Dynamic contrast-enhanced magnetic resonance imaging and pharmacokinetic models in prostate cancer

Multiparametric MRI before and after Focal Therapy for Prostate Cancer: Pearls and Pitfalls for the Reporting Radiologist

In this era of personalized precision medicine, the accuracy of multiparametric MRI (mpMRI) and targeted biopsy in helping detect low-volume clinically significant prostate cancer has rekindled interest in focal therapy for primary prostate cancer. Such therapy may reduce the debilitating morbidity of radical whole-gland treatment. Post-focal therapy mpMRI surveillance is critical for assessing oncologic efficacy. Radiologists interpreting post-focal therapy mpMRI must be familiar with expected posttreatment changes and pitfalls in assessing posttreatment recurrence. In this review, the authors present their experience with mpMRI before and after focal therapy. While cryotherapy and irreversible electroporation are the primary modalities of focal therapy offered in their institution, the authors aim to provide a comprehensive overview of the more common focal therapy modalities in use. Pertinent considerations of mpMRI in pretreatment patient selection and treatment planning are discussed. The recently proposed standardized post-focal therapy assessment systems, Prostate Imaging after Focal Ablation (ie, PI-FAB) and Transatlantic Recommendations for Prostate Gland Evaluation with MRI after Focal Therapy (ie, TARGET), as well as pearls and pitfalls in the detection of tumor recurrence and medium- and long-term mpMRI surveillance of the post-focal therapy prostate, are also discussed. This review aims to provide a valuable reference for radiologists involved in the care of patients in the evolving field of prostate cancer focal therapy. Keywords: MR Imaging, Urinary, Prostate, Neoplasms-Primary, Focal Therapy, Prostate Cancer, MRI, Surveillance, Tumor Recurrence Published under a CC BY 4.0 license.

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer

Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Materials and Methods Male patients with suspected prostate cancer who underwent multiparametric MRI were retrospectively included from three centers from April 2020 to April 2023. A deep learning model (pix2pix algorithm) was trained to synthesize contrast-enhanced MRI scans from four noncontrast MRI sequences (T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps) and then tested on an internal and two external datasets. The reference standard for model training was the second postcontrast phase of the dynamic contrast-enhanced sequence. Similarity between simulated and acquired contrast-enhanced images was evaluated using the multiscale structural similarity index. Three radiologists independently scored T2-weighted and diffusion-weighted MRI with either simulated or acquired contrast-enhanced images using PI-RADS, version 2.1; agreement was assessed with Cohen κ. Results A total of 567 male patients (mean age, 66 years ± 11 [SD]) were divided into a training test set (n = 244), internal test set (n = 104), external test set 1 (n = 143), and external test set 2 (n = 76). Simulated and acquired contrast-enhanced images demonstrated high similarity (multiscale structural similarity index: 0.82, 0.71, and 0.69 for internal test set, external test set 1, and external test set 2, respectively) with excellent reader agreement of PI-RADS scores (Cohen κ, 0.96; 95% CI: 0.94, 0.98). When simulated contrast-enhanced imaging was added to biparametric MRI, 34 of 323 (10.5%) patients were upgraded to PI-RADS 4 from PI-RADS 3. Conclusion It was feasible to generate simulated contrast-enhanced prostate MRI using deep learning. The simulated and acquired contrast-enhanced MRI scans exhibited high similarity and demonstrated excellent agreement in assessing clinically significant prostate cancer based on PI-RADS, version 2.1. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Neji and Goh in this issue.

Benefit of dynamic contrast-enhanced (DCE) imaging for prostate cancer detection depending on readers experience in prostate MRI

AIM

To investigate the relevance of dynamic contrast enhanced imaging (DCE) within multiparametric magnetic resonance imaging (mpMRI) for the detection of clinically significant prostate cancer (csPC) depending on reader experience.

MATERIALS AND METHODS

Consecutive patients with 3 T mpMRI and subsequent combined MRI/ultrasound fusion-guided targeted and systematic biopsy from January to September 2019 were included. All mpMRI examinations were read separately by two less experienced (R1; <500 prostate MRI) and two expert radiologists (R2; >5,000 prostate MRI) in consensus and blinded re-read as biparametric MRI (bpMRI). The primary endpoint was the performance comparison of mpMRI versus bpMRI of R1 and R2.

RESULTS

Fifty-three of 124 patients had csPC (43%). The PI-RADS agreement of bpMRI and mpMRI was fair for R1 (κ = 0.373) and moderate for R2 (κ = 0.508). R1 assessed 11 csPC with PI-RADS ≤3 (20.8%) on mpMRI and 12 (22.6%) on bpMRI (R2: 1 [1.9%] and 6 [11.3%], respectively). Sensitivity for csPC of mpMRI was 79.3% (NPV 79.3%) for R1 and 98.1% (NPV 97.5%) for R2 (bpMRI: 77.4% [NVP 75.5%] and 86.8% [NPV 84.4%], respectively). Specificity of mpMRI for csPC was 59.2% for R1 and 54.9% for R2 (bpMRI: 52.1% and 53.5%, respectively). Overall accuracy of mpMRI was 79.8% for R1 compared to bpMRI 66.9% (p=0.017; R2: 87.1% and 81.5%; p=0.230).

CONCLUSION

Prostate MRI benefits from reader experience. Less experienced readers missed a relevant proportion of csPC with mpMRI and even more with bpMRI. The overall performance of expert readers was comparable for mpMRI and bpMRI but DCE enabled detection of some further ISUP 2 PC.

Magnetic Resonance Imaging Biomarkers of Bone and Soft Tissue Tumors

Magnetic resonance imaging (MRI) is essential in the management of musculoskeletal (MSK) tumors. This review delves into the diverse MRI modalities, focusing on anatomical, functional, and metabolic sequences that provide essential biomarkers for tumor detection, characterization, disease extent determination, and assessment of treatment response. MRI's multimodal capabilities offer a range of biomarkers that enhance MSK tumor evaluation, aiding in better patient management.

