAD) were collected before coronary angiography and analyzed by nuclear magnetic resonance (NMR) spectroscopy.