Metabolism of prostate cancer by magnetic resonance spectroscopy (MRS)

An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis

MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.

Factors influencing the detection rate of fumarate peak in 1H MR spectroscopy of fumarate hydratase-deficient renal cell carcinoma at 3 T MRI

AIM

To identify factors that may be associated with fumarate detection rate in 1H-magnetic resonance spectroscopy (MRS) in fumarate hydratase-deficient renal cell carcinoma (FH-RCC).

MATERIALS AND MEHODS

Between February 2018 and March 2022, 16 FH-RCC patients with 30 lesions underwent 1H-MRS. Detection results were classified as having a detected fumarate peak (n=12), undetected peak (n=10), or technical failure (n=8). Factors including tumour size, tumour location, treatment history, and metastasis status were collected and analysed. A Bayesian logistic regression model was applied to evaluate the association between these factors and the detection result.

RESULTS

Bayesian analysis demonstrated significant associations between fumarate detection results and the following factors: long-axis diameter (odds ratio [OR] of 1.64; 95% confidence interval [CI] of 1.07-2.53), short-axis diameter (OR of 1.90; 95% CI of 1.19-3.06), voxel size (OR of 2.85; 95% CI of 1.70-4.75), treatment history (OR of 0.35; 95% CI of 0.21-0.58), non-metastatic state (OR of 2.45; 95% CI of 1.48-4.06), and lymph node metastasis (OR of 0.35; 95% CI of 0.21-0.58). Technical failure results were associated with factors such as treatment history (OR of 2.59; 95% CI of 1.37-4.66), non-metastatic state (OR of 0.36; 95% CI of 0.19-0.66), and lymph node metastasis (OR of 2.61; 95% CI of 1.39-4.74).

CONCLUSION

Tumour size, treatment history, and metastasis character were associated with the detection of abnormal fumarate accumulation. This finding will serve as a reference for interpreting 1H-MRS results and for selecting suitable scenarios to evaluate FH-RCC.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS

BACKGROUND

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time.

PURPOSE

We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS.

METHODS

The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising.

RESULTS

With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels.

CONCLUSIONS

The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

Correlation between choline kinase alpha expression and 11C-choline accumulation in breast cancer using positron emission tomography/computed tomography: a retrospective study

Choline kinase (CK) is reportedly overexpressed in various malignancies. Among its isoforms, CKα overexpression is presumably related to oncogenic change. Choline positron emission tomography (PET) is reportedly useful for detecting and evaluating therapy outcomes in malignancies. In this study, we investigated the correlation between CKα expression and 11C-choline accumulation in breast cancer cells. We also compared the CKα expression level with other pathological findings for investigating tumour activity. Fifty-six patients with breast cancer (mean age: 51 years) who underwent their first medical examination between May 2007 and December 2008 were enrolled. All the patients underwent 11C-choline PET/computed tomography imaging prior to surgery. The maximum standardised uptake value was recorded for evaluating 11C-choline accumulation. The intensity of CKα expression was classified using immunostaining. A significant correlation was observed between CKα expression and 11C-choline accumulation (P < 0.0001). A comparison of breast cancer mortality demonstrated that strong CKα expression was associated with a shorter survival time (P < 0.0001). 11C-choline accumulation was also negatively correlated with survival time (P < 0.0001). Tumours with strong CKα expression are reportedly highly active in breast cancer. A correlation was observed between CKα expression and 11C-choline accumulation, suggesting their role as prognostic indicators of breast cancer.

Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives

Prostate cancer is the most frequent epithelial neoplasia after skin cancer in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. Prostate cancer imaging includes several radiological modalities, ranging from ultrasonography, computed tomography (CT), and magnetic resonance to nuclear medicine hybrid techniques such as single-photon emission computed tomography (SPECT)/CT and positron emission tomography (PET)/CT. Innovation in radiopharmaceutical compounds has introduced specific tracers with diagnostic and therapeutic indications, opening the horizons to targeted and very effective clinical care for patients with prostate cancer. The aim of the present review is to illustrate the current knowledge and future perspectives of nuclear medicine, including stand-alone diagnostic techniques and theragnostic approaches, in the clinical management of patients with prostate cancer from initial staging to advanced disease.