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.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

A narrative review of MRI acquisition for MR-guided-radiotherapy in prostate cancer

Magnetic resonance guided radiotherapy (MRgRT), enabled by the clinical introduction of the integrated MRI and linear accelerator (MR-LINAC), is a novel technique for prostate cancer (PCa) treatment, promising to further improve clinical outcome and reduce toxicity. The role of prostate MRI has been greatly expanded from the traditional PCa diagnosis to also PCa screening, treatment and surveillance. Diagnostic prostate MRI has been relatively familiar in the community, particularly with the development of Prostate Imaging - Reporting and Data System (PI-RADS). But, on the other hand, the use of MRI in the emerging clinical practice of PCa MRgRT, which is substantially different from that in PCa diagnosis, has been so far sparsely presented in the medical literature. This review attempts to give a comprehensive overview of MRI acquisition techniques currently used in the clinical workflows of PCa MRgRT, from treatment planning to online treatment guidance, in order to promote MRI practice and research for PCa MRgRT. In particular, the major differences in the MRI acquisition of PCa MRgRT from that of diagnostic prostate MRI are demonstrated and explained. Limitations in the current MRI acquisition for PCa MRgRT are analyzed. The future developments of MRI in the PCa MRgRT are also discussed.

Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution

METHODS

A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation.

RESULTS

There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%).

CONCLUSIONS

Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.

Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis

BACKGROUND

Accurate preoperative diagnosis of malignant ovarian tumors (MOTs) is particularly important for selecting the optimal treatment strategy and avoiding overtreatment.

PURPOSE

To evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for MOTs.

MATERIAL AND METHODS

A systematic search was performed in PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles up to October 2019. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Studies on the diagnosis of MOTs with quantitative or semi-quantitative DCE-MRI were analyzed separately. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity.

RESULTS

For semi-quantitative DCE-MRI, the pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curves (AUC) were 85% (95% confidence interval [CI] 0.75-0.92), 85% (95% CI 0.77-0.91), 5.8 (95% CI 3.8-8.8), 0.17 (95% CI 0.10-0.30), 33 (95% CI 18-61), and 0.92 (95% CI 0.89-0.94), respectively. For quantitative DCE-MRI, the pooled sensitivity, specificity, positive LR, negative LR, DOR, and AUC were 88% (95% CI 0.65-0.96), 93% (95% CI 0.78-0.98), 12.3 (95% CI 3.4-43.9), 0.13 (95% CI 0.04-0.45), 91 (95% CI 10-857), and 0.96 (95% CI 0.94-0.98), respectively.

CONCLUSION

DCE-MRI has great diagnostic value for MOTs. Semi-quantitative DCE-MRI may be a relatively mature approach; however, quantitative DCE-MRI appears to be more promising than semi-quantitative DCE-MRI.

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

PI-RADS Committee Position on MRI Without Contrast Medium in Biopsy-Naive Men With Suspected Prostate Cancer: Narrative Review

The steadily increasing demand for diagnostic prostate MRI has led to concerns regarding the lack of access to and the availability of qualified MRI scanners and sufficiently experienced radiologists, radiographers, and technologists to meet the demand. Solutions must enhance operational benefits without compromising diagnostic performance, quality, and delivery of service. Solutions should also mitigate risks such as decreased reader confidence and referrer engagement. One approach may be the implementation of MRI without the use gadolinium-based contrast medium (bipara-metric MRI), but only if certain prerequisites such as high-quality imaging, expert interpretation quality, and availability of patient recall or on-table monitoring are mandated. Alternatively, or in combination, a clinical risk-based approach could be used for protocol selection, specifically, which biopsy-naive men need MRI with contrast medium (multiparametric MRI). There is a need for prospective studies in which biopsy decisions are made according to MRI without contrast enhancement. Such studies must define clinical and operational benefits and identify which patient groups can be scanned successfully without contrast enhancement. These higher-quality data are needed before the Prostate Imaging Reporting and Data System (PI-RADS) Committee can make evidence-based recommendations about MRI without contrast enhancement as an initial diagnostic approach for prostate cancer workup.

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.

Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa.

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.

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.

The Evolution of MRI of the Prostate: The Past, the Present, and the Future

OBJECTIVE. The purpose of this article is to discuss the evolution of MRI in prostate cancer from the early 1980s to the current day, providing analysis of the key studies on this topic. CONCLUSION. The rapid diffusion of MRI technology has meant that residual variability remains between centers regarding the quality of acquisition and the quality and standardization of reporting.

Dynamic contrast-enhanced MR imaging of the prostate: intraindividual comparison of gadoterate meglumine and gadobutrol

OBJECTIVES

To intraindividually compare the signal-enhancing effect of 0.5 M gadoterate meglumine and 1.0 M gadobutrol in dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging of the prostate.

METHODS

Fifty patients who underwent two 3-T MR examinations of the prostate were included in this IRB-approved retrospective uncontrolled, unrandomized study. All received two scans (mean time interval, 20.5 months) including T1-weighted DCE-MR imaging, one with 0.5 M gadoterate meglumine and one with 1.0 M gadobutrol. Equimolar doses of gadolinium (0.1 mmol/kg body weight) were administered with identical injection speed (2 mL/s), resulting in differing gadolinium delivery rate. An identical region of interest (ROI_tz) within a BPH-node was identified on both scans. The area under the time-enhancement curve of each ROI_tz from 0 to 180 s post contrast arrival and pharmacokinetic parameters were calculated. Relative enhancement and signal-to-noise (SNR) and contrast-to-noise (CNR) ratios in the delayed phase at about 180 s were compared between both agents.

