Enhancing Nonmass Lesions in the Breast: Evaluation with Proton (H) MR Spectroscopy

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.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

In vivo MR spectroscopy for breast cancer diagnosis

Breast cancer is a significant health concern in females, worldwide. In vivo proton (1H) MR spectroscopy (MRS) has evolved as a non-invasive tool for diagnosis and for biochemical characterization of breast cancer. Water-to-fat ratio, fat and water fractions and choline containing compounds (tCho) have been identified as diagnostic biomarkers of malignancy. Detection of tCho in normal breast tissue of volunteers and in lactating females limits the use of tCho as a diagnostic marker. Technological developments like high-field scanners, multi channel coils, pulse sequences with water and fat suppression facilitated easy detection of tCho. Also, quantification of tCho and its cut-off for objective assessment of malignancy have been reported. Meta-analysis of in vivo 1H MRS studies have documented the pooled sensitivities and the specificities in the range of 71-74% and 78-88%, respectively. Inclusion of MRS has been shown to enhance the diagnostic specificity of MRI, however, detection of tCho in small sized lesions (≤1 cm) is challenging even at high magnetic fields. Potential of MRS in monitoring the effect of chemotherapy in breast cancer has also been reported. This review briefly presents the potential clinical role of in vivo 1H MRS in the diagnosis of breast cancer, its current status and future developments.

Proton MR spectroscopy in the breast: Technical innovations and clinical applications

Proton magnetic resonance spectroscopy (MRS) is a promising noninvasive diagnostic technique for investigation of breast cancer metabolism. Spectroscopic imaging data may be obtained following contrast-enhanced MRI by applying the point-resolved spectroscopy sequence (PRESS) or the stimulated echo acquisition mode (STEAM) sequence from the MR voxel encompassing the breast lesion. Total choline signal (tCho) measured in vivo using either a qualitative or quantitative approach has been used as a diagnostic test in the workup of malignant breast lesions. In addition to tCho metabolites, other relevant metabolites, including multiple lipids, can be detected and monitored. MRS has been heavily investigated as an adjunct to morphologic and dynamic MRI to improve diagnostic accuracy in breast cancer, obviating unnecessary benign biopsies. Besides its use in the staging of breast cancer, other promising applications have been recently investigated, including the assessment of treatment response and therapy monitoring. This review provides guidance on spectroscopic acquisition and quantification methods and highlights current and evolving clinical applications of proton MRS. Level of Evidence 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Experimental mammary carcinogenesis - Rat models

Mammary cancer is one of the most common cancers, victimizing more than half a million of women worldwide every year. Despite all the studies in this field, the current therapeutic approaches are not effective and have several devastating effects for patients. In this way, the need to better understand the mammary cancer biopathology and find effective therapies led to the development of several rodent models over years. With this review, the authors intended to provide the readers with an overview of the rat models used to study mammary carcinogenesis, with a special emphasis on chemically-induced models.

Multiparametric MR Imaging of Breast Cancer

Breast MR imaging has increased in popularity over the past 2 decades due to evidence of its high sensitivity for cancer detection. Current clinical MR imaging approaches rely on the use of a dynamic contrast-enhanced acquisition that facilitates morphologic and semiquantitative kinetic assessments of breast lesions. The use of more functional and quantitative parameters holds promise to broaden the utility of MR imaging and improve its specificity. Because of wide variations in approaches for measuring these parameters and the considerable technical challenges, robust multicenter data supporting their routine use are not yet available, limiting current applications of many of these tools to research purposes.

Unique plasma metabolomic signature of osteonecrosis of the femoral head

Metabolomic analysis was performed to determine the metabolomic signature of osteonecrosis of the femoral head (ONFH), and to investigate the underlying relationship between the metabolomic signature and the pathogenesis of ONFH. Plasma samples were collected from 30 ONFH patients and 30 normal subjects. The global metabolomic profile was obtained through a combination of high-throughput liquid- and gas-chromatography-based mass spectrometry analyses. All statistical analyses were conducted using the R software. The results showed clear differences in the metabolomic signature between the plasma of ONFH patients compared with normal subjects. Among the 354 identified metabolites, the expression of 123 metabolites were significantly changed in ONFH patients compared with normal subjects (p < 0.05, q < 0.10). Bioinformatics analysis revealed that these abnormal metabolites were mainly involved in lipid-, glutathione-, nucleotide-, and energy-associated pathways, which might be related to enhanced inflammation, oxidative stress, and energy deficiency due to ONFH. This study provides the first metabolomic analysis of ONFH, and identifies a previously unrecognized metabolic signature in ONFH plasma. The results offer new insights into the pathological mechanisms of ONFH through its influence on metabolic pathways, providing the requisite framework for identifying biomarkers or novel targets for therapeutic intervention. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:1158-1167, 2016.

