Stratification of the aggressiveness of prostate cancer using pre-biopsy multiparametric MRI (mpMRI)

Sensitivity of magnetic resonance elastography to extracellular matrix and cell motility in human prostate cancer cell line-derived xenograft models

Prostate cancer (PCa) is a significant health problem in the male population of the Western world. Magnetic resonance elastography (MRE), an emerging medical imaging technique sensitive to mechanical properties of biological tissues, detects PCa based on abnormally high stiffness and viscosity values. Yet, the origin of these changes in tissue properties and how they correlate with histopathological markers and tumor aggressiveness are largely unknown, hindering the use of tumor biomechanical properties for establishing a noninvasive PCa staging system. To infer the contributions of extracellular matrix (ECM) components and cell motility, we investigated fresh tissue specimens from two PCa xenograft mouse models, PC3 and LNCaP, using magnetic resonance elastography (MRE), diffusion-weighted imaging (DWI), quantitative histology, and nuclear shape analysis. Increased tumor stiffness and impaired water diffusion were observed to be associated with collagen and elastin accumulation and decreased cell motility. Overall, LNCaP, while more representative of clinical PCa than PC3, accumulated fewer ECM components, induced less restriction of water diffusion, and exhibited increased cell motility, resulting in overall softer and less viscous properties. Taken together, our results suggest that prostate tumor stiffness increases with ECM accumulation and cell adhesion - characteristics that influence critical biological processes of cancer development. MRE paired with DWI provides a powerful set of imaging markers that can potentially predict prostate tumor development from benign masses to aggressive malignancies in patients. STATEMENT OF SIGNIFICANCE: Xenograft models of human prostate tumor cell lines, allowing correlation of microstructure-sensitive biophysical imaging parameters with quantitative histological methods, can be investigated to identify hallmarks of cancer.

Application of targeted diagnosis of PSMA in the modality shift of prostate cancer diagnosis: a review

Prostate cancer (PCa) is a serious threat to the health of men all over the world. The progression of PCa varies greatly among different individuals. In clinical practice, some patients often progress to advanced PCa. Therefore, accurate imaging for diagnosis and staging of PCa is particularly important for clinical management of patients. Conventional imaging examinations such as MRI and CT cannot accurately diagnose the pathological stages of advanced PCa, especially metastatic lymph node (LN) stages. As a result, developing an accurate molecular targeted diagnosis is crucial for advanced PCa. Prostate specific membrane antigen (PSMA) is of great value in the diagnosis of PCa because of its specific expression in PCa. At present, researchers have developed positron emission tomography (PET) targeting PSMA. A large number of studies have confirmed that it not only has a higher tumor detection rate, but also has a higher diagnostic efficacy in the pathological stage of advanced PCa compared with traditional imaging methods. This review summarizes recent studies on PSMA targeted PET in PCa diagnosis, analyzes its value in PCa diagnosis in detail, and provides new ideas for urological clinicians in PCa diagnosis and clinical management.

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.

Diagnostic Accuracy of Predictive Models in Prostate Cancer: A Systematic Review and Meta-Analysis

Aim

Accurate diagnosis of prostate cancer (PCa) has a fundamental role in clinical and patient care. Recent advances in diagnostic testing and marker lead to standardized interpretation and increased prescription by clinicians to improve the detection of clinically significant PCa and select patients who strictly require targeted biopsies.

Methods

In this study, we present a systematic review of the overall diagnostic accuracy of each testing panel regarding the panel details. In this meta-analysis, using a structured search, Web of Science and PubMed databases were searched up to 23 September 2019 with no restrictions and filters. The study's outcome was the AUC and 95% confidence interval of prediction models. This index was reported as an overall and based on the WHO region and models with/without MRI.

Results

The thirteen final articles included 25,691 people. The overall AUC and 95% CI in thirteen studies were 0.78 and 95% CI: 0.73-0.82. The weighted average AUC in the countries of the Americas region was 0.73 (95% CI: 0.70-0.75), and in European countries, it was 0.80 (95% CI: 0.72-0.88). In four studies with MRI, the average weighted AUC was 0.88 (95% CI: 0.86-0.90), while in other articles where MRI was not a parameter in the diagnostic model, the mean AUC was 0.73 (95% CI: 0.70-0.76).

Conclusions

The present study's findings showed that MRI significantly improved the detection accuracy of prostate cancer and had the highest discrimination to distinguish candidates for biopsy.