RESULTS

There was a significantly larger area under the time-enhancement curve (5.53 vs 4.97 p = 0.0007) and higher relative enhancement of BPH nodules (2.23 vs 1.96 p < 0.0001) with gadobutrol compared with gadoterate meglumine. There were no significant differences in SNR (44.55 vs 37.63 p = 0.12), CNR (31.22 vs 26.39 p = 0.18), and pharmacokinetic parameters Ktrans (0.31 vs 0.32 p = 0.86), Ve (1.36 vs 0.98 p = 0.13), and Kep (0.34 vs 0.36 p = 0.12).

CONCLUSIONS

At equimolar doses, increased gadolinium delivery over time using gadobutrol provides higher relative enhancement parameters in BPH nodules compared with gadoterate meglumine, but does not translate into improved SNR or CNR.

KEY POINTS

• At equal injection rate and equimolar total dose, gadobutrol compared with gadoterate meglumine provides a significantly greater relative enhancement in DCE-MR imaging of BPH over the first 180 s. • There are no significant differences in SNRs, CNRs, and pharmacokinetic parameters between the two GBCAs.

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.

Multiparametric MRI to improve detection of prostate cancer compared with transrectal ultrasound-guided prostate biopsy alone: the PROMIS study

BACKGROUND

Men with suspected prostate cancer usually undergo transrectal ultrasound (TRUS)-guided prostate biopsy. TRUS-guided biopsy can cause side effects and has relatively poor diagnostic accuracy. Multiparametric magnetic resonance imaging (mpMRI) used as a triage test might allow men to avoid unnecessary TRUS-guided biopsy and improve diagnostic accuracy.

OBJECTIVES

To (1) assess the ability of mpMRI to identify men who can safely avoid unnecessary biopsy, (2) assess the ability of the mpMRI-based pathway to improve the rate of detection of clinically significant (CS) cancer compared with TRUS-guided biopsy and (3) estimate the cost-effectiveness of a mpMRI-based diagnostic pathway.

DESIGN

A validating paired-cohort study and an economic evaluation using a decision-analytic model.

SETTING

Eleven NHS hospitals in England.

PARTICIPANTS

Men at risk of prostate cancer undergoing a first prostate biopsy.

INTERVENTIONS

Participants underwent three tests: (1) mpMRI (the index test), (2) TRUS-guided biopsy (the current standard) and (3) template prostate mapping (TPM) biopsy (the reference test).

MAIN OUTCOME MEASURES

Diagnostic accuracy of mpMRI, TRUS-guided biopsy and TPM-biopsy measured by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using primary and secondary definitions of CS cancer. The percentage of negative magnetic resonance imaging (MRI) scans was used to identify men who might be able to avoid biopsy.

RESULTS

Diagnostic study - a total of 740 men were registered and 576 underwent all three tests. According to TPM-biopsy, the prevalence of any cancer was 71% [95% confidence interval (CI) 67% to 75%]. The prevalence of CS cancer according to the primary definition (a Gleason score of ≥ 4 + 3 and/or cancer core length of ≥ 6 mm) was 40% (95% CI 36% to 44%). For CS cancer, TRUS-guided biopsy showed a sensitivity of 48% (95% CI 42% to 55%), specificity of 96% (95% CI 94% to 98%), PPV of 90% (95% CI 83% to 94%) and NPV of 74% (95% CI 69% to 78%). The sensitivity of mpMRI was 93% (95% CI 88% to 96%), specificity was 41% (95% CI 36% to 46%), PPV was 51% (95% CI 46% to 56%) and NPV was 89% (95% CI 83% to 94%). A negative mpMRI scan was recorded for 158 men (27%). Of these, 17 were found to have CS cancer on TPM-biopsy. Economic evaluation - the most cost-effective strategy involved testing all men with mpMRI, followed by MRI-guided TRUS-guided biopsy in those patients with suspected CS cancer, followed by rebiopsy if CS cancer was not detected. This strategy is cost-effective at the TRUS-guided biopsy definition 2 (any Gleason pattern of ≥ 4 and/or cancer core length of ≥ 4 mm), mpMRI definition 2 (lesion volume of ≥ 0.2 ml and/or Gleason score of ≥ 3 + 4) and cut-off point 2 (likely to be benign) and detects 95% (95% CI 92% to 98%) of CS cancers. The main drivers of cost-effectiveness were the unit costs of tests, the improvement in sensitivity of MRI-guided TRUS-guided biopsy compared with blind TRUS-guided biopsy and the longer-term costs and outcomes of men with cancer.

LIMITATIONS

The PROstate Magnetic resonance Imaging Study (PROMIS) was carried out in a selected group and excluded men with a prostate volume of > 100 ml, who are less likely to have cancer. The limitations in the economic modelling arise from the limited evidence on the long-term outcomes of men with prostate cancer and on the sensitivity of MRI-targeted repeat biopsy.

CONCLUSIONS

Incorporating mpMRI into the diagnostic pathway as an initial test prior to prostate biopsy may (1) reduce the proportion of men having unnecessary biopsies, (2) improve the detection of CS prostate cancer and (3) increase the cost-effectiveness of the prostate cancer diagnostic and therapeutic pathway. The PROMIS data set will be used for future research; this is likely to include modelling prognostic factors for CS cancer, optimising MRI scan sequencing and biomarker or translational research analyses using the blood and urine samples collected. Better-quality evidence on long-term outcomes in prostate cancer under the various management strategies is required to better assess cost-effectiveness. The value-of-information analysis should be developed further to assess new research to commission.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN16082556 and NCT01292291.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 39. See the NIHR Journals Library website for further project information. This project was also supported and partially funded by the NIHR Biomedical Research Centre at University College London (UCL) Hospitals NHS Foundation Trust and UCL and by The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research Biomedical Research Centre and was co-ordinated by the Medical Research Council's Clinical Trials Unit at UCL (grant code MC_UU_12023/28). It was sponsored by UCL. Funding for the additional collection of blood and urine samples for translational research was provided by Prostate Cancer UK.

Hypoenhancing prostate cancers on dynamic contrast-enhanced MRI are associated with poor outcomes in high-risk patients: results of a hypothesis generating study

PURPOSE

To investigate the association of hypoenhancement on dynamic Contrast enhanced (DCE) with prostate cancer patient outcomes.