Can the signal-to-noise ratio of choline in magnetic resonance spectroscopy reflect the aggressiveness of endometrial cancer?

RATIONALE AND OBJECTIVES

To differentiate endometrial cancer (ECa) from benign lesions in endometrial or in submucosa (BLs-ESm), and investigate whether the signal-to-noise ratio (SNR) of choline-containing compounds (Cho) obtained from three-dimensional (1)H magnetic resonance spectroscopy (MRS) is associated with the aggressiveness of ECa.

MATERIALS AND METHODS

Thirty-three patients with ECa and 15 patients with BLs-ESm underwent preoperative multivoxel (1)H MRS at 3 T MR. The amplitude of Cho peak of each voxel was recorded, and the corresponding SNR of Cho peak (ChoSNR) was calculated. The maximum ChoSNR (max ChoSNR) for each lesion was identified. The max ChoSNR of ECa and BLs-ESm, as well as type I ECa and type II ECa, was compared. The relationship between max ChoSNR and pathologic characteristics of tumors, including tumor grade, stage, type, and tumor size, was analyzed.

RESULTS

The mean max ChoSNR (±standard deviation [SD]) was 30.93 ± 16.89 for ECa and 10.40 ± 3.07 for BLs-ESm (P < .001). The mean max ChoSNR for type II ECa (48.54 ± 21.46) was higher than that for type I ECa (26.19 ± 12.02, P = .001). There were no significant differences among different grades (P = .449). The Spearman coefficient between max ChoSNR and stage was 0.423 (P = .014); the difference existed only between Ia and III ECa (P = .048). The Pearson coefficient between ChoSNR and tumor size was 0.515 (P = .002).

CONCLUSIONS

The max ChoSNR obtained from MRS can differentiate ECa from BLs and type I ECa and type II ECa, but cannot differentiate between each grade ECa and each International Federation of Gynecology and Obstetrics stage ECa. However, max ChoSNR increased with the increase in International Federation of Gynecology and Obstetrics stage and size of ECa.

Molecular diagnostics

Orthopaedic infections are complex conditions that require immediate diagnosis and accurate identification of the causative organisms to facilitate appropriate management. Conventional methodologies for diagnosis of these infections sometimes lack accuracy or sufficient rapidity. Molecular diagnostics is an emerging area of bench-to-bedside research in orthopaedic infections. Examples of promising molecular diagnostics include measurement of a specific biomarker in the synovial fluid, polymerase chain reaction-based detection of bacterial genes, and metabolomic determination of responses to orthopaedic infection.

Application of metabolomics in thyroid cancer research

Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput "omics" techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.

Inflammatory cytokines and cellular metabolites as synovial fluid biomarkers of posttraumatic ankle arthritis

BACKGROUND

There is a paucity of research on posttraumatic ankle arthritis (PTAA). We aimed to identify synovial fluid PTAA biomarkers using cytokine analysis and metabolic profiling.

METHODS

Ankle joint synovial fluid was obtained from 20 patients with PTAA and 20 patients with no ankle pain and no radiographic evidence of ankle arthritis (control group). Synovial fluid samples were analyzed for IFN-γ, TNF-α, MIP-1β, MCP-1, IL-1β, IL-1Ra, IL-4, IL-6, IL-8, IL-10, IL-13, and IL-15 using ELISA and for more than 3000 metabolites using liquid and gas chromatography with mass spectroscopy. To compare presence of cytokines and metabolites between groups, t tests were used. Random forest analysis was performed on metabolites to determine whether control and PTAA samples could be differentiated based on metabolic profile.

RESULTS

IL-1Ra, IL-6, IL-8, IL-10, IL-15, and MCP-1 were significantly elevated in the PTAA group. In addition, 107 metabolites in the PTAA group were significantly altered, including derangement in amino acid, carbohydrate, lipid, and energy metabolism, extracellular matrix turnover, and collagen degradation. Random forest analysis yielded a predictive accuracy of 90% when using the metabolic profiles to distinguish between control and PTAA samples.

CONCLUSION

This study identified inflammatory cytokines and metabolites present in the synovial fluid of PTAA.

CLINICAL RELEVANCE

Several of these entities have previously been implicated in rheumatoid arthritis and osteoarthritis of the knee, but many could potentially be used as novel biomarkers of PTAA. Most importantly, the findings suggest that metabolites could be used to distinguish synovial fluid from patients with PTAA.

