Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness

Validation of single reference variable flip angle (SR-VFA) dynamic T1 mapping with T2 * correction using a novel rotating phantom

PURPOSE

To validate single reference variable flip angle (SR-VFA) dynamic T1 mapping with and without T2 * correction against inversion recovery (IR) T1 measurements.

METHODS

A custom cylindrical phantom with three concentric compartments was filled with variably doped agar to produce a smooth spatial gradient of the T1 relaxation rate as a function of angle across each compartment. IR T1 , VFA T1 , and B1 + measurements were made on the phantom before rotation, and multi-echo stack-of-radial dynamic images were acquired during rotation via an MRI-compatible motor. B1 + -corrected SR-VFA and SR-VFA-T2 * T1 maps were computed from the sliding window reconstructed images and compared against rotationally registered IR and VFA T1 maps to determine the percentage error.

RESULTS

Both VFA and SR-VFA-T2 * T1 maps fell within 10% of IR T1 measurements for a low rotational speed, with a mean accuracy of 2.3% ± 2.6% and 2.8% ± 2.6%, respectively. Increasing rotational speed was found to decrease the accuracy due to increasing temporal smoothing over ranges where the T1 change had a nonconstant slope. SR-VFA T1 mapping was found to have similar accuracy as the SR-VFA-T2 * and VFA methods at low TEs (˜<2 ms), whereas accuracy degraded strongly with later TEs. T2 * correction of the SR-VFA T1 maps was found to consistently improve accuracy and precision, especially at later TEs.

CONCLUSION

SR-VFA-T2 * dynamic T1 mapping was found to be accurate against reference IR T1 measurements within 10% in an agar phantom. Further validation is needed in mixed fat-water phantoms and in vivo.

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.

Effects of dynamic contrast enhancement on transition zone prostate cancer in Prostate Imaging Reporting and Data System Version 2.1

BACKGROUND

The aim of the study was to analyse the effects of dynamic contrast enhanced (DCE)-MRI on transitional-zone prostate cancer (tzPCa) and clinically significant transitional-zone prostate cancer (cs-tzPCa) in Prostate Imaging Reporting and Data System (PI-RADS) Version 2.1.

PATIENTS AND METHODS

The diagnostic efficiencies of T2-weighted imaging (T2WI) + diffusion-weighted imaging (DWI), T2WI + dynamic contrast-enhancement (DCE), and T2WI + DWI + DCE in tzPCa and cs-tzPCa were compared using the score of ≥ 4 as the positive threshold and prostate biopsy as the reference standard.

RESULTS

A total of 425 prostate cases were included in the study: 203 cases in the tzPCa group, and 146 in the cs-tzPCa group. The three sequence combinations had the similar areas under the curves in diagnosing tzPCa and cs-tzPCa (all P < 0.05). The sensitivity of T2WI + DCE and T2WI + DWI + DCE (84.7% and 85.7% for tzPCa; 88.4% and 89.7% for cs-tzPCa, respectively) in diagnosing tzPCa and cs-tzPCa was significantly greater than that of T2WI + DWI (79.3% for tzPCa; 82.9% for cs-tzPCa). The specificity of T2WI + DWI (86.5% for tzPCa; 74.9% for cs-tzPCa) were significantly greater than those of T2WI + DCE and T2WI + DWI + DCE (68.0% and 68.5% for tzPCa; 59.1% and 59.5% for cs-tzPCa, respectively) (all P > 0.05). The diagnostic efficacies of T2WI + DCE and T2WI + DWI + DCE had no significant differences (all P < 0.05).

CONCLUSIONS

DCE can improve the sensitivity of diagnosis for tzPCa and cs-tzPCa, and it is useful for small PCa lesion diagnosis.

Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks

OBJECTIVE

Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI.

METHODS

A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate.

RESULTS

The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents.

CONCLUSION

Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population.

KEY POINTS

• Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.

Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate—Can We Make Better Use of It?

