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This is a data article from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy” [1]. From January 2014 to April 2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate cancer (PCa) detection by TB and/or additional SB was analyzed.

Background: To analyze contrast free adrenal vein sampling (AVS) for differentiating unilateral from bilateral disease in patients diagnosed with hypertension due to primary aldosteronism (PA)., Methods: Consecutive patients with PA and subsequent contrast medium free AVS between April 2015 and March 2020 were retrospectively included. Cross-sectional imaging (CSI), AVS and clinical data were analyzed regarding diagnostic performance. In addition, patients with lateralisation receiving adrenalectomy were compared to a control group treated with mineralocorticoid antagonists., Results: In total 186 patients with AVS were included. The success rate for bilateral catheterization was 88% (median effective dose 2.8 mSv). CSI had an accuracy of 60% (CI: 0.52-0.67) in the detection of lateralization compared to AVS. Patients with bilateral adrenal hyperplasia and those with aldosterone-producing adenoma did not differ in systolic blood pressure (sBP) (p = 0.63) or number of antihypertensive drugs (NAD) (p = 0.11). After adrenalectomy, 28 patients were cured (51%; sBP ≤130 mmHg, NAD = 0), 18 were improved (33%; decrease of sBP ≥20 mmHg and NAD), and 8 were unchanged (15%). Serum renin increased significantly after treatment (p < 0.01)., Conclusion: Contrast medium free AVS is a reliable procedure in the diagnostic management of patients with PA with high technical success rate. The accordance between CSI and results from AVS was only moderate indicating the central role of AVS in the diagnostic work-up of patients with PA. Patients with predominant disease diagnosed with AVS had a high cure rate and/or significant improvement after adrenalectomy., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objectives: Diffusion weighted imaging (DWI) is the most important sequence for detection and grading prostate cancer (PCa), but it is considerably prone to artifacts. New approaches like zoomed single-shot imaging (z-EPI) with advanced image processing or multi-shot readout segmentation (rs-EPI) try to improve DWI quality. This study evaluates objective and subjective image quality (IQ) of rs-EPI and z-EPI with and without advanced processing., Materials and Methods: Fifty-six consecutive patients (67 ± 8 years; median PSA 8.3 ng/ml) with mp-MRI performed at 3 Tesla between February and October 2019 and subsequently verified PCa by targeted plus systematic MRI/US-fusion biopsy were included in this retrospective single center cohort study. Rs-EPI and z-EPI were prospectively acquired in every patient. Signal intensities (SI) of PCa and benign tissue in ADC, b1000, and calculated high b-value images were analyzed. Endpoints were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), PCa contrast intensity (CI), and subjective IQ on a 5-point scale evaluated by three blinded readers. Wilcoxon signed rank test, Friedman test and Cohen's kappa coefficient was calculated., Results: SNR, CNR, and PCa CI of z-EPI with and without advanced processing was superior to rs-EPI (p < 0.01), whereas no significant differences were observed between z-EPI with and without advanced processing. Subjective IQ was significantly higher for z-EPI with advanced processing compared rs-EPI for ADC, b1000, and calculated high b-values (p < 0.01). Compared to z-EPI without advanced processing, z-EPI with advanced processing was superior for ADC and calculated high b-values (p < 0.01), but no significant differences were shown for b1000 images., Conclusions: Z-EPI with and without advanced processing was superior to rs-EPI regarding objective imaging parameters and z-EPI with advanced processing was superior to rs-EPI regarding subjective imaging parameters for the detection of PCa., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment. Purpose To propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC. Methods and population The previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios. Results Compared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly. Conclusion Downgrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: To determine the capability of Gadolinium-free arterial spin labelling (ASL) sequences as novel, contrast-free, non-invasive alternative perfusion imaging method to differentiate prostate cancer (PCA) from benign prostate tissue compared to conventional DCE MRI., Methods: Thirty men with histologically confirmed PCA were included in this prospectively enrolled single center cohort study. All patients received multiparametric MRI (T2, DWI, DCE) at 3 T with additional ASL of the PCA lesion. Primary endpoint was differentiability of PCA versus benign prostate tissue by signal intensities (SI) and contrast ratios (CR) in ASL in comparison to