Lesion type and reader experience affect the diagnostic accuracy of breast MRI: a multiple reader ROC study

PURPOSE

To evaluate the influence of lesion type (mass versus non-mass) and reader experience on the diagnostic performance of breast MRI (BMRI) in a non-screening setting.

MATERIALS AND METHODS

Consecutive patients (mean age, 55 ± 12 years) with breast lesions that were verified by biopsy or surgery, and who had had BMRI as part of their diagnostic workup, were eligible for this retrospective single-center study. Cancers diagnosed by biopsy before BMRI were excluded to eliminate biological and interpretation bias due to biopsy or chemotherapy effects (n=103). Six blinded readers (experience level, high (HE, n=2); intermediate (IE, n=2); and low (LE, n=2)) evaluated all examinations and assigned independent MRI BI-RADS ratings. Lesion type (mass, non-mass, focal) was noted. Receiver operating characteristics (ROC) and logistic regression analysis was performed to compare diagnostic accuracies.

RESULTS

There were 259 histologically verified lesions (123 malignant, 136 benign) investigated. There were 169 mass (103 malignant, 66 benign) and 48 non-mass lesions (19 malignant, 29 benign). Another 42 lesions that met the inclusion criteria were biopsied due to conventional findings (i.e., microcalcifications, architectural distortions), but did not enhance on MRI (41 benign, one DCIS). ROC analysis revealed a total area under the curve (AUC) between 0.834 (LE) and 0.935 (HI). Logistic regression identified a significant effect of non-mass lesions (P<0.0001) and reader experience (P=0.005) on diagnostic performance.

CONCLUSIONS

Non-mass lesion type and low reader experience negatively affect the diagnostic performance of breast MRI in a non-screening setting.

Proof-of-the-Concept Study on Mathematically Optimized Magnetic Resonance Spectroscopy for Breast Cancer Diagnostics

Magnetic resonance (MR)-based modalities aid breast cancer detection without exposure to ionizing radiation. Magnetic resonance imaging is very sensitive but costly and insufficiently specific. Molecular imaging through magnetic resonance spectroscopy (MRS) can provide information about key metabolites. Here, the measured/encoded time signals cannot be interpreted directly, necessitating mathematics for mapping to the more manageable frequency domain. Conventional applications of MRS are hampered by data analysis via the fast Fourier transform (FFT) and postprocessing by fitting techniques. Most in vivo MRS studies on breast cancer rely upon estimations of total choline (tCHO). These have yielded only incremental improvements in diagnostic accuracy. In vitro studies reveal richer metabolic information for identifying breast cancer, particularly in closely overlapping components of tCHO. Among these are phosphocholine (PC), a marker of malignant transformation of the breast. The FFT cannot assess these congested spectral components. This can be done by the fast Padé transform (FPT), a high-resolution, quantification-equipped method, which we presently apply to noisy MRS time signals consistent with those encoded in breast cancer. The FPT unequivocally and robustly extracted the concentrations of all physical metabolites, including PC. In sharp contrast, the FFT produced a rough envelope spectrum with a few distorted peaks and key metabolites absent altogether. As such, the FFT has poor resolution for these typical MRS time signals from breast cancer. Hence, based on Fourier-estimated envelope spectra, tCHO estimates are unreliable. Using even truncated time signals, the FPT clearly distinguishes noise from true metabolites whose concentrations are accurately extracted. The high resolution of the FPT translates directly into shortened examination time of the patient. These capabilities strongly suggest that by applying the FPT to time signals encoded in vivo from the breast, MRS will, at last, fulfill its potential to become a clinically reliable, cost-effective method for breast cancer detection, including screening/surveillance.

Dixon imaging-based partial volume correction improves quantification of choline detected by breast 3D-MRSI

OBJECTIVES

Our aim was to develop a partial volume (PV) correction method of choline (Cho) signals detected by breast 3D-magnetic resonance spectroscopic imaging (3D-MRSI), using information from water/fat-Dixon MRI.

METHODS

Following institutional review board approval, five breast cancer patients were measured at 3 T. 3D-MRSI (1 cm(3) resolution, duration ~11 min) and Dixon MRI (1 mm(3), ~2 min) were measured in vivo and in phantoms. Glandular/lesion tissue was segmented from water/fat-Dixon MRI and transformed to match the resolution of 3D-MRSI. The resulting PV values were used to correct Cho signals. Our method was validated on a two-compartment phantom (choline/water and oil). PV values were correlated with the spectroscopic water signal. Cho signal variability, caused by partial-water/fat content, was tested in 3D-MRSI voxels located in/near malignant lesions.