We sought to investigate whether quantitative parameters from a dynamic contrast-enhanced study can be used to differentiate cancer from normal tissue and to determine a cut-off value of specific parameters that can predict malignancy more accurately, compared to the obturator internus muscle as a reference tissue. This retrospective study included 56 patients with biopsy proven prostate cancer (PCa) after multiparametric magnetic resonance imaging (mpMRI), with a total of 70 lesions; 39 were located in the peripheral zone, and 31 in the transition zone. The quantitative parameters for all patients were calculated in the detected lesion, morphologically normal prostate tissue and the obturator internus muscle. Increase in the K_trans value was determined in lesion-to-muscle ratio by 3.974368, which is a cut-off value to differentiate between prostate cancer and normal prostate tissue, with specificity of 72.86% and sensitivity of 91.43%. We introduced a model to detect prostate cancer that combines K_trans lesion-to-muscle ratio value and iAUC lesion-to-muscle ratio value, which is of higher accuracy compared to individual variables. Based on this model, we identified the optimal cut-off value with 100% sensitivity and 64.28% specificity. The use of quantitative DCE pharmacokinetic parameters compared to the obturator internus muscle as reference tissue leads to higher diagnostic accuracy for prostate cancer detection.

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.

Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T

PURPOSE

To compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC).

METHODS

104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0-25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including K^trans, K_ep, and V_e were obtained from a region of interest placed on the dominant tumor of each patient.

RESULTS

ADC_mean, ADC_0-25, D, DDC, and V_e were significantly lower and K_ep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC_0-25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC_0-25 had the highest correlation with Gleason grade (ρ = -0.625, P < 0.001).

CONCLUSIONS

DWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.

The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Objective

To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer.

Methods

The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (K^trans), rate constant (k_ep), the volume of the extravascular extracellular space (v_e), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve.

Results

The DCE-MRI parameters K^trans and k_ep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the K^trans, k_ep, and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the K^trans, k_ep, and v_e (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = –0.525; p < 0.05).

Conclusion

The DCE-MRI quantitative parameters K^trans and k_ep, as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.

Prostatitis, the Great Mimicker of Prostate Cancer: Can We Differentiate Them Quantitatively With Multiparametric MRI?

OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. MATERIALS AND METHODS. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (K^trans), reverse volume transfer constant (k_ep), plasma volume fraction (V_p), extravascular extracellular space volume fraction (V_e), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. RESULTS. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (K^trans, k_ep, V_e, and V_p), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that V_e values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, k_ep, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. CONCLUSION. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.

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

PURPOSE

To investigate the role of qualitative and quantitative DCE-MRI parameters in prostate cancer (PCa) stratified by whole-mount histopathology (WMHP) Gleason score (GS) and PI-RADSv2.

METHODS

This retrospective study included 323 PCa tumors in 254 men, who underwent 3T MRI prior to prostatectomy, 7/2009-12/2016. Qualitative DCE curve types included type 1 (progressive), type 2 (plateau) and type 3 (washout). Quantitative DCE-MRI pharmacokinetic (PK) parameters included K^trans (influx volume transfer coefficient), K_ep (efflux reflux rate constant) and iAUC (initial area under the curve). DCE-MRI features of true positive lesions were evaluated for overall, index, transition zone (TZ) and peripheral zone (PZ), based on GS grade (low = 6, high > 6) and PI-RADSv2 score using SPSSv24.

RESULTS

There were 57 (17.6%) low-grade and 266 (82.4%) high-grade PCa lesions. PI-RADSv2 3, 4 and 5 included 106, 120 and 97 lesions, respectively. 251 (77.7%) and 72 (22.3%) lesions were located in PZ and TZ, respectively. High-grade lesions had significantly higher proportion of Type 3 curves compared to low-grade lesions in overall (70.3% vs. 54.4%) and TZ (73.5% vs. 43.5%). As PI-RADSv2 increased, the proportion of type 3 curve significantly increased for overall (80.4-51.9%), index (80.4-54.7%) and PZ (78.7-52.1%) lesions. Among PK parameters, K^trans (0.43 vs 0.32) and iAUC (8.99 vs 6.9) for overall PCa, K^trans (0.43 vs 0.31) and iAUC (9 vs 6.67) for PZ PCa, and iAUC (8.94 vs 7.42) for index PCa were significantly higher for high-grade versus low-grade lesions. Also, K^trans (0.51-0.34), K_ep (1.75-1.29) and iAUC (9.79-7.6) for overall PCa, K^trans (0.53-0.32), K_ep (1.81-1.26) and iAUC (9.83-7.34) for PZ PCa; and K_ep (1.79-1.17) and iAUC (11.3-8.45) for index PCa increased significantly with a higher PI-RADSv2 score.

