Dynamic contrast-enhanced (DCE) MR imaging: the role of qualitative and quantitative parameters for evaluating prostate tumors stratified by Gleason score and PI-RADS v2

Quantitative Analysis of Prostate MRI: Correlation between Contrast-Enhanced Magnetic Resonance Fingerprinting and Dynamic Contrast-Enhanced MRI Parameters

This research aimed to assess the relationship between contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) values and dynamic contrast-enhanced (DCE) MRI parameters including (Ktrans, Kep, Ve, and iAUC). To evaluate the correlation between the MRF-derived values (T1 and T2 values, CE T1 and T2 values, T1 and T2 change) and DCE-MRI parameters and the differences in the parameters between prostate cancer and noncancer lesions in 68 patients, two radiologists independently drew regions-of-interest (ROIs) at the focal prostate lesions. Prostate cancer was identified in 75% (51/68) of patients. The CE T2 value was significantly lower in prostate cancer than in noncancer lesions in the peripheral zone and transition zone. Ktrans, Kep, and iAUC were significantly higher in prostate cancer than noncancer lesions in the peripheral zone (p < 0.05), but not in the transition zone. The CE T1 value was significantly correlated with Ktrans, Ve, and iAUC in prostate cancer, and the CE T2 value was correlated to Ve in noncancer. Some CE MRF values are different between prostate cancer and noncancer tissues and correlate with DCE-MRI parameters. Prostate cancer and noncancer tissues may have different characteristics regarding contrast enhancement.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

Development and validation of a nomogram for predicting prostate cancer based on combining contrast-enhanced transrectal ultrasound and biparametric MRI imaging

Objectives

This study was to explore the feasibility of combining contrast-enhanced transrectal ultrasound (CE-TRUS) with biparametric MRI (CEUS-BpMRI) score for diagnosing prostate cancer (PCa).

Methods

A total of 183 patients with suspected PCa who underwent multiparametric MRI (Mp-MRI) and CE-TRUS were included. CEUS-BpMRI score was developed based on the results of Mp-MRI and CE-TRUS. The diagnostic performance was evaluated by the area under the curve (AUC). The diagnostic efficacy of the CEUS-BpMRI score, BpMRI score, and PI-RADS v2.1 score were compared. Total patients were randomly assigned to a training cohort (70%) or validation cohort (30%). A nomogram was constructed based on univariate and multivariate logistic regression. The model was evaluated by AUC and calibration curve.

Results

The diagnostic performance of CEUS-BpMRI score (AUC 0.857) was comparable to that of PI-RADS v2.1 (AUC 0.862) (P = 0.499), and both were superior to Bp-MRI score (AUC 0.831, P < 0.05). In peripheral zone lesions with Bp-MRI score of 3, there was no statistically significant difference between PI-RADS v2.1 score (AUC 0.728) and CEUS-BpMRI score (AUC 0.668) (P = 0.479). Multivariate analysis showed that age, total prostate specific antigen/free prostate specific antigen (F/T), time to peak (TTP), and CEUS-BpMRI score were independent factors. The AUC of the nomogram was 0.909 in the training cohort and 0.914 in the validation cohort.

Conclusions

CEUS-BpMRI score has high diagnostic efficacy for diagnosing PCa. A nomogram model established by combining age, F/T, TTP, and CEUS-BpMRI score can achieve the best predictive accuracy for PCa.

Morphologic perfusion patterns and PI-RADSv2.1 in transition zone prostate cancer

PURPOSE

To evaluate morphologic perfusion patterns in transition zone prostate cancer in multiparametric MRI controlled by in-bore MRI-guided prostate biopsy.

METHODS

Two experienced radiologists evaluated MRI perfusion patterns in consensus from 321 biopsy cores from the transition zone in 141 patients. Transition zone cancer was present in 77 cores in 36 patients. Single early-phase perfusion images were evaluated separately for the presence of a transition zone prostate cancer (consensus tumor early perfusion). The proposed criteria for the perfusion pattern (asymmetry, signal strength, and homogeneity) were rated in consensus for each biopsy position in the presence of the T2w images including the markers of the biopsy trace. We analyzed receiver operating characteristic curves for the PI-RADSv2.1 score and the proposed perfusion pattern.

