Magnetic resonance spectroscopy imaging in diagnosis of suspicious prostate cancer: A meta-analysis

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

Deuterium MR spectroscopy: potential applications in oncology research

Metabolic imaging in clinical practice has long relied on PET with fluorodeoxyglucose (FDG), a radioactive tracer. However, this conventional method presents inherent limitations such as exposure to ionizing radiation and potential diagnostic uncertainties, particularly in organs with heightened glucose uptake like the brain. This review underscores the transformative potential of traditional deuterium MR spectroscopy (MRS) when integrated with gradient techniques, culminating in an advanced metabolic imaging modality known as deuterium MRI (DMRI). While recent advancements in hyperpolarized MRS hold promise for metabolic analysis, their widespread clinical usage is hindered by cost constraints and the availability of hyperpolarizer devices or facilities. DMRI, also denoted as deuterium metabolic imaging (DMI), represents a pioneering, single-shot, and noninvasive paradigm that fuses conventional MRS with nonradioactive deuterium-labelled substrates. Extensively tested in animal models and patient cohorts, particularly in cases of brain tumours, DMI's standout feature lies in its seamless integration into standard clinical MRI scanners, necessitating only minor adjustments such as radiofrequency coil tuning to the deuterium frequency. DMRI emerges as a versatile tool for quantifying crucial metabolites in clinical oncology, including glucose, lactate, glutamate, glutamine, and characterizing IDH mutations. Its potential applications in this domain are broad, spanning diagnostic profiling, treatment response monitoring, and the identification of novel therapeutic targets across diverse cancer subtypes.

The Risk Factors and Screening Uptake for Prostate Cancer: A Scoping Review

OBJECTIVES

The purpose of this scoping review was to identify the risk factors and screening uptake for prostate cancer.

DESIGN

Scoping review.

METHODS

Arksey and O'Malley's framework guided this review; five databases (Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE, PsycINFO, Academic Search Complete and Cochrane Library) and grey literature were searched. Screening was undertaken against predetermined inclusion criteria for articles published before July 2023 and written in English. This review is reported in line with PRISMA-Sc.

RESULTS

10,899 database results were identified; 3676 papers were removed as duplicates and 7115 papers were excluded at title and abstract review. A total of 108 papers were full-text reviewed and 67 were included in the review. Grey literature searching yielded no results. Age, family history/genetics, hormones, race/ethnicity, exposure to hazards, geographical location and diet were identified as risk factors. Prostatic antigen test (PSA), digital rectal examination (DRE), transrectal ultrasound (TRUS), magnetic resonance imaging (MRI), magnetic resonance spectroscopic imaging (MRSI) and prostate biopsy were identified as screening/diagnostic methods. The evidence reviewed highlights moderate knowledge and screening uptake of prostate cancer with less than half of men reporting for PSA screening. On the other hand, there is a year-to-year increase in PSA and DRE screening, but factors such as poverty, religion, culture, communication barriers, language and costs affect men's uptake of prostate cancer screening.

CONCLUSION

As prostate cancer rates increase globally, there is a need for greater uptake of prostate cancer screening and improved health literacy among men and health workers. There is a need to develop a comprehensive prostate cancer awareness and screening programme that targets men and addresses uptake issues so as to provide safe, quality care.

STRENGTHS AND LIMITATIONS OF THIS STUDY

(1) A broad search strategy was utilised incorporating both databases and grey literature. (2) The PRISMA reporting guidelines were utilised. (3) Only English language papers were included, and this may have resulted in relevant articles being omitted.

Tools to Detect Risk of Death by Suicide: A Systematic Review and Meta-Analysis

Objective: There is limited knowledge about the ability of instruments to detect risk of suicide in a range of settings. Prior reviews have not considered whether the utility of instruments depends on prior probability of risk. We performed a systematic review to determine the diagnostic accuracy of instruments to detect risk of suicide in adults using likelihood ratio analysis. This method aids evaluation of prior probabilities of risk.

Data Sources: We searched MEDLINE, Cochrane Database of Systematic Reviews, PsycINFO, EMBASE, and Scopus from inception through January 19, 2021.

Study Selection: We included clinical trials, observational studies, and quasi-experimental studies assessing the diagnostic accuracy of instruments to detect risk of suicide in adults. There were no language restrictions.

Data Extraction: Three reviewers in duplicate assessed full texts to determine eligibility and extracted data from included studies. Positive (LR+) and negative likelihood ratio (LR-) and 95% CIs were calculated for each instrument.

Results: Thirty studies met inclusion criteria. Most instruments showed minimal utility to detect or rule out risk of suicide, with LR+ ≤ 2.0 and LR- ≥ 0.5. A few instruments had a high utility for improving risk detection in emergency department, inpatient mental health, and prison settings when patients scored above the cutoff (LR+ > 10). For example, among patients discharged from an emergency department, the Columbia Suicide Severity Rating Scale-Clinical Practice Screener had a LR+ of 10.3 (95% CI, 6.3-16.8) at 3-month follow-up. The clinical utility of the instruments depends on the pretest probability of suicide in the setting. Because studies spanned over 6 decades, the findings are at risk for secular trends.

Discussion: We identified several instruments that may hold promise for detecting risk of suicide in emergency department, inpatient mental health, or prison settings. The utility of the instrument hinges, in part, on baseline suicide risk.