RESULTS

Phantom measurements showed good correlation (r = 0.99) with quantified 3D-MRSI water signals, and better homogeneity after correction. The dependence of the quantified Cho signal on the water/fat voxel composition was significantly (p < 0.05) reduced using Dixon MRI-based PV correction, compared to the original uncorrected data (1.60-fold to 3.12-fold) in patients.

CONCLUSIONS

The proposed method allows quantification of the Cho signal in glandular/lesion tissue independent of water/fat composition in breast 3D-MRSI. This can improve the reproducibility of breast 3D-MRSI, particularly important for therapy monitoring.

Overcoming limitations in diffusion-weighted MRI of breast by spatio-temporal encoding

PURPOSE

Evaluating the usefulness of diffusion-weighted spatio-temporal encoding (SPEN) methods to provide quantitative apparent diffusion coefficient (ADC)-based characterizations of healthy and malignant human breast tissues, in comparison with results obtained using techniques based on spin-echo echo planar imaging (SE-EPI).

METHODS

Twelve healthy volunteers and six breast cancer patients were scanned at 3T using scanner-supplied diffusion-weighted imaging EPI sequences, as well as two fully refocused SPEN variants programmed in-house. Suitable codes were written to process the data, including calculations of the actual b-values and retrieval of the ADC maps.

RESULTS

Systematically better images were afforded by the SPEN scans, with negligible geometrical distortions and markedly weaker ghosting artifacts arising from either fat tissues or from strongly emitting areas such as cysts. SPEN-derived images provided improved characterizations of the fibroglandular tissues and of the lesions' contours. When translated into the calculation of the ADC maps, there were no significant differences between the mean ADCs derived from SPEN and SE-EPI: if reliable images were available, both techniques showed that ADCs decreased by nearly two-fold in the malignant lesion areas.

CONCLUSION

SPEN-based sequences yielded diffusion-weighted breast images with minimal artifacts and distortions, enabling the calculation of improved ADC maps and the identification of decreased ADCs in malignant regions.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Magnetic resonance spectroscopy of the breast: current status

In vivo magnetic resonance spectroscopy (MRS) of the breast can be used to measure the level of choline-containing compounds, which is a biomarker of malignancy. In the diagnostic setting, MRS can provide high specificity for distinguishing benign from malignant lesions. MRS also can be used as an early response indicator in patients undergoing neoadjuvant chemotherapy. This article describes the acquisition and analysis methods used for measuring total choline levels in the breast using MRS, reviews the findings from clinical studies of diagnosis and treatment response, and discusses problems, limitations, and future developments for this promising clinical technology.

Tissue-based approaches to study pharmacodynamic endpoints in early phase oncology clinical trials

Anti-cancer clinical drug development is currently costly and slow with a high attrition rate. There is thus an urgent and unmet need to integrate pharmacodynamic biomarkers into early phase clinical trials in the framework provided by the “pharmacologic audit trail” in order to overcome this challenge. This review discusses the rationale, advantages and disadvantages, as well as the practical considerations of various tissue-based approaches to perform pharmacodynamic studies in early phase oncology clinical trials using case histories of molecular targeting agents such as PI3K, m-TOR, HSP90, HDAC and PARP inhibitors. These approaches include the use of normal “surrogate” tissues such as peripheral blood mononuclear cells, platelet-rich plasma, plucked hair follicles, skin biopsies, plasma-based endocrine assays, proteomics, metabolomics and circulating endothelial cells. In addition, the review discusses the use of neoplastic tissues including tumor biopsies, circulating tumor DNA and tumor cells and metabolomic approaches. The utilization of these tissues and technology platforms to study biomarkers will help accelerate the development of molecularly targeted agents for the treatment of cancer.

New opportunities from the cancer metabolome

BACKGROUND

Metabolomics, the study of all metabolites produced in the body, which often includes flora and drug metabolites, is the omics approach that can be considered most closely related to a patient's phenotype. Metabolomics has a great and largely untapped potential in the field of oncology, and the analysis of the cancer metabolome to identify biofluid markers and novel druggable targets can now be undertaken in many research laboratories.

CONTENT

The cancer metabolome has been used to identify and begin to evaluate potential biomarkers and therapeutic targets in a variety of malignancies, including breast, prostate, and kidney cancer. We discuss the several standard techniques for metabolite separation and identification, with their potential problems and drawbacks. Validation of biomarkers and targets may entail intensive use of labor and technology and generally requires a large number of study participants as well as laboratory validation studies. The field of pharmacometabolomics, in which specific therapies are chosen on the basis of a patient's metabolomic profile, has shown some promise in the translation of metabolomics into the arena of personalized medicine.