RESULTS

A logistic regression model with PI-RADSv2.1 and perfusion patterns in early perfusion imaging improved the model fit significantly compared to a model containing only PI-RADSv2.1 (Likelihood Ratio Test, LR = 14.5, p < .001). The AUC was 0.96 for the multiple regression model compared to 0.92 for the PI-RADSv2.1 alone. The evaluation of homogeneity in single early-enhancement images is not inferior compared to the conventional DCE parameter of PI-RADSv2.1 (AUC 0.84 versus 0.83).

CONCLUSION

Morphologic perfusion patterns significantly improve the diagnostic performance of PI-RADSv2.1 in TZ prostate cancer.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Parameters Could Predict International Society of Urological Pathology Risk Groups of Prostate Cancers on Radical Prostatectomy

BACKGROUND

The International Society of Urological Pathology (ISUP) grade and positive surgical margins (PSMs) after radical prostatectomy (RP) may reflect the prognosis of prostate cancer (PCa) patients. This study aimed to investigate whether DCE-MRI parameters (i.e., Ktrans, kep, and IAUC) could predict ISUP grade and PSMs after RP.

METHOD

Forty-five PCa patients underwent preoperative DCE-MRI. The clinical characteristics and DCE-MRI parameters of the 45 patients were compared between the low- and high-risk (i.e., ISUP grades III-V) groups and between patients with or without PSMs after RP. Multivariate logistic regression analysis was used to identify the significant predictors of placement in the high-risk group and PSMs.

RESULTS

The DCE parameter Ktrans-max was significantly higher in the high-risk group than in the low-risk group (p = 0.028) and was also a significant predictor of placement in the high-risk group (odds ratio [OR] = 1.032, 95% confidence interval [CI] = 1.005-1.060, p = 0.021). Patients with PSMs had significantly higher prostate-specific antigen (PSA) titers, positive biopsy core percentages, Ktrans-max, kep-median, and kep-max than others (all p < 0.05). Of these, positive biopsy core percentage (OR = 1.035, 95% CI = 1.003-1.068, p = 0.032) and kep-max (OR = 1.078, 95% CI = 1.012-1.148, p = 0.020) were significant predictors of PSMs.

CONCLUSION

Preoperative DCE-MRI parameters, specifically Ktrans-max and kep-max, could potentially serve as preoperative imaging biomarkers for postoperative PCa prognosis based on their predictability of PCa risk group and PSM on RP, respectively.

Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy

BACKGROUND

Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level.

METHODS

Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone.

RESULTS

All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours.

CONCLUSIONS

Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms

PURPOSE

This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images.

METHODS

A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis.

RESULTS

The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively.

CONCLUSION

This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.

Dynamic contrast-enhanced magnetic resonance imaging for risk stratification in patients with prostate cancer

BACKGROUND

To investigate the usefulness of perfusion parameters derived from dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of patients diagnosed as prostate cancer (PCa) in differentiating clinically significant cancer [CSC, Gleason score (GS) ≥7] from non-CSC (GS 6).

METHODS

A total of 94 patients diagnosed between August 2018 and September 2020 as PCa by radical prostatectomy were included in this retrospective study (mean age: 68.7 years, range, 47-83 years). All of the patients had undergone DCE-MRI on a single 3T-MR scanner. Whole-tumor volume was measured by reviewing a pathologic topographic map as a reference standard. The quantitative DCE perfusion parameters, including volume transfer constant (K^trans), rate constant (k_ep), extracellular extravascular space (EES) volume fraction (v_e), plasma volume fraction (v_p) and area of region of interest (ROI) were calculated under an extended Tofts model. A receiver operating characteristic (ROC) curve analysis by pair-wise comparison was performed to compare the diagnostic performances of the perfusion parameters.

RESULTS

The study population comprised GS 6 (n=17), GS 7 (n=57), GS 8 (n=9) and GS 9 (n=11) cases. Among the perfusion parameters, v_e differed significantly between CSC (0.238±0.095) and non-CSC (0.300±0.126) (P=0.0308). Area under the curve (AUC) was 0.643 (95% CI, 0.538-0.739), and a maximum accuracy of 64%, a sensitivity of 66%, and a specificity of 53% were estimated. Area of ROI also differed significantly between CSC (201.89±163.87 mm²) and non-CSC (84.99±85.82 mm²) (P=0.0054). AUC was 0.807 (95% CI, 0.713-0.881), and maximum accuracy, sensitivity, and specificity were 81%, 82%, and 76%, respectively.

CONCLUSIONS

Size of the tumor and interstitial space volume fraction are significant parameters in differentiating aggressiveness in PCa.

Pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI in PI-RADS category 3 peripheral zone lesions: preliminary study evaluating DCE-MRI as an imaging biomarker for detection of clinically significant prostate cancers

PURPOSE

To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI.

MATERIALS AND METHODS

In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC.

RESULTS

There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88).

CONCLUSION

Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions.

Can dynamic contrast enhanced MRI predict gleason score in prostate cancer? a systematic review and meta analysis

BACKGROUND AND OBJECTIVES

Multiparametric MRI has become a corner stone in diagnosis of prostate cancer (PC). DCE-MRI is used to quantify the influx of contrast media into tissues, which was shown to correlate with histopathology features. The present analysis sought to correlate DCE-MRI parameters with Gleason score (GS) based upon a large patient sample.

MATERIAL AND METHODS

MEDLINE library, Cochrane and SCOPUS databases were screened for the associations between DCE-MRI and GS in PC up to April 2020. The primary endpoint of the systematic review was the correlation between DCE-MRI parameters and GS and mean K^trans and K_ep and V_e values with standard deviation. In total, 13 studies with overall 894 patients were suitable for the analysis and included into the present study.

RESULTS

The highest correlation was identified for K^trans with a pooled correlation coefficient of r = 0.36 (95% CI 0.14-0.59). A large overlap was identified between clinical significant and non-significant PC for all DCE-parameters, for K^trans the pooled mean value of clinically non-significant PC was 0.32 min^-1 [95% CI 0.13-0.51] and for clinically significant PC it was 0.45 min^-1 (95% CI 0.25-0.64).

CONCLUSION

DCE-MRI cannot be used to predict GS in PC, and consequently cannot discriminate clinically significant from non-significant cancers.

Impact of qualitative, semi-quantitative, and quantitative analyses of dynamic contrast-enhanced magnet resonance imaging on prostate cancer detection

Dynamic contrast enhanced imaging (DCE) as an integral part of multiparametric prostate magnet resonance imaging (mpMRI) can be evaluated using qualitative, semi-quantitative, or quantitative assessment methods. Aim of this study is to analyze the clinical benefits of these evaluations of DCE regarding clinically significant prostate cancer (csPCa) detection and grading. 209 DCE data sets of 103 consecutive patients with mpMRI (T2, DWI, and DCE) and subsequent MRI-(in-bore)-biopsy were retrospectively analyzed. Qualitative DCE evaluation according to PI-RADS v2.1, semi-quantitative (curve type; DCE score according to PI-RADS v1), and quantitative Tofts analyses (Ktrans, kep, and ve) as well as PI-RADS v1 and v2.1 overall classification of 209 lesions (92 PCa, 117 benign lesions) were performed. Of each DCE assessment method, cancer detection, discrimination of csPCa, and localization were assessed and compared to histopathology findings. All DCE analyses (p<0.01-0.05), except ve (p = 0.02), showed significantly different results for PCa and benign lesions in the peripheral zone (PZ) with area under the curve (AUC) values of up to 0.92 for PI-RADS v2.1 overall classification. In the transition zone (TZ) only the qualitative DCE evalulation within PI-RADS (v1 and v2.1) could distinguish between PCa and benign lesions (p<0.01; AUC = 0.95). None of the DCE parameters could differentiate csPCa from non-significant (ns) PCa (p ≥ 0.1). Qualitative analysis of DCE within mpMRI according to PI-RADS version 2.1 showed excellent results regarding (cs)PCa detection. Semi-quantitative and quantitative parameters provided no additional improvements. DCE alone wasn't able to discriminate csPCa from nsPCa.

Value of MRI texture analysis for predicting new Gleason grade group

OBJECTIVES

To explore the potential value of multiparametric magnetic resonance imaging (mpMRI) texture analysis (TA) to predict new Gleason Grade Group (GGG).

METHODS

Fifty-eight lesions of fifty patients who underwent mpMRI scanning, including T₂-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters were obtained by the postprocessing software, and each lesion was assigned to its corresponding GGG. TA parameters derived from T2WI and DWI were statistically analyzed in detail.

RESULTS

Energy, inertia, and correlation derived from apparent diffusion coefficient (ADC) maps and T2WI had a statistically significant difference among the five groups. Kurtosis, energy, inertia, correlation on ADC maps and Energy, inertia on T2WI were moderately related to the GGG trend. ADC-energy and T2-energy were significant independent predictors of the GGG trend. ADC-energy, T2WI-energy, and T2WI-correlation had a statistically significant difference between GGG1 and GGG2-5. ADC-energy were significant independent predictors of the GGG1. ADC-energy, T2WI-energy, and T2WI-correlation showed satisfactory diagnostic efficiency of GGG1 (area under the curve (AUC) 84.6, 74.3, and 83.5%, respectively), and ADC-energy showed excellent sensitivity and specificity (88.9 and 95.1%, respectively).