MATERIAL AND METHODS

This was a single-institution retrospective Institutional Review Board (IRB)-approved cohort study of 54 men who had prostate Magnetic Resonance Imaging (MRI) within 6 months of cancer diagnosis between 01/2012 to 03/2014. Two readers independently identified the dominant MRI-lesions utilizing Prostate Imaging-Reporting and Data System-version2- guidelines. These lesions were classified as hypoenhancing or hyperenhancing, compared to normal peripheral zone using quantitative DCE analysis. The t test for unequal sample sizes and the two-sample Wilcoxon rank-sum tests were used to compare groups. Logistic regression determined if DCE characteristics predict the development of metastases or prostate cancer death.

RESULTS

Time-to-progression was significantly shorter for hypoenhancing tumors (6.2 vs. 24.8 months, p = 0.05). Men with these lesions had a higher odds of having poor outcome (univariate logistic regression, odds ratio (OR) 6.79, 95% confidence interval (CI) 1.45-31.72, p = 0.02; multivariate analysis, OR 2.05, 95% CI 0.30-13.72, p = 0.47). Hypoenhancing tumors were larger (33.1 vs. 19.1 mm, p < 0.001) and more likely to be intermediate (Gleason scores 3 + 4 and 4 + 3) and high-grade (Gleason scores ≥ 4 + 4) prostate cancers (p = 0.05). Men in the hypoenhancing group had a higher mean prostate-specific antigen (PSA) value (87.6 vs. 24.8 ng/dL, p = 0.01) and PSA density (1.54 vs. 0.72, p = 0.03). The mean K_trans and k_ep of hypoenhancing lesion were lower when compared to hyperenhancing lesions (p = 0.03 and p = 0.04). V_e values did not differ (p = 0.25).

CONCLUSION

Men with hypoenhancing prostate cancers may have a worse prognosis than men with hyperenhancing tumors.

Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies

Multiparametric magnetic resonance imaging (mpMRI) has become the standard of care to achieve accurate and reproducible diagnosis of prostate cancer. However, mpMRI is quite demanding in terms of technical rigour, patient's tolerability and safety, expertise in interpretation, and costs. This paper reviews the main technical strategies proposed as less-is-better solutions for clinical practice (non-contrast biparametric MRI, reduction of acquisition time, abbreviated protocols, computer-aided diagnosis systems), discussing them in the light of the available evidence and of the concurrent evolution of Prostate Imaging Reporting and Data System (PI-RADS). We also summarised research results on those advanced techniques representing an alternative different-is-better line of the still ongoing evolution of prostate MRI (quantitative diffusion-weighted imaging, quantitative dynamic contrast enhancement, intravoxel incoherent motion, diffusion tensor imaging, diffusional kurtosis imaging, restriction spectrum imaging, radiomics analysis, hybrid positron emission tomography/MRI).

Role of perfusion parameters on DCE-MRI and ADC values on DWMRI for invasive ductal carcinoma at 3.0 Tesla

BACKGROUND

The value of apparent diffusion coefficient (ADC) values and quantitative parameters (Ktrans, Kep, Ve) in detecting prognostic factor at 3.0 Tesla remains unclear, especially in predicting prognosis of breast cancer.

METHODS

A total of 151 patients with IDC underwent breast DCE-MRI and DWI-MRI at 3.0 Tesla following surgery. The ADC values were acquired with b values of 0 and 1000 s/mm². The relationship between ADC values or DCE-MRI quantitative parameters and size, histologic grade (HG), lymph node metastasis (LNM), ER, PR, and Ki67 was evaluated. The predictive values of ADC, Ktrans, Kep, and Ve to prognosis of IDC were assessed.

RESULTS

ADC value was positively related to size (P = 0.04) and HER2 (P = 0.046) expression and negatively related to ER (P = 0.012) and PR (P < 0.001) expression. Ktrans value has positive correlation with size (P < 0.001), HG (P < 0.001), LNM (P < 0.001), HER2 (P = 0.007), and Ki67 (P < 0.001) expression and negative correlation with ER (P < 0.001) and PR (P < 0.001) expression. Kep value was positively related to size (P < 0.001) and negatively related to ER (P < 0.001) and PR (P < 0.001) expression. Ve value was negatively related to HER2 expression (P = 0.004). The Cox hazard ratio (HR) of ADC, Ktrans, Kep, and Ve values on survival was 5.26 (P = 0.093), 1.081 (P = 0.002), 1.006 (P = 0.941), and 0.883 (P = 0.926), respectively.

CONCLUSIONS

Ktrans value was a best predictive indicator of HG, LNM, ER, PR, and Ki67 expression, and ADC value was the best predictive indicator of HER2. Preoperative use of the 3.0 Tesla could provide important information to determine the optimal treatment plan.

Prostate DCE-MRI with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction using an approximated analytical approach

PURPOSE

To develop and evaluate a practical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction method for prostate dynamic contrast-enhanced (DCE) MRI analysis.

THEORY

We proposed a simple analytical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction method using a Taylor series approximation to the steady-state spoiled gradient echo signal equation. This approach only requires <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> maps and uncorrected pharmacokinetic (PK) parameters as input to estimate the corrected PK parameters.

METHODS

The proposed method was evaluated using a prostate digital reference object (DRO), and 82 in vivo prostate DCE-MRI cases. The approximated analytical correction was compared with the ground truth PK parameters in simulation, and compared with the reference numerical correction in in vivo experiments, using percentage error as the metric.

RESULTS

The prostate DRO results showed that our approximated analytical approach provided residual error less than 0.4% for both K^trans and v_e , compared to the ground truth. This noise-free residual error was smaller than the noise-induced error using the reference numerical correction, which had a minimum error of 2.1+4.3% with baseline signal-to-noise ratio of 234.5. For the 82 in vivo cases, K^trans and v_e percentage error compared to the reference numerical correction method had a mean of 0.1% (95% central range of [0.0%, 0.2%]) across the prostate volume.