CONCLUSIONS

The results of study show the possible utility of qualitative and quantitative DCE-MRI parameters for assessment of PCa GS and PI-RADSv2 categorization.

Diagnosis and Grading of Prostate Cancer by Relaxation Maps From Synthetic MRI

BACKGROUND

The interpretation system for prostate MRI is largely based on qualitative image contrast of different tissue types. Therefore, a fast, standardized, and robust quantitative technique is necessary. Synthetic MRI is capable of quantifying multiple relaxation parameters, which might have potential applications in prostate cancer (PCa).

PURPOSE

To investigate the use of quantitative relaxation maps derived from synthetic MRI for the diagnosis and grading of PCa.

STUDY TYPE

Prospective.

SUBJECTS

In all, 94 men with pathologically confirmed PCa or benign pathological changes.

FIELD STRENGTH/SEQUENCE

T₁ -weighted imaging, T₂ -weighted imaging, diffusion-weighted imaging, and synthetic MRI at 3.0T.

ASSESSMENT

Four kinds of tissue types were identified on pathology, including PCa, stromal hyperplasia (SH), glandular hyperplasia (GH), and noncancerous peripheral zone (PZ). PCa foci were grouped as low-grade (LG, Gleason score ≤6) and intermediate/high-grade (HG, Gleason score ≥7). Regions of interest were manually drawn by two radiologists in consensus on parametric maps according to the pathological results.

STATISTICAL TESTS

Independent sample t-test, Mann-Whitney U-test, and receiver operating characteristic curve analysis.

RESULTS

T₁ and T₂ values of PCa were significantly lower than SH (P = 0.015 and 0.002). The differences of T₁ and T₂ values between PCa and noncancerous PZ were also significant (P ≤ 0.006). The area under the curve (AUC) of the apparent diffusion coefficient (ADC) value was significantly higher than T₁ , T₂ , and proton density (PD) values in discriminating PCa from SH and noncancerous PZ (P ≤ 0.025). T₂ , PD, and ADC values demonstrated similar diagnostic performance in discriminating LG from HG PCa (AUC = 0.806 [0.640-0.918], 0.717 [0.542-0.854], and 0.817 [0.652-0.925], respectively; P ≥ 0.535).

DATA CONCLUSION

Relaxation maps derived from synthetic MRI were helpful for discriminating PCa from other benign pathologies. But the overall diagnostic performance was inferior to the ADC values. T₂ , PD, and ADC values performed similarly in discriminating LG from HG PCa lesions.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:552-564.

The Utility of Diffusion-Weighted Imaging and Perfusion Magnetic Resonance Imaging Parameters for Detecting Clinically Significant Prostate Cancer

Introduction

To establish the diagnostic performance of the parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging at 3T in discriminating between non-clinically significant prostate cancers (ncsPCa, Gleason score [GS] < 7) and clinically significant prostate cancers (csPCa, GS ≥ 7) in the peripheral zone.

Materials and Methods

Twenty-six male patients with peripheral zone prostate cancer (PCa) who had undergone 3T multiparametric magnetic resonance imaging (MRI) scan prior to biopsy were included in the study and evaluated retrospectively. The GS was obtained by both standard 12-core transrectal ultrasound guided biopsy and targeted MRI-US fusion biopsy and then confirmed by prostatectomy, if available. For each confirmed tumour focus, DCE-derived quantitative perfusion metrics (Ktrans, Kep, Ve, initial area under the curve [AUC]), the apparent diffusion coefficient (ADC) value, and normalized versions of quantitative metrics were measured and correlated with the GS.