Cancer metabolism in a snapshot: MRS(I)

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by ¹³ C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-¹³ C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr ¹³ C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the ¹³ C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. ³¹ P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole ¹ H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

Multiparametric (mp) MRI of prostate cancer

Prostate cancer (PCa) is one of the most prevalent cancers in men. A large number of men are detected with PCa; however, the clinical behavior ranges from low-grade indolent tumors that never develop into a clinically significant disease to aggressive, invasive tumors that may rapidly progress to metastatic disease. The challenges in clinical management of PCa are at levels of screening, diagnosis, treatment, and follow-up after treatment. Magnetic resonance imaging (MRI) methods have shown a potential role in detection, localization, staging, assessment of aggressiveness, targeting biopsies, etc. in PCa patients. Multiparametric MRI (mpMRI) is emerging as a better option compared to the individual imaging methods used in the evaluation of PCa. There are attempts to improve the reproducibility and reliability of mpMRI by using an objective scoring system proposed in the prostate imaging reporting and data system (PIRADS) for standardized reporting. Prebiopsy mpMRI may be used to detect PCa in men with elevated prostate-specific antigen or abnormal digital rectal examination and to enable targeted biopsies. mpMRI can also be used to decide on clinical management of patients, for example active surveillance, and may help in detecting only the pathology that requires detection. It can potentially not only guide patient selection for initial and repeat biopsy but also reduce false-negative biopsies. This review presents a description of the MR methods most commonly applied for investigations of prostate. The anatomical, functional and metabolic parameters obtained from these MR methods are discussed with regard to their physical basis and their contribution to mpMRI investigations of PCa.

New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer?

PURPOSE

Our objective is to determine the accuracy of multiparametric MRI (mpMRI) in predicting pathologic grade of prostate cancer (PCa) after radical prostatectomy (RP) using simple apparent diffusion coefficient metrics and, specifically, whether mpMRI can accurately separate disease into one of two risk categories (low vs. higher grade) or one of three risk categories (low, intermediate, or high grade) corresponding to the new prognostic grade group (PGG) criteria.

METHODS

This retrospective, HIPAA-compliant, IRB-approved study included 140 patients with PCa who underwent 3 T mpMRI with endorectal coil and transrectal ultrasound-guided (TRUS-G) biopsy before RP. MpMRI was used to classify lesions using a two-tier (low-grade/PGG 1 vs. high-grade/PGG 2-5) or a three-tier system (low-grade/PGG 1 vs. intermediate-grade/PGG 2 vs. high-grade/PGG 3-5). Accuracy of mpMRI was compared against RP for each system.

RESULTS

The predictive accuracy of mpMRI using the two-tier system is higher than when using three-tier system (0.77 and 0.45, respectively). There were similar rates of undergrading between mpMRI and TRUS-G biopsy compared to RP (16% & 21%; respectively); rate of overgrading was higher for mpMRI vs. TRUS-G biopsy compared to RP (42% & 17%, respectively). When mpMRI and TRUS-G biopsy are combined, rate of undergrading is 1.4% and overgrading is 11%.

CONCLUSIONS

MpMRI predictive accuracy is higher when using a two-tier vs. a three-tier system, suggesting that advanced metrics may be necessary to delineate intermediate- from high-grade disease. Rates of under- and overgrading decreased when mpMRI and TRUS-G biopsy are combined, suggesting that these techniques may be complementary in predicting tumor grade.

Predictive Models in Differentiating Vertebral Lesions Using Multiparametric MRI

BACKGROUND AND PURPOSE

Conventional MR imaging has high sensitivity but limited specificity in differentiating various vertebral lesions. We aimed to assess the ability of multiparametric MR imaging in differentiating spinal vertebral lesions and to develop statistical models for predicting the probability of malignant vertebral lesions.

MATERIALS AND METHODS

One hundred twenty-six consecutive patients underwent multiparametric MRI (conventional MR imaging, diffusion-weighted MR imaging, and in-phase/opposed-phase imaging) for vertebral lesions. Vertebral lesions were divided into 3 subgroups: infectious, noninfectious benign, and malignant. The cutoffs for apparent diffusion coefficient (expressed as 10^-3 mm²/s) and signal intensity ratio values were calculated, and 3 predictive models were established for differentiating these subgroups.

RESULTS

Of the lesions of the 126 patients, 62 were infectious, 22 were noninfectious benign, and 42 were malignant. The mean ADC was 1.23 ± 0.16 for infectious, 1.41 ± 0.31 for noninfectious benign, and 1.01 ± 0.22 mm²/s for malignant lesions. The mean signal intensity ratio was 0.80 ± 0.13 for infectious, 0.75 ± 0.19 for noninfectious benign, and 0.98 ± 0.11 for the malignant group. The combination of ADC and signal intensity ratio showed strong discriminatory ability to differentiate lesion type. We found an area under the curve of 0.92 for the predictive model in differentiating infectious from malignant lesions and an area under the curve of 0.91 for the predictive model in differentiating noninfectious benign from malignant lesions. On the basis of the mean ADC and signal intensity ratio, we established automated statistical models that would be helpful in differentiating vertebral lesions.