CONCLUSION

The approximated analytical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction method provides comparable results with less than 0.2% error within 95% central range, compared to reference numerical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction. The proposed method is a practical solution for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction in prostate DCE-MRI because of its simple implementation.

Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer

RATIONALE AND OBJECTIVES

This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI.

MATERIALS AND METHODS

Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (β), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones.

RESULTS

Cancer (α = 6.45 ± 4.71 s^-1, β = 0.067 ± 0.042 s^-1, slope = 3.78 ± 1.90 s^-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s^-1, β = 0.034 ± 0.050 s^-1, slope = 1.9 ± 1.4 s^-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection.

CONCLUSION

Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.

A direct comparison of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging for prostate cancer detection and prediction of aggressiveness

INTRODUCTION

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) analyse tissue vascularization. We evaluated if CEUS can provide comparable information as DCE-MRI for the detection of prostate cancer (PCa) and prediction of its aggressiveness.

MATERIAL AND METHODS

A post-hoc evaluation of 92 patients was performed. In each patient CEUS and DCE-MRI parameters of the most suspicious lesion identified on MRI were analysed. The predictive values for discrimination between benign lesions, low-/intermediate- and high-grade PCa were evaluated. Results of targeted biopsy served as reference standard (benign lesions, n=51; low- and intermediate-grade PCa [Gleason grade group 1 and 2], n=22; high-grade PCa [≥ Gleason grade group 3], n=19).

RESULTS

In peripheral zone lesions of all tested CEUS parameters only time to peak (TTP_CEUS) showed significant differences between benign lesions and PCa (AUC 0.65). Of all tested DCE-MRI parameters, rate constant (K_ep) was the best discriminator of high-grade PCa in the whole prostate (AUC 0.83) and in peripheral zone lesions (AUC 0.89).

CONCLUSION

DCE-MRI showed a superior performance for detection of PCa and prediction of its aggressiveness. CEUS and DCE-MRI performed better in peripheral zone lesions than in transition zone lesions.

KEY POINTS

• DCE-MRI gathers information about vascularization and capillary permeability characteristics of tissues. • DCE-MRI can detect PCa and predict its aggressiveness. • CEUS also gathers information about vascularization of tissues. • For detection of PCa and prediction of aggressiveness DCE-MRI performed superiorly. • Both imaging techniques performed better in peripheral zone lesions.

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K ^trans and v _e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K ^trans and v _e for the ROI was extracted. The simulations were run 100 times for each ROI with new K ^trans and v _e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n  =  6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K ^trans and v _e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K ^trans and v _e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K ^trans was ~25% to ~50% larger (p  <  0.01) than the ROI-averaged K ^trans. At higher noise levels, the pixel-averaged K ^trans was greater than the 'true' K ^trans, but the ROI-averaged K ^trans was lower than the 'true' K ^trans. The ROI-averaged K ^trans was closer to the true K ^trans than pixel-averaged K ^trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K ^trans was ~15% larger than the ROI-averaged K ^trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

[Focal therapy of prostate cancer]

The target of focal therapy (FT) in prostate cancer (PC) is partial treatment of the prostate aiming at preserving surrounding anatomical structures. The intention is to minimize typical side effects of radical treatment options combined with local tumor control. Numerous established and new technologies are used. Results of published studies showed a good safety profile, few side effects and good preservation of functional results. Oncologic long-term data are lacking so far. Photodynamic therapy (PDT) is the only technology that has been studied in a published prospective randomized trial. The FT is challenged by the multifocality of PC; therefore, the quality of prostate biopsy, histopathological assessment as well as imaging are of paramount importance. Multiparametric magnetic resonance imaging (MRI) has gained increasing importance. The FT is experimental and should only be offered within clinical trials.

Diagnostic value of semi-quantitative and quantitative analysis of functional parameters in multiparametric MRI of the prostate

OBJECTIVE

To determine the diagnostic value of semi-quantitative and quantitative parameters of three functional techniques in multiparametric (mp)-MRI of the prostate.

METHODS

Mp-MRI was performed in 110 patients with suspicion of prostate cancer (PCA) before transrectal ultrasound (TRUS)-guided core biopsy. Peak-enhancement, initial and post-initial enhancement, initial area under gadolinium curve, Ktrans (forward rate constant), Kep (efflux rate constant), Ve (extracellular volume), ADC (apparent diffusion coefficient) and MR spectroscopy ratio were obtained for malignant and benign lesions. For iAUGC, Ktrans, Kep and Ve we evaluated median, mean and the difference (Diff) between mean and median. For ADC we evaluated mean, median, Diff between mean and median, and min. In addition, we evaluated these parameters in dependence of Gleason score in PCA. Receiver operating characteristic analysis and area under curve (AUC) were determined.

RESULTS

ADC min and Kep Diff were the best predictors of malignancy in all lesions (AUC: 0.765). ADC min was the best predictor of malignancy for lesions in peripheral zone (PZ) (AUC: 0.7506) and Kep Diff was the best predictor of malignancy for lesions in transitional zone (AUC: 0.7514). Peak enhancement was the best parameter in differentiation of low-grade PCA with Gleason score 6 from high-grade PCA with Gleason score ≥ 7 (AUC: 0.7692).

CONCLUSION

ADC min can differentiate PCA from benign prostate lesions in PZ. Kep Diff could possibly improve prostate cancer detection in. Peak enhancement might be able to differentiate low grade from high-grade PCA. Semi-quantitative and quantitative parameters may be useful for the functional techniques in mp-MRI. Advances in knowledge: ADC min can differentiate PCA from benign prostate lesions in PZ. Peak enhancement might be able to differentiate low grade from high-grade PCA.