Results

Ktrans had the highest diagnostic accuracy value of 82% among the DCE-MRI parameters (AUC 0.90), and ADC had the strongest diagnostic accuracy value of 87% among the overall parameters (AUC 0.92). The combination of ADC and Ktrans have higher diagnostic performance with the area under the receiver operating characteristic curve being 0.98 (sensitivity 0.94; specificity 0.89; accuracy 0.92) compared to the individual evaluation of each parameter alone. The GS showed strong negative correlations with ADC (r = −0.72) and normalized ADC (r = −0.69) as well as a significant positive correlation with Ktrans (r = 0.69).

Conclusion

The combination of Ktrans and ADC and their normalized versions may help differentiate between ncsPCa from csPCa in the peripheral zone.

Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer

PURPOSE

To compare the diagnostic accuracy of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) and to explore the application value of dynamic contrast-enhanced (DCE) MRI in prostate imaging.

METHODS AND MATERIALS

This study retrospectively enrolled 235 patients with suspected PCa in our hospital from January 2016 to December 2017, and all lesions were histopathologically confirmed. The lesions were scored according to the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2). The bpMRI (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC]) and mpMRI (T2WI, DWI/ADC and DCE) scores were recorded to plot the receiver operating characteristic (ROC) curves. The area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for each method were calculated and compared. The patients were further stratified according to bpMRI scores (bpMRI ≥3, and bpMRI = 3, 4, 5) to analyse the difference in DCE MRI between PCa and non-PCa lesions (as well as between csPCa and non-csPCa).

RESULTS

The AUC values for the bpMRI and mpMRI protocols for PCa were comparable (0.790 [0.732-0.840] and 0.791 [0.733-0.841], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for PCa were 76.2, 79.5, 72.6, 75.8, and 76.6%, respectively, and the values for mpMRI were 77.4, 84.4, 69.9, 75.2, and 80.6%, respectively. The AUC values for the bpMRI and mpMRI protocols for the diagnosis of csPCa were similar (0.781 [0.722-0.832] and 0.779 [0.721-0.831], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for csPCa were 74.0, 83.8, 66.9, 64.8, and 85.0%, respectively; and 73.6, 87.9, 63.2, 63.2, and 87.8%, respectively, for mpMRI. For patients with bpMRI scores ≥3, positive DCE results were more common in PCa and csPCa lesions (both P = 0.001). Further stratification analysis showed that for patients with a bpMRI score = 4, PCa and csPCa lesions were more likely to have positive DCE results (P = 0.003 and P < 0.001, respectively).

CONCLUSION

The diagnostic accuracy of bpMRI is comparable with that of mpMRI in the detection of PCa and the identification of csPCa. DCE MRI is helpful in further identifying PCa and csPCa lesions in patients with bpMRI ≥3, especially bpMRI = 4, which may be conducive to achieving a more accurate PCa risk stratification. Rather than omitting DCE, we think further comprehensive studies are required for prostate MRI.

Can DCE-MRI reduce the number of PI-RADS v.2 false positive findings? Role of quantitative pharmacokinetic parameters in prostate lesions characterization

PURPOSE

To test the potential impact of pharmacokinetic parameters, derived from DCE-MRI analysis, on the diagnostic performance of PI-RADSv.2 classification in prostate lesions characterization.

METHOD

Among patients who underwent multiparametric prostate MRI (mpMRI) (January 2016-March 2018) followed by histological evaluation (targeted biopsies/prostatectomy), 103 men were retrospectively selected. For each patient the index lesion was identified and pharmacokinetic parameters (Ktrans, Kep, Ve, Vp) were assessed. MRI diagnostic performance in the detection of significant tumors [Gleason Score (GS)≥7] was assessed, considering PI-RADS≥3 as positive.