CONCLUSIONS

Our study shows that multiparametric MRI differentiates various vertebral lesions, and we established prediction models for the same.

Characterization and stratification of prostate lesions based on comprehensive multiparametric MRI using detailed whole-mount histopathology as a reference standard

The purpose of this study was to characterize prostate cancer (PCa) based on multiparametric MR (mpMR) measures derived from MRI, diffusion, spectroscopy, and dynamic contrast-enhanced (DCE) MRI, and to validate mpMRI in detecting PCa and predicting PCa aggressiveness by correlating mpMRI findings with whole-mount histopathology. Seventy-eight men with untreated PCa received 3 T mpMR scans prior to radical prostatectomy. Cancerous regions were outlined, graded, and cancer amount estimated on whole-mount histology. Regions of interest were manually drawn on T₂ -weighted images based on histopathology. Logistic regression was used to identify optimal combinations of parameters for the peripheral zone and transition zone to separate: (i) benign from malignant tissues; (ii) Gleason score (GS) ≤3 + 3 disease from ≥GS3 + 4; and (iii) ≤ GS3 + 4 from ≥GS4 + 3 cancers. The performance of the models was assessed using repeated fourfold cross-validation. Additionally, the performance of the logistic regression models created under the assumption that one or more modality has not been acquired was evaluated. Logistic regression models yielded areas under the curve (AUCs) of 1.0 and 0.99 when separating benign from malignant tissues in the peripheral zone and the transition zone, respectively. Within the peripheral zone, combining choline, maximal enhancement slope, apparent diffusion coefficient (ADC), and citrate measures for separating ≤GS3 + 3 from ≥GS3 + 4 PCa yielded AUC = 0.84. Combining creatine, choline, and washout slope yielded AUC = 0.81 for discriminating ≤GS3 + 4 from ≥GS4 + 3 disease. Within the transition zone, combining washout slope, ADC, and creatine yielded AUC = 0.93 for discriminating ≤GS3 + 3 and ≥GS3 + 4 cancers. When separating ≤GS3 + 4 from ≥GS4 + 3 PCa, combining choline and washout slope yielded AUC = 0.92. MpMRI provides excellent separation between benign tissues and PCa, and across PCa tissues of different aggressiveness. The final models prominently feature spectroscopy and DCE-derived metrics, underlining their value within a comprehensive mpMRI examination.

Prebiopsy multiparametric MRI-based risk score for predicting prostate cancer in biopsy-naive men with prostate-specific antigen between 4-10 ng/mL

BACKGROUND

Risk calculators have traditionally utilized serum prostate-specific antigen (PSA) values in addition to clinical variables to predict the likelihood of prostate cancer (PCa).

PURPOSE

To develop a prebiopsy multiparametric MRI (mpMRI)-based risk score (RS) and a statistical equation for predicting the risk of PCa in biopsy-naive men with serum PSA between 4-10 ng/mL that may help reduce unnecessary biopsies.

STUDY TYPE

Prospective cross-sectional study.

SUBJECTS

In all, 137 consecutive men with PSA between 4-10 ng/mL underwent prebiopsy mpMRI (diffusion-weighted [DW]-MRI and MR spectroscopic imaging [MRSI]) during 2009-2015 were recruited for this study.

FIELD STRENGTH/SEQUENCE

1.5T (Avanto, Siemens Health Care, Erlangen, Germany); T₁ -weighted, T₂ -weighted, DW-MRI, and MRSI sequences were used.

ASSESSMENT

All eligible patients underwent mpMRI-directed, cognitive-fusion transrectal ultrasound (TRUS)-guided biopsies.

STATISTICAL TESTS

An equation model and an RS were developed using receiver operating characteristic (ROC) curve analysis and a multivariable logistic regression approach. A 10-fold crossvalidation and simulation analyses were performed to assess diagnostic performance of various combinations of mpMRI parameters.

RESULTS

Of 137 patients, 32 were diagnosed with PCa on biopsy. Multivariable analysis, adjusted with positive pathology, showed apparent diffusion coefficient (ADC), metabolite ratio, and PSA as significant predictors of PCa (P < 0.05). A statistical equation was derived using these predictors. A simple 6-point mpMRI-based RS was derived for calculating the risk of PCa and it showed that it is highly predictive for PCa (odds ratio = 3.74, 95% confidence interval [CI]: 2.24-6.27, area under the curve [AUC] = 0.87). Both models (equation and RS) yielded high predictive performance (AUC ≥0.85) on validation analysis.