[Diagnostic significance of multiparametric MRI combined with US-fusion guided biopsy of the prostate in patients with increased PSA levels and negative standard biopsy results to detect significant prostate cancer - Correlation with the Gleason score. Korrelation mit dem Gleason Score]

AIMS

To increase diagnostic precision and to reduce overtreatment of low-risk malignant disease, multiparametric MRI (mpMRI) combined with ultrasound (US) fusion guided biopsy of the prostate were performed.

METHODS

In 99 male patients with increased PSA plasma levels and previous negative standard biopsy procedures, mpMRI was carried out followed by US fusion guided perineal biopsy. PI-RADS-Data (PS) of mpMRI and histopathological Gleason score (GS) were categorized and statistically compared.

RESULTS

Lesions in 72/99 (73 %) of patients were determined to be suspect of malignancy, based on a PS 4 or 5. In 33/99 (33 %) of patients, malignancy could not be confirmed by histopathology. With regard to the remaining 66 patients with previous negative biopsy results, 42 (64 %) were diagnosed with a low-grade carcinoma (GS 6, 7a) and 24 (36 %) with a high-grade carcinoma (GS ≥ 7b). The proportion of corresponding results in mpMRI (PS 4-5) when a high-grade carcinoma had been detected, was 21/24 (88 %), which related to a sensitivity of 88 % and a negative predictive value (NPV) of 85 % (p = 0,002). In addition, 35 of 42 patients (83%), graded PS 4-5 in mpMRI, were diagnosed with low-grade carcinoma-positive (p < 0,001). Sensitivity to differentiation between low- and high-grade carcinomas (GS ≤ 7a vs. ≥ 7b) by means of PS was 88 % with a NPV of 70 % (p = 0,74).

CONCLUSION

Our results suggest that mpMRI combined with US-fusion guided biopsy is able to detect considerably higher rates of clinically relevant prostate malignancies compared to conventional diagnostic procedures. However, no statistical significance could be shown regarding the differentiation between high- and low-grade carcinomas. It is hoped that the hybrid methods PSMA-PET/CT or PSMA-PET/MRI will lead to the next optimization step in the differentiation between high- and low-grade carcinomas which so far has been unsatisfactory.

Improved performance of prostate DCE-MRI using a 32-coil vs. 12-coil receiver array

PURPOSE

To assess whether acquisition with 32 receiver coils rather than the vendor-recommended 12 coils provides significantly improved performance in 3D dynamic contrast-enhanced MRI (DCE-MRI) of the prostate.

MATERIALS

The study was approved by the institutional review board and was compliant with HIPAA. 50 consecutive male patients in whom prostate MRI was clinically indicated were prospectively imaged in March 2015 with an accelerated DCE-MRI sequence in which image reconstruction was performed using 12 and 32 coil elements. The two reconstructions were compared quantitatively and qualitatively. The first was done using signal-to-noise ratio (SNR) and g-factor analysis to assess sensitivity to acceleration. The second was done using a five-point scale by two experienced radiologists using criteria of perceived SNR, artifact, sharpness, and overall preference. Significance was assessed with the Wilcoxon signed rank test. Extension to T2-weighted spin-echo and diffusion sequences was assessed in phantom studies.

RESULTS

Reconstruction using 32 vs. 12 coil elements provided improved performance in DCE-MRI based on intrinsic SNR (18% higher) and g-factor statistics (14% higher), with a median 32% higher overall SNR within the prostate volume over all subjects. Reconstruction using 32 coils was qualitatively rated significantly improved (p<0.001) vs. 12 coils on the basis of perceived SNR and radiologist preference and equivalent for sharpness and artifact. Phantom studies suggested the improvement in intrinsic SNR could extend to T2-weighted spin-echo and diffusion sequences.

CONCLUSIONS

Reconstruction of 3D accelerated DCE-MRI studies of the prostate using 32 independent receiver coils provides improved overall performance vs. using 12 coils.

The value of dynamic contrast-enhanced MRI in characterizing complex ovarian tumors

BACKGROUND

The study aimed to investigate the utility of dynamic contrast enhanced MRI (DCE-MRI) in the differentiation of malignant, borderline, and benign complex ovarian tumors.

METHODS

DCE-MRI data of 102 consecutive complex ovarian tumors (benign 15, borderline 16, and malignant 71), confirmed by surgery and histopathology, were analyzed retrospectively. The patterns (I, II, and III) of time-signal intensity curve (TIC) and three semi-quantitative parameters, including enhancement amplitude (EA), maximal slope (MS), and time of half rising (THR), were evaluated and compared among benign, borderline, and malignant ovarian tumors. The types of TIC were compared by Pearson Chi-square χ ² between malignant and benign, borderline tumors. The mean values of EA, MS, and THR were compared using one-way ANOVA or nonparametric Kruskal-Wallis test.

RESULTS

Fifty-nine of 71 (83%) malignant tumors showed a type-III TIC; 9 of 16 (56%) borderline tumors showed a type-II TIC, and 10 of 15 (67%) benign tumors showed a type-II TIC, with a statistically significant difference between malignant and benign tumors (P < 0.001) and between malignant and borderline tumors (P < 0.001). MS was significantly higher in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.013, respectively). THR was significantly lower in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.007, respectively). There was no statistically significant difference between malignant and borderline tumors in MS and THR (P = 0.19, 0.153) or among malignant, borderline, and benign tumors in EA (all P > 0.05).

CONCLUSIONS

DCE-MRI is helpful for characterizing complex ovarian tumors; however, semi-quantitative parameters perform poorly when distinguishing malignant from borderline tumors.

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

PURPOSE

The aim of this work is to establish normality and tumor tissue ranges for perfusion parameters from dynamic contrast-enhanced (DCE) MR of the peripheral prostate at 3T and to compare the diagnostic performance of quantitative and semi-quantitative parameters.

MATERIALS AND METHODS

Thirty-six patients with prostate carcinomas (18 Gleason-6, 15 Gleason-7, and 3 Gleason-8) and 33 healthy subjects were included. Image analysis workflow comprised four steps: manual segmentation of whole prostate and lesions, series registration, voxelwise T1 mapping and calculation of pharmacokinetic and semi-quantitative parameters.