Evaluation of prostate cancer tissue metabolomics: would clinics utilise it for diagnosis?

The difficulty of diagnosing prostate cancer (PC) with the available biomarkers frequently leads to over-diagnosis and overtreatment of PC, underscoring the need for novel molecular signatures. The purpose of this review is to provide a summary of the currently available cellular metabolomics for PC molecular signatures. A comprehensive search on PubMed was conducted to find studies published between January 2004 and August 2022 that reported biomarkers for PC detection, development, aggressiveness, recurrence and treatment response. Although potential studies have reported the presence of distinguishing molecules that can distinguish between benign and cancerous prostate tissue. However, there are few studies looking into signature molecules linked to disease development, therapy response or tumour recurrence. The majority of these studies use high-dimensional datasets, and the number of potential metabolites investigated frequently exceeds the size of the available samples. In light of this, pre-analytical, statistical, methodological and confounding factors such as antiandrogen therapy (NAT) may also be linked to the identified chemometric multivariate differences between PC and relevant control samples in the datasets. Despite the methodological and procedural challenges, a range of methodological groups and processes have consistently identified a number of signature metabolites and pathways that appear to imply a substantial involvement in the cellular metabolomics of PC for tumour formation and recurrence.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Imaging in translational cancer research

This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

Developments in proton MR spectroscopic imaging of prostate cancer

In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.

Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer

Objectives

This study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors.

Materials and methods

Prospectively undersampled 5D echo-planar J-resolved spectroscopic imaging (EP-JRESI) data were acquired in nine prostate cancer (PCa) patients and three healthy males. The 5D EP-JRESI data were reconstructed using DL and compared with gradient sparsity-based Total Variation (TV) and Perona-Malik (PM) methods. A hybrid reconstruction technique, Dictionary Learning-Total Variation (DLTV), was also designed to further improve the quality of reconstructed spectra.

Results

The CS reconstruction of prospectively undersampled (8x and 12x) 5D EP-JRESI data acquired in prostate cancer and healthy subjects were performed using DL, DLTV, TV and PM. It is evident that the hybrid DLTV method can unambiguously resolve 2D J-resolved peaks including myo-inositol, citrate, creatine, spermine and choline.

Conclusion

Improved reconstruction of the accelerated 5D EP-JRESI data was observed using the hybrid DLTV. Accelerated acquisition of in vivo 5D data with as low as 8.33% samples (12x) corresponds to a total scan time of 14 min as opposed to a fully sampled scan that needs a total duration of 2.4 h (TR = 1.2 s, 32 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{x}$$\end{document}kx×16 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{y}$$\end{document}ky×8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{z}$$\end{document}kz, 512 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${t}_{2}$$\end{document}t2 and 64 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${t}_{1}$$\end{document}t1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-022-01029-z.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Change in knee cartilage components in stroke patients with genu recurvatum analysed by zero TE MR imaging

Genu recurvatum in stroke patients with hemiplegia causes readily cumulative damage and degenerative changes in the knee cartilage. It is important to detect early cartilage lesions for appropriate treatment and rehabilitation. The purpose of this cross-sectional study was to provide a theoretical basis for the early rehabilitation of hemiplegia patients. We used a zero TE double-echo imaging sequence to analyse the water content in knee joint cartilage at 12 different sites of 39 stroke patients with genu recurvatum and 9 healthy volunteers using a metric similar to the porosity index. When comparing the hemiplegic limb vs. the nonhemiplegic limb in patients, the ratios of the deep/shallow free water content of the femur cartilages at the anterior horn (1.16 vs. 1.06) and posterior horn (1.13 vs. 1.25) of the lateral meniscus were significantly different. Genu recurvatum in stroke patients with hemiplegia can cause changes in the moisture content of knee cartilage, and the changes in knee cartilage are more obvious as the genu recurvatum increases. The "healthy limb" can no longer be considered truly healthy and should be considered simultaneously with the affected limb in the development of a rehabilitation treatment plan.

Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer

Nuclear magnetic resonance (NMR) metabolomics is a powerful analytical technique and a tool which has unique characteristics and capabilities for the evaluation of a number of biochemicals/metabolites of cancer and other disease processes that are present in biofluids (urine and blood) and tissues. The potential of NMR metabolomics in prostate cancer (PCa) has been explored by researchers and its usefulness has been documented. A large number of metabolites such as citrate, choline, and sarcosine were detected by NMR metabolomics from biofluids and tissues related to PCa and their levels were compared with controls and benign prostatic hyperplasia. The changes in the levels of these metabolites aid in the diagnosis and help to understand the dysregulated metabolic pathways in PCa. We review recent studies on in vitro and ex vivo NMR spectroscopy-based PCa metabolomics and its possible role as a diagnostic tool.

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development

Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

Deuterium Metabolic Imaging-Rediscovery of a Spectroscopic Tool

The growing demand for metabolism-specific imaging techniques has rekindled interest in Deuterium (2H) Metabolic Imaging (DMI), a robust method based on administration of a substrate (glucose, acetate, fumarate, etc.) labeled with the stable isotope of hydrogen and the observation of its metabolic fate in three-dimensions. This technique allows the investigation of multiple metabolic processes in both healthy and diseased states. Despite its low natural abundance, the short relaxation time of deuterium allows for rapid radiofrequency (RF) pulses without saturation and efficient image acquisition. In this review, we provide a comprehensive picture of the evolution of DMI over the course of recent decades, with a special focus on its potential clinical applications.

Nanomaterials for Diagnosis and Treatment of Brain Cancer: Recent Updates

Brain tumors, especially glioblastoma, remain the most aggressive form of all the cancers because of inefficient diagnosis and profiling. Nanostructures, such as metallic nanostructures, silica nano-vehicles, quantum dots, lipid nanoparticles (NPs) and polymeric NPs, with high specificity have made it possible to permeate the blood–brain barrier (BBB). NPs possess optical, magnetic and photodynamic properties that can be exploited by surface modification, bio composition, contrast agents’ encapsulation and coating by tumor-derived cells. Hence, nanotechnology has brought on a revolution in the field of diagnosis and imaging of brain tumors and cancers. Recently, nanomaterials with biomimetic functions have been introduced to efficiently cross the BBB to be engulfed by deep skin tumors and cancer malignancies for imaging. The review focuses on nanotechnology-based diagnostic and imaging approaches for exploration in brain tumors and cancers. Moreover, the review also summarizes a few strategies to image glioblastoma and cancers by multimodal functional nanocomposites for more precise and accurate clinical diagnosis. Their unique physicochemical attributes, including nanoscale sizes, larger surface area, explicit structural features and ability to encapsulate diverse molecules on their surface, render nanostructured materials as excellent nano-vehicles to cross the blood–brain barrier and convey drug molecules to their target region. This review sheds light on the current progress of various kinds of nanomaterials, such as liposomes, nano-micelles, dendrimers, carbon nanotubes, carbon dots and NPs (gold, silver and zinc oxide NPs), for efficient drug delivery in the treatment and diagnosis of brain cancer.

Biophysical Reviews: a Q1 ranked journal in biophysics and structural biology

This Editorial for Biophysical Reviews (Volume 12, Issue 5) begins with a description of the two feature articles. The first being the latest in the "Meet the Editors Series" describing Rosangela Itri-the Biophysical Reviews Executive Editor responsible for the South American region. The second feature article is by Alexandra Zidovska, the inaugural winner of the 2020 "Michèle Auger Award for Young Scientists' Independent Research." Next highlighted are the Issue contents, which consist of five Commentaries/Letters and eleven Reviews. Finally, we conclude with a description of Biophysical Reviews' ascension within the world's major journal rankings index (Elsevier, Scimago)-becoming 12th overall (out of 156) within the biophysics category and receiving the coveted Q1 rating in both biophysics and structural biology sections.