DATA CONCLUSION

A statistical equation and a simple 6-point mpMRI-based RS can be used as a point-of-care tool to potentially help limit the number of negative biopsies in men with PSA between 4 and 10 ng/mL.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1227-1236.

Lesion volume predicts prostate cancer risk and aggressiveness: validation of its value alone and matched with prostate imaging reporting and data system score

OBJECTIVE

To demonstrate the association between magnetic resonance imaging (MRI) estimated lesion volume (LV), prostate cancer detection and tumour clinical significance, evaluating this variable alone and matched with Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score.

PATIENTS AND METHODS

We retrospectively analysed 157 consecutive patients, with at least one prior negative systematic prostatic biopsy, who underwent transperineal prostate MRI/ultrasonography fusion-targeted biopsy between January 2014 and February 2016. Suspicious lesions were delineated using a 'region of interest' and the system calculated prostate volume and LV. Patients were divided in groups considering LV (≤0.5, 0.5-1, ≥1 mL) and PI-RADS score (1-5). We considered clinically significant prostate cancer as all cancers with a Gleason score of ≥3 + 4 as suggested by PI-RADS v2. A direct comparison between MRI estimated LV (MRI LV) and histological tumour volume (HTV) was done in 23 patients who underwent radical prostatectomy during the study period. Differences between MRI LV and HTV were assessed using the paired sample t-test. MRI LV and HTV concordance was verified using a Bland-Altman plot. The chi-squared test and logistic and ordinal regression models were used to evaluate difference in frequencies.

RESULTS

The MRI LV and PI-RADS score were associated both with prostate cancer detection (both P < 0.001) and with significant prostate cancer detection (P < 0.001 and P = 0.008, respectively). When the two variables were matched, increasing LV increased the risk within each PI-RADS group. Prostate cancer detection was 1.4-times higher for LVs of 0.5-1 mL and 1.8-times higher for LVs of ≥1 mL; significant prostate cancer detection was 2.6-times for LVs of 0.5-1 mL and 4-times for LVs of ≥1 mL. There was a positive correlation between MRI LV and HTV (r = 0.9876, P < 0.001). Finally, Bland-Altman analysis showed that MRI LV was underestimated by 4.2% compared to HTV. Study limitations include its monocentric and retrospective design and the limited cohort.

CONCLUSIONS

This study demonstrates that PI-RADS score and the MRI LV, independently and in combination, are associated with prostate cancer detection and with tumour clinical significance.

Multiparametric MR can identify high grade prostatic intraepithelial neoplasia (HGPIN) lesions and predict future detection of prostate cancer in men with a negative initial prostate biopsy

PURPOSE

This study aims to determine the pre-biopsy diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) characteristics of patients with high-grade prostatic intraepithelial neoplasia (HGPIN) and perform follow-up studies in these patients to assess the clinical implications.

MATERIALS AND METHODS

One hundred sixteen men with prostate specific antigen between 4 and 10ng/ml underwent pre-biopsy MR examinations. Nine of them had HGPIN lesions without concomitant prostate cancer (PCa) on biopsy. Apparent diffusion coefficient (ADC) and metabolite ratio [Citrate/(Choline+Creatine)] were calculated and these 9 patients were followed to determine the clinical outcomes.

RESULTS

Mean ADC for HGPIN foci was 1.01±0.16×10(-3)mm(2)/s while for the normal peripheral zone it was 1.69±0.25×10(-3)mm(2)/s (p<0.005). Mean metabolite ratio for voxels in the HGPIN region of initial biopsy was 0.24±0.16 while for the normal peripheral zone the value was 2.66±1.57 (p<0.005). Four of 5 patients who were available for follow-up were detected to have prostate cancer on repeat biopsy. No significant change in metabolite ratio and PSA was observed while ADC showed further reduction on follow-up.

CONCLUSION

HGPIN foci have ADC and metabolite ratio values similar to adenocarcinoma prostate, indicating that such patients have a high likelihood of developing cancer. DWI may help identify such men who may be candidates for close follow-up.

A urologist's perspective on prostate cancer imaging: past, present, and future

Prostate cancer is unique in that unlike other solid organ malignancies, only recently has imaging been employed to routinely detect and localize disease. The introduction of transrectal ultrasound was a significant development, transitioning digitally guided prostate biopsies to ultrasound guidance. The arrival of multiparametric MRI has become the next major step, transforming the way Urologist's diagnose, stage, and treat prostate cancer. Recent recommendations against PSA screening have changed the landscape of urologic oncology with the changing needs being reflected in the initiation of additional robust imaging techniques at different time points in prostate cancer care. The current review aims to provide a clinical perspective in the history, current standard of care, and novel imaging modalities in the evaluation of prostate cancer.