SUMMARY

The relatively new approach using metabolomics has just begun to enter the mainstream of cancer diagnostics and therapeutics. As this field advances, metabolomics will take its well-deserved place next to genomics, transcriptomics, and proteomics in both clinical and basic research in oncology.

Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T

PURPOSE

To qualitatively and quantitatively compare the diagnostic value of diffusion-weighted (DW) magnetic resonance (MR) imaging based on standard single-shot echo-planar imaging and readout-segmented echo-planar imaging in patients with breast cancer at 3.0 T.

MATERIALS AND METHODS

Institutional review board approval and written informed consent were obtained. Forty-seven patients with 49 histopathologically verified lesions were included in this study. In all patients, DW imaging, with single-shot echo-planar imaging and readout-segmented echo-planar imaging with comparable imaging parameters, was performed with a 3.0-T MR imager. Two independent readers visually assessed image quality and lesion conspicuity, and image properties (ie, signal-to-noise ratio, contrast, geometric distortions) were quantified. Regions of interest were drawn in all lesions (28 malignant, 21 benign) and in the normal breast parenchyma to investigate differences in apparent diffusion coefficient (ADC). Diagnostic accuracy was calculated on the basis of an ADC threshold of 1.25 × 10(-3) mm(2)/sec.

RESULTS

Each reader found a higher diagnostic accuracy for readout-segmented (96%) than for single-shot (90%) echo-planar imaging. The area under the curve for readout-segmented echo-planar imaging (0.981) was significantly larger than for single-shot echo-planar imaging (0.867) (P = .026). There was no significant difference in the ADC obtained by using either DW imaging method. Lesion conspicuity and image quality of readout-segmented echo-planar imaging were rated superior to those of single-shot echo-planar imaging (P < .001). Readout-segmented echo-planar imaging reduced geometric distortions by a factor of three.

CONCLUSION

DW imaging based on readout-segmented echo-planar imaging provided significantly higher image quality and lesion conspicuity than single-shot echo-planar imaging by reducing geometric distortions, image blurring, and artifact level with a clinical high-field-strength MR imager. Thereby, readout-segmented echo-planar imaging reached a higher diagnostic accuracy for the differentiation of benign and malignant breast lesions.

In vivo proton magnetic resonance spectroscopy of breast cancer: a review of the literature

An emerging clinical modality called proton magnetic resonance spectroscopy ((1)H-MRS) enables the non-invasive in vivo assessment of tissue metabolism and is demonstrating applications in improving the specificity of MR breast lesion diagnosis and monitoring tumour responsiveness to neoadjuvant chemotherapies. Variations in the concentration of choline-based cellular metabolites, detectable with (1)H-MRS, have shown an association with malignant transformation of tissue in in vivo and in vitro studies. (1)H-MRS exists as an adjunct to the current routine clinical breast MR examination. This review serves as an introduction to the field of breast (1)H-MRS, discusses modern high-field strength and quantitative approaches and technical considerations, and reviews the literature with respect to the application of (1)H-MRS for breast cancer.

Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform

BACKGROUND

Currently available tests are insufficient to distinguish patients with epithelial ovarian cancer (EOC) from normal individuals. Metabolomics, a study of metabolic processes in biologic systems, has emerged as a key technology in the measurements of small molecular metabolites in tissues or biofluids.

MATERIAL AND METHODS

To investigate the application of metabolomics on selecting EOC-associated biomarkers, 173 plasma specimens (80 newly diagnosed EOC patients and 93 normal individuals) were analyzed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS). A two-step strategy was performed to select EOC-associated biomarkers. The first step was to select potential biomarkers in distinguishing 42 cancer patients from 58 normal controls through partial least-squares discriminant analysis (PLS-DA) and database searching, and the second step was to validate the discrimination performance of these biomarkers in a dataset contained 38 EOCs and 35 controls.

RESULTS

Eight candidate biomarkers were selected. The combination of these biomarkers resulted in the area of receiver operating characteristic curve (AUC) of 0.941, a sensitivity of 0.921, and a specificity of 0.886 at the best cut-off point for detecting EOC.

DISCUSSION

Our findings suggested that sharp differences in metabolic profiles exist between EOC patients and normal controls. The identified eight metabolites associated with EOC may be served as novel biomarkers for diagnosis.

In vivo 1H MRS for musculoskeletal lesion characterization: which factors affect diagnostic accuracy?