CONCLUSION

TA parameters ADC-energy and T2-energy played an important role in predicting GGG trend. Both ADC-energy and T2-correlation produced a high diagnostic power of GGG1, and ADC-energy was perfect predictors of GGG1.

ADVANCES IN KNOWLEDGE

TA parameters were innovatively used to predict new GGG trend, and the predictive factors of GGG1 were screen out.

Ancillary imaging and clinical features for the characterization of prostate lesions: A proposed approach to reduce false positives

The relatively low specificity and positive predictive value of the Prostate Imaging-Reporting and Data System (PI-RADS) can lead to considerable false-positive results and unnecessary biopsies. The aim of this study was to propose ancillary features (AFs) indicating clinically significant prostate cancer (csPCa) or benign tissues in PI-RADS category ≥3 lesions and determine the usefulness of these AFs in reducing false-positive assessments of suspicious lesions in men at csPCa risk. This was a retrospective study, which included 199 men. A 3T, including turbo spin echo T₂ -weighted, echo-planar diffusion-weighted, and spoiled gradient echo dynamic contrast-enhanced (DCE) images, was used. Five AFs (prostate-specific antigen density ≥0.15 ng/mL² ; size ≥10 mm; heterogeneous T2 signal intensity; circumscribed nodule in the junction of peripheral and transition zone; and DCE time curves) indicating csPCa or non-csPCa were evaluated by three independent readers. The sensitivity and specificity of each AF were calculated. Inter-reader agreement was evaluated using κ statistics. Univariate and multivariate logistic regression analyses were conducted to determine significant AFs. The reduction in positive call rates and csPCa detection rates with combined AF use were calculated and compared with the findings obtained with PI-RADS use alone. The sensitivities and specificities of the AFs indicating csPCa were 72.1%-96.5% and 27.4%-75.2% for reader 1, 66.3%-96.5% and 23.9%-62.0% for reader 2, and 67.4%-96.5% and 34.5%-78.8% for reader 3, with moderate to substantial inter-reader agreement (Fleiss κ, 0.551-0.643). The combined use of two or more AFs for assessing PI-RADS ≥3 lesions resulted in a 19.6%-30.7% reduction in positive calls (p < .05) compared to PI-RADS use alone while preserving the csPCa detection rates (p ≥ .06) for three readers. The use of AFs in combination with PI-RADS can reduce positive calls and false positives without csPCa under-detection.

Clinical value of high-resolution dynamic contrast-enhanced (DCE) MRI in diagnosis of cutaneous squamous cell carcinoma

BACKGROUND

While uncomplicated cases of skin squamous cell carcinoma (cSCC) can be treated with surgery topical therapy alone, more objective and non-invasive examination methods are needed to guide clinicians to make more detailed biopsy and surgical plans for lesions with atypical or subcutaneous growth. High-resolution magnetic resonance imaging (HR-MRI) is a novel skin imaging method.

MATERIALS AND METHODS

Prospective collection of 19 patients with clinically suspected cSCC. All patients underwent high-resolution DCE-MRI using a 70-mm microscopy coil before operation. The imaging features and results of surgical pathology were recorded. K^trans , K_ep , V_e values, and the time-signal curve (TIC) types were determined using DCE images.

RESULTS

16 cases of cSCC, 3 cases of acanthoma. The subcutaneous invasion of all lesions was clearly displayed, of which 8 lesions invaded the subcutaneous fat layer, 5 invaded the muscle layer, 1 invaded the periosteum, 2 invaded the cap fascia, and the layer of all lesions invasion judged by HR-MR imaging was consistent with the postoperative pathology. The main manifestations of cSCC were ill-defined margin, obvious inhomogeneous enhancement, higher perfusion parameters value and type-III TIC, while acanthoma showed well-defined and type-I TIC. Some imaging findings (such as boundary, enhancement) and DCE perfusion parameters of the two groups overlap.

CONCLUSION

High-resolution DCE-MRI can fully and directly display the subcutaneous invasion of cSCC, and more work needs to be done to prove its value. Next, we will expand the sample size, and further explore its value in the differential diagnosis and prognosis evaluation of cSCC from acanthoma or other skin tumors.