RESULTS

GS ≥ 7 (n = 59) showed higher Ktrans (p < 0.01) and Kep (p = 0.01) compared to GS < 7. At ROC curve analysis, a Ktrans cut-off of 191 × 10^-3/min was identified to predict the presence of GS ≥ 7 (AUC:0.75; sensitivity:95%; specificity:61%). Sensitivity and PPV of mpMRI using PI-RADSv.2 were 98% and 61%. Reclassifying PI-RADS≥3 lesions according to Ktrans cut-off, 22 false positives were shifted to true negatives with 3 false negative findings; PPV raised to 79%. Appling Ktrans cut-off to PI-RADS 3 lesions of peripheral zone (n = 18), 12 true negatives, 4 true positives, 2 false positives were identified.

CONCLUSIONS

Despite its high sensitivity prostate mpMRI generates many false positive cases: Ktrans in addition to PIRADS v.2 seems to improve MRI-PPV and may help in avoiding redundant biopsies.

Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.

Short review of biparametric prostate MRI

Magnetic resonance imaging (MRI) of the prostate has become the gold standard for visualization of prostate cancer. Prostate MRI is usually performed as multiparametric MRI (mpMRI). Since mpMRI has several drawbacks, a biparametric MRI (bpMRI) of the prostate has been proposed. Many studies have been published on mpMRI and bpMRI in recent years. This short review offers an overview of the latest developments in this rapidly evolving field of research.

Quantitative assessment of dynamic 18F-flumethycholine PET and dynamic contrast enhanced MRI in high risk prostate cancer

OBJECTIVE:

To describe dynamic ¹⁸F-flumethycholine PET (dPET) and dynamic contrast enhancement MR (DCE MR) parameters in localized high-risk prostate cancer (PCa), and determine whether these differ from normal prostate. Furthermore, to determine whether a correlation exists between dPET and DCE MR parameters.

METHODS:

41 consenting patients who underwent prostate DCE MR and dPET were included in this institutionally approved study. Intraprostatic lesions on MR were assigned a PI-RADS v2 score, and focal lesions on PET were documented. All lesions were correlated with pathology. Quantitative and semi-quantitative DCE MR and two-tissue compartmental model dPET parameters were determined and tumor-to-normal gland ratios (T/N) for these parameters were calculated. Finally, dPET and DCE MR correlation was estimated using Spearman correlation coefficients.

RESULTS:

There were 46 malignant lesions per standard of reference. On dPET, peripheral zone (PZ) tumors had higher K1 (p < 0.001), and a T/N ratio ≥2 was significant (p < 0.001). On DCE MR, the parameters in, kep, Ktrans and quantitative iAUC were higher for PZ and non-PZ tumors than corresponding normal tissue (p < 0.001); for PZ tumors, a T/N ratio ≥ 1.5 for Ktrans and pei was significant (p = 0.0019 and 0.0026, respectively). Moderate Spearman correlation (0.40 < ρ < 0.59) was found between dPET K1 and DCE MR Ktrans and pei.

CONCLUSION:

In patients with high-risk PCa, quantitative dPET and DCE-MR parameters in primary tumors differ from normal tissue. Only moderate correlation exists between K1 (dPET) and Ktrans and pei (DCE MR). The incremental value of any of these parameters to PI-RADS v2 warrants further investigation.

ADVANCES IN KNOWLEDGE:

Unique quantitative and semi-quantitative FCH PET/MR parameters in PCa differ from normal gland, and should be further investigated to determine their potential contribution to PI-RADS v2 in the detection of clinically significant PCa.

Comparison of multiparametric and biparametric MRI of the prostate: are gadolinium-based contrast agents needed for routine examinations?

PURPOSE

To investigate, if and how omitting gadolinium-based contrast agents (GBCA) and dynamic contrast-enhanced imaging (DCE) influences diagnostic accuracy and tumor detection rates of prostate MRI.

METHODS

In this retrospective study, 236 patients were included. The results of biparametric (bpMRI) and multiparametric magnetic resonance imaging (mpMRI) were compared using the PI-RADS version 2 scoring system. The distribution of lesions to PIRADS score levels, tumor detection rates, diagnostic accuracy and RoC analysis were calculated and compared to the results of histopathological analysis or 5-year follow-up for benign findings.