In vivo (1)H MRS is a noninvasive imaging technique for the identification of malignancy. Musculoskeletal lesions vary in their composition, causing field inhomogeneity and magnetic susceptibility effects which may be technical and diagnostic challenges for MRS. This study investigated the factors that affect diagnostic accuracy in the use of MRS for the characterization of musculoskeletal neoplasms. During a 7-year period, 210 consecutive patients with musculoskeletal lesions larger than 1.5 cm in diameter were examined. MRS of a single-voxel point-resolved spectroscopy sequence with TE = 135 ms was undertaken using a 1.5-T scanner. Lesions with a choline signal-to-noise ratio larger than 3.0 were considered to be malignant tumors. The diagnostic accuracy was calculated for all lesions and for subgroups on the basis of lesion type (bone and soft tissue), lesion composition (mixed and solid nonsclerotic), lesion size (≤4, >4-10 and >10 cm), MR scanner (MR scanner 1 and 2) and selected voxel size (≤3, >3-8 and >8 cm(3)). Multivariate logistic regressions were performed to estimate the associations between each factor and diagnostic accuracy. The diagnostic accuracy was 73.3% for all lesions. The accuracy was 54.4% for mixed lesions and 80.4% for solid nonsclerotic lesions (p < 0.001). The diagnostic accuracy was lower for larger lesions [86.8% for lesions of ≤4 cm, 71.6% for lesions of >4-10 cm (p = 0.04) and 63.6% for lesions of >10 cm (p = 0.007)]. There was no difference in diagnostic accuracy for bone versus soft-tissue lesions or as a function of MR scanner or voxel size. By the use of multivariate logistic regression, a solid nonsclerotic lesion was 3.15 times (95% confidence interval, 1.59-6.27) more likely than a mixed lesion to have a diagnosis (p = 0.001). MRS can be used to characterize musculoskeletal lesions, particularly solid nonsclerotic lesions.

Effect of contrast agent on the results of in vivo ¹H MRS of breast tumors - is it clinically significant?

Choline (Cho) signal identification and quantification in (1)H MRS are used in breast cancer diagnosis. However, an influence of the gadolinium-based contrast agent on the Cho amplitude has been reported experimentally. This study aims to identify the impact of gadolinium-based contrast agents on Cho detection and quantification in postcontrast breast MRS. Consecutive patients were recruited prospectively and randomly allocated to two groups. Group A received a neutral (gadolinium diethylenetriaminepentaacetic acid bis-methylamide) and group B an ionic (gadolinium diethylenetriaminepentaacetic acid) contrast agent, each at a dosage of 0.1 mmol/kg. First, the presence of Cho was identified visually. Then, the normalized Cho intensity in malignant lesions was quantified. Multivariate analysis was applied to identify independent influencing factors on Cho. Sixty-three lesions were investigated [A, n = 34; B, n = 29; 43 malignant (one bilaterally malignant), 20 benign]. Cho was identified visually in 14 of 20 malignant tumors in group A and 12 of 22 malignant tumors in group B (p = 0.477). Normalized Cho differed significantly (p = 0.001) between groups A (mean, 26.8 ± 6.0 AU) and B (mean, 18.2 ± 12.5 AU). No linewidth differences were identified (p > 0.05). Multivariate analysis revealed only group membership (A versus B) as an independent predictor of Cho (p = 0.017). The results suggest stronger negative effects of an ionic relative to a neutral gadolinium-based contrast agent on breast tumor MRS in vivo. These results should be considered when conducting and comparing quantitative Cho measurements in the breast.

Appropriate timing of proton MR spectroscopy in breast cancer

Proton magnetic resonance (MR) spectroscopy (MRS) of the mammary gland region has customarily been used in basic research but is now commonly performed in clinical practice as MR techniques have improved. To debate its usefulness in a variety of fields and ultimately grade the timing of its use, a symposium entitled "Clinical Application and the Latest Technology of MRS-Timing of the Addition of MRS" was presented in 2009 at the 37th Annual Meeting of the Japanese Society for Magnetic Resonance in Medicine (JSMRM). MRS timing was classified into 3 grades according to when its addition: is always better, Grade 1; will sometimes be effective, Grade 2; and can provide only supplemental information, Grade 3. We describe the content of the meeting session on "Timing of the Addition of MRS in the Breast Cancer Field," explain the reasons for the timing classifications, and review previous papers.

MRI for breast cancer screening, diagnosis, and treatment

MRI is used widely both for screening women who are at increased risk of breast cancer and for treatment selection. Prospective studies confirm that MRI screening of women with known or suspected genetic mutation results in a higher sensitivity for cancer detection than does mammography. However, survival data are not available. In women with breast cancer, MRI detects cancer not identified with other types of screening. In two randomised trials, this increased sensitivity did not translate into improved selection of surgical treatment or a reduction in the number of operations. Data for longer-term outcomes such as ipsilateral breast tumour recurrence rates and contralateral breast cancer incidence are scarce, but to date do not show clear benefit for MRI. MRI is better than other methods of assessing the response to neoadjuvant chemotherapy, and is helpful in identifying the primary tumour in patients who present with axillary adenopathy.