Registration: PROSPERO: CRD42021285528.

Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI

Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.

Three-dimensional nuclear magnetic resonance spectroscopy: a complementary tool to multiparametric magnetic resonance imaging in the identification of aggressive prostate cancer at 3.0T

Background

The limitations of the assessment of tumor aggressiveness by Prostate Imaging Reporting and Data System (PI-RADS) and biopsies suggest that the diagnostic algorithm could be improved by quantitative measurements in some chosen indications. We assessed the tumor high-risk predictive performance of 3.0 Tesla (3.0T) multiparametric magnetic resonance imaging (mp-MRI) combined with nuclear magnetic resonance spectroscopic sequences (NMR-S) in order to show that the metabolic analysis could bring out an evocative result for the aggressive form of prostate cancer.

Methods

We conducted a retrospective study of 26 patients (mean age, 62.4 years) who had surgery for prostate cancer between 2009 and 2016 after pre-therapeutic assessment with 3.0T mp-MRI and NMR-S. Groups within the intermediate range of the D'Amico risk classification were divided into two categories, low risk (n=20) and high risk (n=6), according to the International Society of Urological Pathology (ISUP) 2-3 limit. Histoprognostic discordances within various risk groups were compared with the corresponding predictive MRI values. The performance of predictive models was assessed based on sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves.

Results

After prostatectomy, histological analysis reclassified 18 patients as high-risk, including 16 who were T3 MRI grade, of whom 13 (81.3%) were found to be pT3. Among the patients who had cT1 or cT2 digital rectal examinations, the T3 MRI factor multiplied by 8.7 [odds ratio (OR), 8.7; 95% confidence interval (CI), 1.3-56.2; P=0.024] the relative risk of being pT3 and by 5.8 (OR, 5.8; 95% CI, 0.95-35.7; P=0.05) the relative risk of being pGleason (pGS) > GS-prostate biopsy. Spectroscopic data showed that the choline concentration was significantly higher (P=0.001) in aggressive disease.

Conclusions

The predictive model of tumor aggressiveness combining mp-MRI plus NMR-S was better than the mp-MRI model alone (AUC, 0.95 vs. 0.86). Information obtained by mp-MRI coupled with spectroscopy may improve the detection of occult aggressive disease, helping in the discrimination of intermediate risks.

The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism

Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1-13C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised 13C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of 13C-pyruvate to 13C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI. This review will focus on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment. A comparison of hyperpolarised 13C-MRI with state-of-the-art multiparametric MRI is likely to reveal the unique additional information and applications offered by the technique.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Simple Summary

The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted.

Abstract

The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

Role of metabolic imaging in diagnosis of primary, metastatic, and recurrent prostate cancer

PURPOSE OF REVIEW

The present review describes the current role of metabolic imaging techniques such as multiparametric MRI (mpMRI), magnetic resonance spectroscopic imaging (MRSI), hyperpolarized MRSI, and positron emission tomography (PET) in the diagnosis of primary prostate cancer, surveillance of low-grade disease, detection of metastases, and evaluation of biochemical recurrence after therapy.

RECENT FINDINGS

The natural history of prostate cancer ranges from indolent disease that is optimally monitored by active surveillance, to highly aggressive disease that can be lethal. Current diagnostic methods remain imperfect in noninvasively distinguishing between silent versus aggressive tumors. Hence, there is a high demand for noninvasive imaging techniques that offer insight into biological behavior of prostate cancer cells. Characterization of prostate cancer metabolism is a promising area to provide such insights.

SUMMARY

Metabolic imaging may allow for greater detection and ultimately characterization of tumor based on aggressiveness and spread. Hence, it has the potential to monitor tumor activity, predict prognostic outcomes, and guide individualized therapies.

Micro-Ultrasound Imaging for Accuracy of Diagnosis in Clinically Significant Prostate Cancer: A Meta-Analysis

Background: Prostate cancer is a frequently diagnosed malignant solid tumor in men. The accuracy of diagnosis is becoming increasingly important. This meta-analysis evaluated the accuracy of micro-ultrasound in the diagnosis of clinically significant prostate cancer. Methods: We searched PubMed, Embase, Web of Science, and Cochrane Library databases to recruit studies in English. The quality assessment of diagnostic accuracy studies-2 protocol was used to evaluate the literature quality. Publication bias was analyzed using Deeks' funnel plot asymmetry test. We calculated the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and 95% confidence interval (95% CI) for studies of micro-ultrasound imaging for prostate cancer. The results were assessed by the summary receiver-operating characteristic curve (SROC). Ultimately, a univariable meta-regression and subgroup analysis, Fagan plot, and a likelihood matrix were conducted. Results: A total of seven studies containing 769 patients were included in this meta-analysis. Micro-ultrasound had a pooled sensitivity, specificity, DOR, and an area under the SROC of 0.91, 0.49, 10, and 0.82, respectively. Based on these findings, micro-ultrasound has superior ability to diagnose clinically significant prostate cancer. Conclusion: Micro-ultrasound is a more convenient and cost-effective method in real-time imaging during the biopsy procedure in detecting clinically significant prostate cancer. Although micro-ultrasound has shown promising results, more clinical data and comprehensive analysis are still needed.