RESULTS

Omitting DCE changed PI-RADS scores in 9.75% of patients, increasing the number of PI-RADS 3 scores by 8.89% when compared to mpMRI. No change of more than one score level was observed. BpMRI did not show significant differences in diagnostic accuracy or tumor detection rates. (AuC of 0.914 vs 0.917 in ROC analysis). Of 135 prostate carcinomas (PCa), 94.07% were scored identically, and 5.93% were downgraded only from PI-RADS 4 to PI-RADS 3 by bpMRI. All of them were low-grade PCa with Gleason Score 6 or 7a. No changes were observed for PCa ≥ 7b.

CONCLUSION

Omitting DCE did not lead to significant differences in diagnostic accuracy or tumor detection rates when using the PI-RADS 2 scoring system. According to these data, it seems reasonable to use a biparametric approach for initial routine prostate MRI. This could decrease examination time and reduce costs without significantly lowering the diagnostic accuracy.

Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate

PURPOSE

To describe an efficient numerical optimization technique using non-linear least squares to estimate perfusion parameters for the Tofts and extended Tofts models from dynamic contrast enhanced (DCE) MRI data and apply the technique to prostate cancer.

METHODS

Parameters were estimated by fitting the two Tofts-based perfusion models to the acquired data via non-linear least squares. We apply Variable Projection (VP) to convert the fitting problem from a multi-dimensional to a one-dimensional line search to improve computational efficiency and robustness. Using simulation and DCE-MRI studies in twenty patients with suspected prostate cancer, the VP-based solver was compared against the traditional Levenberg-Marquardt (LM) strategy for accuracy, noise amplification, robustness to converge, and computation time.

RESULTS

The simulation demonstrated that VP and LM were both accurate in that the medians closely matched assumed values across typical signal to noise ratio (SNR) levels for both Tofts models. VP and LM showed similar noise sensitivity. Studies using the patient data showed that the VP method reliably converged and matched results from LM with approximate 3× and 2× reductions in computation time for the standard (two-parameter) and extended (three-parameter) Tofts models. While LM failed to converge in 14% of the patient data, VP converged in the ideal 100%.

CONCLUSION

The VP-based method for non-linear least squares estimation of perfusion parameters for prostate MRI is equivalent in accuracy and robustness to noise, while being more reliably (100%) convergent and computationally about 3× (TM) and 2× (ETM) faster than the LM-based method.

The impact of injector-based contrast agent administration in time-resolved MRA

OBJECTIVES

Time-resolved contrast-enhanced MR angiography (4D-MRA), which allows the simultaneous visualization of the vasculature and blood-flow dynamics, is widely used in clinical routine. In this study, the impact of two different contrast agent injection methods on 4D-MRA was examined in a controlled, standardized setting in an animal model.

METHODS

Six anesthetized Goettingen minipigs underwent two identical 4D-MRA examinations at 1.5 T in a single session. The contrast agent (0.1 mmol/kg body weight gadobutrol, followed by 20 ml saline) was injected using either manual injection or an automated injection system. A quantitative comparison of vascular signal enhancement and quantitative renal perfusion analyses were performed.

RESULTS

Analysis of signal enhancement revealed higher peak enhancements and shorter time to peak intervals for the automated injection. Significantly different bolus shapes were found: automated injection resulted in a compact first-pass bolus shape clearly separated from the recirculation while manual injection resulted in a disrupted first-pass bolus with two peaks. In the quantitative perfusion analyses, statistically significant differences in plasma flow values were found between the injection methods.

CONCLUSIONS

The results of both qualitative and quantitative 4D-MRA depend on the contrast agent injection method, with automated injection providing more defined bolus shapes and more standardized examination protocols.

KEY POINTS

• Automated and manual contrast agent injection result in different bolus shapes in 4D-MRA. • Manual injection results in an undefined and interrupted bolus with two peaks. • Automated injection provides more defined bolus shapes. • Automated injection can lead to more standardized examination protocols.