¹H-magnetic resonance spectroscopy of the breast at 3.0-T: comparison of results obtained before and after administration of gadolinium-based contrast agent

PURPOSE

To assess the effects of gadolinium-based contrast agent (GBCA) on (1) H-magnetic resonance spectroscopy (MRS) of the breast at 3.0-T.

MATERIALS AND METHODS

Patients (n = 98) with breast cancer (98 lesions) underwent MRS (point-resolved spectroscopy sequence [PRESS]; TR/TE, 2000/100 msec; voxel size, 15 × 15 × 15 mm) before the administration of GBCA. In 52 of those patients, MRS was also performed after the administration of GBCA. The voxel-of-interest (VOI) was placed by referring to the noncontrast-enhanced MRI (diffusion-weighted images combined with fat-suppressed T2-weighted images). We reviewed and graded the appropriateness of VOI location compared to the correlating enhancement lesions. Integral values of the choline peak at a frequency of 3.2 ppm on MRS were compared before and after the administration of GBCA.

RESULTS

The VOI was placed correctly in 64 lesions (65%), although the VOI was placed outside the targeted lesion in 34 lesions (35%). The integral value of the choline peak on MRS decreased significantly after the administration of GBCA (P < 0.001).

CONCLUSION

Accumulation of GBCA in breast cancer could affect the choline peak on MRS. MRS of breast cancer at 3.0-T can be recommended to be acquired before contrast-enhanced study; however, some problems remain in VOI placement with reference to the noncontrast-enhanced study.

The added value of quantitative multi-voxel MR spectroscopy in breast magnetic resonance imaging

Objective

To determine whether quantitative multivoxel MRS improves the accuracy of MRI in the assessment of breast lesions.

Methods

Twenty-five consecutive patients with 26 breast lesions ≥1 cm assessed as BI-RADS 3 or 4 with mammography underwent quantitative multivoxel MRS and contrast-enhanced MRI. The choline (Cho) concentration was calculated using the unsuppressed water signal as a concentration reference. ROC analysis established the diagnostic accuracy of MRI and MRS in the assessment of breast lesions.

Results

Respective Cho concentrations in 26 breast lesions re-classified by MRI as BI-RADS 2 (n = 5), 3 (n = 8), 4 (n = 5) and 5 (n = 8) were 1.16 ± 0.43 (mean ± SD), 1.43 ± 0.47, 2.98 ± 2.15 and 4.94 ± 3.10 mM. Two BI-RADS 3 lesions and all BI-RADS 4 and 5 lesions were malignant on histopathology and had Cho concentrations between 1.7 and 11.8 mM (4.03 ± 2.72 SD), which were significantly higher (P = 0.01) than that in the 11 benign lesions (0.4–1.5 mM; 1.19 ± 0.33 SD). Furthermore, Cho concentrations in the benign and malignant breast lesions in BI-RADS 3 category differed (P = 0.01). The accuracy of combined multivoxel MRS/breast MRI BI-RADS re-classification (AUC = 1.00) exceeded that of MRI alone (AUC = 0.96 ± 0.03).

Conclusions

These preliminary data indicate that multivoxel MRS improves the accuracy of MRI when using a Cho concentration cut-off ≤1.5 mM for benign lesions.

Key Points

• Quantitative multivoxel MR spectroscopy can improve the accuracy of contrast-enhanced breast MRI.

• Multivoxel-MRS can differentiate breast lesions by using the highest Cho-concentration.

• Multivoxel-MRS can exclude patients with benign breast lesions from further invasive diagnostic procedures.

The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast

PURPOSE

To compare the pathology and kinetic characteristics of breast lesions with focus-, mass-, and nonmass-like enhancement.

MATERIALS AND METHODS

A total of 852 MRI detected breast lesions in 697 patients were selected for an IRB approved review. Patients underwent dynamic contrast enhanced MRI using one pre- and three to six postcontrast T(1)-weighted images. The "type" of enhancement was classified as mass, nonmass, or focus, and kinetic curves quantified by the initial enhancement percentage (E(1)), time to peak enhancement (T(peak)), and signal enhancement ratio (SER). These kinetic parameters were compared between malignant and benign lesions within each morphologic type.