RESULTS

K^trans, v_e, upslope and AUC60 showed statistically significant differences between healthy peripheral areas and tumors. Curve type showed no association with healthy/tumor peripheral areas (chi-square=0.702). Areas under the ROC curves were 0.64 (95% CI: 0.54-0.75), 0.70 (0.60-0.80), 0.62 (0.51-0.72) and 0.63 (0.52-0.74) for K^trans, v_e, upslope and AUC60, respectively. The optimal cutoff values were: K^trans=0.21min^-1 (sensitivity=0.61, specificity=0.64), v_e=0.36 (0.63, 0.71), upslope=0.59 (0.59, 0.59) and AUC60=2.4 (0.63, 0.64). Significant differences were found between Gleason scores 6 and 7 for normalized K^trans, upslope and AUC60, with good diagnostic accuracy (area under ROC curve 0.80, 95% CI: 0.60-1.00).

CONCLUSION

Quantitative (K^trans and v_e) and semi-quantitative (upslope and AUC60) perfusion parameters showed significant differences between tumors and control areas in the peripheral prostate. Normalized K^trans, upslope and AUC60 values might characterize tumor aggressiveness.

[MR imaging of the prostate]

CLINICAL ISSUE

Prostate cancer is the most common form of cancer in men in Germany; however, there is a distinct difference between incidence and mortality.

STANDARD TREATMENT

The detection of prostate cancer is based on clinical and laboratory testing using serum prostate-specific antigen (PSA) levels and transrectal ultrasound with randomized biopsy.

DIAGNOSTIC WORK-UP

Multiparametric MR imaging of the prostate can provide valuable diagnostic information for detection of prostate cancer, especially after negative results of a biopsy prior to repeat biopsy.

PERFORMANCE

In addition the use of MR ultrasound fusion-guided biopsy has gained in diagnostic importance and has increased the prostate cancer detection rate.

ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS

The prostate imaging reporting and data system (PI-RADS) classification has standardized the reporting of prostate MRI which has positively influenced the acceptance by urologists.

Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method

PURPOSE

To develop and evaluate a prostate-based method (PBM) for estimating pharmacokinetic parameters on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) by leveraging inherent differences in pharmacokinetic characteristics between the peripheral zone (PZ) and transition zone (TZ).

MATERIALS AND METHODS

This retrospective study, approved by the Institutional Review Board, included 40 patients who underwent a multiparametric 3T MRI examination and subsequent radical prostatectomy. A two-step PBM for estimating pharmacokinetic parameters exploited the inherent differences in pharmacokinetic characteristics associated with the TZ and PZ. First, the reference region model was implemented to estimate ratios of K^trans between normal TZ and PZ. Subsequently, the reference region model was leveraged again to estimate values for K^trans and v_e for every prostate voxel. The parameters of PBM were compared with those estimated using an arterial input function (AIF) derived from the femoral arteries. The ability of the parameters to differentiate prostate cancer (PCa) from benign tissue was evaluated on a voxel and lesion level. Additionally, the effect of temporal downsampling of the DCE MRI data was assessed.

RESULTS

Significant differences (P < 0.05) in PBM K^trans between PCa lesions and benign tissue were found in 26/27 patients with TZ lesions and in 33/38 patients with PZ lesions; significant differences in AIF-based K^trans occurred in 26/27 and 30/38 patients, respectively. The 75^th and 100^th percentiles of K^trans and v_e estimated using PBM positively correlated with lesion size (P < 0.05).

CONCLUSION

Pharmacokinetic parameters estimated via PBM outperformed AIF-based parameters in PCa detection. J. Magn. Reson. Imaging 2016;44:1405-1414.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as K^trans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and v_e (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T₁ were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of K^trans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in K^trans than v_e and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, k_ep (= K^trans/v_e), was less sensitive to AIF variation than K^trans (wCV for unadjusted AIFs: 0.45 for k_epvs. 0.74 for K^trans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than K^trans.

Diagnostic value of dynamic contrast-enhanced magnetic resonance imaging in rectal cancer and its correlation with tumor differentiation

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a novel imaging modality that can be used to reflect the microcirculation, although its value in diagnosing rectal cancer is unknown. The present study aimed to explore the clinical application of DCE-MRI in the preoperative diagnosis of rectal cancer, and its correlation with tumor differentiation. To achieve this, 40 pathologically confirmed patients with rectal cancer and 15 controls were scanned using DCE-MRI. The Tofts model was applied to obtain the perfusion parameters, including the plasma to extravascular volume transfer (Ktrans), the extravascular to plasma volume transfer (Kep), the extravascular fluid volume (Ve) and the initial area under the enhancement curve (iAUC). Receiver-operating characteristic (ROC) curves were plotted to determine the diagnostic value. The results demonstrated that the time-signal intensity curve of the rectal cancer lesion exhibited an outflow pattern. The Ktrans, Kep, Ve, and iAUC values were higher in the cancer patients compared with controls (P<0.05). The intraclass correlation coefficients of Ktrans, Kep, Ve and iAUC, as measured by two independent radiologists, were 0.991, 0.988, 0.972 and 0.984, respectively (all P<0.001), indicating a good consistency. The areas under the ROC curves for Ktrans and iAUC were both >0.9, resulting in a sensitivity and specificity of 100% and 93.3% for Ktrans, and of 92.5%, and 93.3% or 100%, for iAUC, respectively. In the 40 rectal cancer cases, there was a moderate correlation between Ktrans and iAUC, and pathological differentiation (0.3<r<0.8, all P<0.05). In conclusion, Ktrans and iAUC were associated with the presence of rectal cancer and differentiation, and therefore may provide novel insights into the preoperative diagnosis of rectal cancer.

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Background

Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology.

Results

Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters.