DCE. For DCE also Signal-Enhancement-Ratio (SER) of native and early contrast enhancement SI was assessed. Secondary objectives were differences regarding PCA localisation in peripheral (PZ) or transition zone (TZ) and PCA detection., Results: In both, ASL and DCE, average SI of PCA differed significantly from SI in benign tissue in the TZ and PZ (p < 0,01, respectively). ASL had significantly higher CR discerning PCA and benign tissue in PZ and TZ (PZ = 5.19; TZ = 6.45) compared to DCE SI (PZ = 1.61; TZ = 1.43) and DCE SER (PZ = 1.59; TZ = 1.43) (p < 0.01, respectively). In subjective evaluation, PCA could be detected in ASL in 28 patients, compared to 29 in DCE., Conclusion: ASL had significantly higher CR differentiating PCA from benign tissue in PZ and TZ compared to DCE. Visual detection of PCA does not differ significantly between the two sequences. As perfusion gadolinium-based contrast media is seen more critical in the last few years, ASL seems to be a promising alternative to DCE in PCA detection., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: Reproducibility of quantitative perfusion analysis of DCE requires a standardized AIF acquisition. However, there are many different approaches for AIF assessment so that the absolute values of perfusion parameters may vary depending on the used method. This study analyzes the influence of the method of AIF determination on quantitative DCE-MRI., Methods: In this retrospective, single-center, cohort study three different methods of AIF determination in 50 consecutive patients with multiparametric MRI of the prostate were conducted. As a reference, AIF was selected manually by defining a region of interest in an artery manually (AIF m ). The second method (AIF a ), based on an automated algorithm and the third, population-derived AIF p where then compared. Primary endpoint were differences in the performance of the perfusion parameters K trans , ve and k ep regarding the AIF acquisition methods, secondary endpoints consisted of the evaluation of differences in the peripheral and transition zone of the prostate (PZ, TZ)., Results: In all three methods, K trans , ve, and k ep were significantly higher in PZ than in TZ with K trans showing least overlapping. There were no significant differences for K trans determined with AIF m and AIF a (0.3 ± 0.2 min -1 for PZ for both and 0.5 ± 0.3 min -1 for TZ in AIF m and 0.4 ± 0.3 min -1 in AIF a ), while there were great differences between AIF a and AIF p and AIF m and AIF p (0.1 ± 0.03 min -1 for TZ and PZ in AIF p ). Spearman test demonstrated good correlation of values for K trans and k ep in all 3 methods (ρ ≥ 0.76). AIF a showed a success rate of 98% in finding the artery., Conclusion: AIF a is a recommendable user-independent automatical method to determine quantitative perfusion parameters allowing an objective measurement and saving interactive time for the radiologist. AIF p may be applied as second alternative method., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To assess the feasibility of full diffusional kurtosis tensor imaging (DKI) in prostate MRI in clinical routine. Histopathological correlation was achieved by targeted biopsy., Materials and Methods: Thirty-one men were prospectively included in the study. Twenty-one were referred to our hospital with increased prostate specific antigen (PSA) values (>4ng/ml) and suspicion of prostate cancer. The other 10 men were volunteers without any history of prostate disease. DKI applying diffusion gradients in 20 different spatial directions with four b-values (0, 300, 600, 1000s/mm(2)) was performed additionally to standard functional prostate MRI. Region of interest (ROI)-based measurements were performed in all histopathologically verified lesions of every patient, as well as in the peripheral zone, and the central gland of each volunteer., Results: DKI showed a substantially better fit to the diffusion-weighted signal than the monoexponential apparent diffusion coefficient (ADC). Altogether, 29 lesions were biopsied in 14 different patients with the following results: Gleason score 3+3=6 (n=1), 3+4=7 (n=7), 4+3=7 (n=6), 4+4=8 (n=1), and 4+5=9 (n=2), and prostatitis (n=12). Values of axial (Kax) and mean kurtosis (Kmean) were significantly different in the tumor (Kax 1.78±0.39, Kmean 1.84±0.43) compared with the normal peripheral zone (Kax 1.09±0.12, Kmean 1.16±0.13; p<0.001) or the central gland (Kax 1.40±0.12, Kmean 1.44±0.17; p=0.01 respectively). There was a minor correlation between axial kurtosis (r=0.19) and the Gleason score., Conclusion: Full DKI is feasible to utilize in a routine clinical setting. Although there is some overlap some DKI parameters can significantly distinguish prostate cancer from the central gland or the normal peripheral zone. Nevertheless, the additional value of DKI compared with conventional monoexponential ADC calculation remains questionable and requires further research., (Copyright © 2014 Elsevier Inc. All rights reserved.)