RESULTS

A total of 552 lesions were classified as mass (396 malignant, 156 benign), 261 as nonmass (212 malignant, 49 benign), and 39 as focus (9 malignant, 30 benign). The most common pathology of malignant/benign lesions by morphology: for mass, invasive ductal carcinoma/fibroadenoma; for nonmass, ductal carcinoma in situ (DCIS)/fibrocystic change(FCC); for focus, DCIS/FCC. Benign mass lesions exhibited significantly lower E(1), longer T(peak), and lower SER compared with malignant mass lesions (P < 0.0001). Benign nonmass lesions exhibited only a lower SER compared with malignant nonmass lesions (P < 0.01).

CONCLUSION

By considering the diverse pathology and kinetic characteristics of different lesion morphologies, diagnostic accuracy may be improved.

Emerging techniques and molecular imaging in breast cancer

The sensitivity of screening mammography is limited in the evaluation of dense breasts, with as few as 45% of cancers visible in extremely dense breasts. Supplementary imaging for improved sensitivity in women with dense breasts is necessary to overcome this limitation. Emerging technologies that advance the applications of digital mammography include digital breast tomosynthesis and dedicated breast cone-beam computed tomography. Molecular imaging goes beyond structural imaging. A functional imaging technique that provides information on the biology, physiology, and metabolic pathways of cancer might help to improve the sensitivity and specificity of breast cancer diagnosis, facilitate early assessment of treatment response, and help individualize therapy options for patients. Advanced magnetic resonance, nuclear medicine, and optical imaging techniques in the realm of molecular imaging will be explored in this article.

Diagnostic usefulness of water-to-fat ratio and choline concentration in malignant and benign breast lesions and normal breast parenchyma: an in vivo (1) H MRS study

PURPOSE

To compare total choline concentrations ([Cho]) and water-to-fat (W/F) ratios of subtypes of malignant lesions, benign lesions, and normal breast parenchyma and determine their usefulness in breast cancer diagnosis. Reference standard was histology.

MATERIALS AND METHODS

In this HIPPA compliant study, proton MRS was performed on 93 patients with suspicious lesions (>1 cm) who underwent MRI-guided interventional procedures, and on 27 prospectively accrued women enrolled for screening MRI. (W/F) and [Cho] values were calculated using MRS data.

RESULTS

Among 88 MRS-evaluable histologically-confirmed lesions, 40 invasive ductal carcinoma (IDC); 10 invasive lobular carcinoma (ILC); 4 ductal carcinoma in situ (DCIS); 3 invasive mammary carcinoma (IMC); 31 benign. No significant difference observed in (W/F) between benign lesions and normal breast tissue. The area under curve (AUC) of receiver operating characteristic (ROC) curves for discriminating the malignant group from the benign group were 0.97, 0.72, and 0.99 using [Cho], (W/F) and their combination as biomarkers, respectively. (W/F) performs significantly (P < 0.0001;AUC = 0.96) better than [Cho] (AUC = 0.52) in differentiating IDC and ILC lesions.

CONCLUSION

Although [Cho] and (W/F) are good biomarkers for differentiating malignancy, [Cho] is a better marker. Combining both can further improve diagnostic accuracy. IDC and ILC lesions have similar [Cho] levels but are discriminated using (W/F) values.

Nonmass lesions in magnetic resonance imaging of the breast: additional T2-weighted images improve diagnostic accuracy

OBJECTIVE

In magnetic resonance imaging (MRI) of the breast, contrast enhancements present as mass or nonmass (NM) lesions. This study aimed to test the usefulness of currently accepted T1-weighted Breast Imaging Reporting and Data System predictors and to determine the incremental value of new T2-weighted predictors for differentiation of benign from malignant NM lesions.

METHODS

Consecutive patients undergoing surgery after MRI (1.5-T contrast-enhanced T1- and T2-weighted images) were investigated. Lesions were rated by 2 observers in consensus. Breast Imaging Reporting and Data System criteria for NM included spatial distribution, internal enhancement, and dynamic enhancement pattern. Additional criteria on T2-weighted images were signal intensity, presence of intraductal fluid, or cysts at the enhancements location. Independent differentiation criteria (benign vs malignant) were identified by logistic regression followed by receiver operating characteristics analysis.

RESULTS

Of 316 patients, 65 demonstrated NM. The NM lesions were split almost equally into malignant (34) and benign (31) histology. Breast Imaging Reporting and Data System predictors did not differentiate benign from malignant lesions, whereas signal intensity and the presence of cysts on contrast-enhanced T2-weighted images did, with a sensitivity of 91.2% and a specificity of 64.5%.

CONCLUSIONS

Differentiation of NM can be improved using additional T2-weighted images.