Conclusion

We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

IV Administered Gadodiamide Enters the Lumen of the Prostatic Glands: X-Ray Fluorescence Microscopy Examination of a Mouse Model

OBJECTIVE

Dynamic contrast-enhanced MRI (DCE-MRI) has become a standard component of multiparametric protocols for MRI examination of the prostate, and its use is incorporated into current guidelines for prostate MRI examination. Analysis of DCE-MRI data for the prostate is usually based on the distribution of gadolinium-based agents, such as gadodiamide, into two well-mixed compartments, and it assumes that gadodiamide does not enter into the glandular lumen. However, this assumption has not been directly tested. The purpose of this study was to use x-ray fluorescence microscopy (XFM) imaging in situ to measure the concentration of gadodiamide in the epithelia and lumens of the prostate of healthy mice after IV injection of the contrast agent.

MATERIALS AND METHODS

Six C57Bl6 male mice (age, 28 weeks) were sacrificed 10 minutes after IV injection of gadodiamide (0.13 mmol/kg), and three mice were sacrificed after saline injection. Prostate tissue samples obtained from each mouse were harvested and frozen; 7-μm-thick slices were sectioned for XFM imaging, and adjacent 5-μm-thick slices were sectioned for H and E staining. Elemental concentrations were determined from XFM images.

RESULTS

A mean (± SD) baseline concentration of gadolinium of 0.01 ± 0.01 mM was determined from XFM measurements of prostatic tissue samples when no gadodiamide was administered, and it was used to determine the measurement error. When gadodiamide was added, the mean concentrations of gadolinium in the epithelia and lumens in 32 prostatic glands from six mice were 1.00 ± 0.13 and 0.36 ± 0.09 mM, respectively.

CONCLUSION

Our data suggest that IV administration of gadodiamide results in uptake of contrast agent by the glandular lumens of the mouse prostate. We were able to quantitatively determine gadodiamide distributions in mouse prostatic epithelia and lumens.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Integration of imaging into clinical practice to assess the delivery and performance of macromolecular and nanotechnology-based oncology therapies

Functional and molecular imaging has become increasingly used to evaluate interpatient and intrapatient tumor heterogeneity. Imaging allows for assessment of microenvironment parameters including tumor hypoxia, perfusion and proliferation, as well as tumor metabolism and the intratumoral distribution of specific molecular markers. Imaging information may be used to stratify patients for targeted therapies, and to define patient populations that may benefit from alternative therapeutic approaches. It also provides a method for non-invasive monitoring of treatment response at earlier time-points than traditional cues, such as tumor shrinkage. Further, companion diagnostic imaging techniques are becoming progressively more important for development and clinical implementation of targeted therapies. Imaging-based companion diagnostics are likely to be essential for the validation and FDA approval of targeted nanotherapies and macromolecular medicines. This review describes recent clinical advances in the use of functional and molecular imaging to evaluate the tumor microenvironment. Additionally, this article focuses on image-based assessment of distribution and anti-tumor effect of nano- and macromolecular systems.

PROMIS--Prostate MR imaging study: A paired validating cohort study evaluating the role of multi-parametric MRI in men with clinical suspicion of prostate cancer

BACKGROUND

Transrectal ultrasound-guided prostate biopsies are prone to detection errors. Multi-parametric MRI (MP-MRI) may improve the diagnostic pathway.

METHODS

PROMIS is a prospective validating paired-cohort study that meets criteria for level 1 evidence in diagnostic test evaluation. PROMIS will investigate whether multi-parametric (MP)-MRI can discriminate between men with and without clinically-significant prostate cancer who are at risk prior to first biopsy. Up to 714 men will have MP-MRI (index), 10-12 core TRUS-biopsy (standard) and 5mm transperineal template mapping (TPM) biopsies (reference). The conduct and reporting of each test will be blinded to the others.

RESULTS

PROMIS will measure and compare sensitivity, specificity, and positive and negative predictive values of both MP-MRI and TRUS-biopsy against TPM biopsies. The MP-MRI results will be used to determine the proportion of men who could safely avoid biopsy without compromising detection of clinically-significant cancers. For the primary outcome, significant cancer on TPM is defined as Gleason grade >/= 4+3 and/or maximum cancer core length of ≥ 6 mm. PROMIS will also assess inter-observer variability among radiologists among other secondary outcomes. Cost-effectiveness of MP-MRI prior to biopsy will also be evaluated.

CONCLUSIONS

PROMIS will determine whether MP-MRI of the prostate prior to first biopsy improves the detection accuracy of clinically-significant cancer.

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters

OBJECTIVES

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist.

METHODS

Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K(trans),Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer.

RESULTS

Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively.

CONCLUSION

The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data.

KEY POINTS

• The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters • The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI • The computed model improves tumour detection compared to an expert viewing parametric maps.

Dynamic Contrast-Enhanced MRI in the Study of Brain Tumors. Comparison Between the Extended Tofts-Kety Model and a Phenomenological Universalities (PUN) Algorithm

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a well-established technique for studying blood-brain barrier (BBB) permeability that allows measurements to be made for a wide range of brain pathologies, including multiple sclerosis and brain tumors (BT). This latter application is particularly interesting, because high-grade gliomas are characterized by increased microvascular permeability and a loss of BBB function due to the structural abnormalities of the endothelial layer. In this study, we compared the extended Tofts-Kety (ETK) model and an extended derivate class from phenomenological universalities called EU1 in 30 adult patients with different BT grades. A total of 75 regions of interest were manually drawn on the MRI and subsequently analyzed using the ETK and EU1 algorithms. Significant linear correlations were found among the parameters obtained by these two algorithms. The means of R (2) obtained using ETK and EU1 models for high-grade tumors were 0.81 and 0.91, while those for low-grade tumors were 0.82 and 0.85, respectively; therefore, these two models are equivalent. In conclusion, we can confirm that the application of the EU1 model to the DCE-MRI experimental data might be a useful alternative to pharmacokinetic models in the study of BT, because the analytic results can be generated more quickly and easily than with the ETK model.