Abbreviated MR Protocols in Prostate MRI

Prostate MRI is an integral part of the clinical work-up in biopsy-naïve patients with suspected prostate cancer, and its use has been increasing steadily over the last years. To further its general availability and the number of men benefitting from it and to reduce the costs associated with MR, several approaches have been developed to shorten examination times, e.g., by focusing on sequences that provide the most useful information, employing new technological achievements, or improving the workflow in the MR suite. This review highlights these approaches; discusses their implications, advantages, and disadvantages; and serves as a starting point whenever an abbreviated prostate MRI protocol is being considered for implementation in clinical routine

Radiological imaging following pelvic prolapse surgery

CLINICAL/METHODICAL ISSUE Pelvic organ prolapse is a common condition in women, for which both conservative and surgical interventions are available. Knowledge of the different surgical procedures and the materials used is essential for adequate radiological diagnosis after prolapse surgery in order to differentiate potential complications from normal postoperative changes. STANDARD RADIOLOGICAL METHODS In the immediate postoperative period, computed tomography (CT) is often the modality of choice for evaluating acute complications such as bleeding or organ injuries. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and is therefore generally preferred for assessing subacute and chronic complications. METHODICAL INNOVATIONS Innovative techniques such as dynamic MRI protocols can improve the radiological assessment after prolapse surgery by enabling the evaluation of organ mobility. PERFORMANCE Radiological standard procedures such as computed tomography (CT) and MRI provide detailed and reliable information about the postoperative site and potential complications following prolapse surgery. ACHIEVEMENTS Radiological imaging plays an important role in the evaluation of patients after prolapse surgery, particularly when complications are suspected. Accurate radiological diagnosis can guide further appropriate therapeutic measures

Contrast media kinetics in multiparametric magnetic resonance imaging before radical prostatectomy predicts the probability of postoperative incontinence.

PURPOSE To evaluate the role of preoperative multiparametric magnetic resonance imaging (MRI) as predictor of post-prostatectomy incontinence (PPI). METHODS We analyzed patients who underwent robot-assisted radical prostatectomy for localized prostate cancer at our institution between July 2015 and April 2017. In these patients, we measured the perfusion quality of the pelvic floor with contrast media kinetics in the preoperative MRI of the prostate and compared the levator ani muscle (region of interest) to the surrounding pelvic muscle structures (reference). Prospectively collected questionnaires regarding urinary incontinence were then evaluated 1 year postoperatively. Outcomes were dichotomized into "continent" (ICIQ-Score = 0-5) and "incontinent" (ICIQ-Score ≥ 6). In each patient, we determined the perfusion ratio of the levator ani muscle divided by the surrounding pelvic muscle structures and compared them among the groups. RESULTS Forty-two patients were included in the study (n = 22 in "continent", n = 20 in "incontinent" group). The median perfusion ratio from the continent group was significantly higher compared to the incontinent group (1.61 vs. 1.15; 95% CI 0.09-0.81, p = 0.015). The median perfusion ratio in "excellent" (ICIQ-Score = 0) was significantly higher than in "poor" (ICIQ-Score ≥ 11) outcomes (1.48 vs. 0.94; 95% CI 0.04-1.03, p = 0.036). Further, a higher perfusion ratio was negatively correlated with ICIQ-Score (r = - 0.33; 95% CI - 0.58 to 0.03; p = 0.031). CONCLUSIONS Our data demonstrate a promising new strategy to predict PPI through the perfusion quality of pelvic muscle structures with contrast media kinetics. This may facilitate preoperative patient consulting and decision-making

Risk factors for prostate cancer in men with false-negative mpMRI: A retrospective single center cohort study of image quality scores and clinical parameters

PURPOSE To identify predictors of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in men with prior false-negative multiparametric MRI (mpMRI), focusing on image quality scoring systems and clinical parameters. METHODS In this IRB-approved retrospective single-center study, patients with a negative mpMRI (PI-RADS score ≤2) and subsequent prostate biopsies were included. Histopathological results served as reference standard. Welch's t-Test was conducted to identify significant differences in image quality scores (PI-QUAL and PSHS) between patients with and without PCa/csPCA. In addition, clinical parameters (age, BMI, PSA density) and image quality scores (PI-QUAL and PSHS) were examined as potential predictors of PCa/csPCa detection after a false-negative mpMRI in uni- and multivariate analyses. RESULTS Among 96 patients with negative mpMRI results, 44.8 % had PCa and 16.7 % had csPCa upon biopsy with histopathological confirmation. PI-QUAL scores were significantly lower in patients with PCa (p = 0.03) and csPCa (p = 0.005). PSHS scores were lower in patients with csPCa, but the difference was not statistically significant (p = 0.1). Higher age (p = 0.035) and a lower PI-QUAL score (p < 0.004) were predictors of subsequent csPCa detection upon biopsy, however, a lower PI-QUAL score was the only independent predictor of missed csPCa in false-negative mpMRIs. CONCLUSIONS Lower image quality scores were associated with missed PCa/csPCa in patients with false-negative mpMRIs, with PI-QUAL being an independent predictor of failed csPCa detection. This highlights the importance of image quality for prostate MRI and advocats the inclusion of its measurement into the standardized report

Index lesion contouring on prostate MRI for targeted MRI/US fusion biopsy - Evaluation of mismatch between radiologists and urologists

PURPOSE: Mistargeting of focal lesions due to inaccurate segmentations can lead to false-negative findings on MRI-guided targeted biopsies. The purpose of this retrospective study was to examine inter-reader agreement of prostate index lesion segmentations from actual biopsy data between urologists and radiologists. METHOD: Consecutive patients undergoing transperineal MRI-targeted prostate biopsy for PI-RADS 3-5 lesions between January 2020 and December 2021 were included. Agreement between segmentations on T2w-images between urologists and radiologists was assessed with Dice similarity coefficient (DSC) and 95 % Hausdorff distance (95 % HD). Differences in similarity scores were compared using Wilcoxon test. Differences depending on lesion features (size, zonal location, PI-RADS scores, lesion distinctness) were tested with Mann-Whitney U test. Correlation with prostate signal-intensity homogeneity score (PSHS) and lesion size was tested with Spearman's rank correlation. RESULTS: Ninety-three patients (mean age 64.9 ± 7.1y, median serum PSA 6.5 [4.33-10.00]) were included. Mean similarity scores were statistically significantly lower between urologists and radiologists compared to radiologists only (DSC 0.41 ± 0.24 vs. 0.59 ± 0.23, p < 0.01; 95 %HD 6.38 ± 5.45 mm vs. 4.47 ± 4.12 mm, p < 0.01). There was a moderate and strong positive correlation between DSC scores and lesion size for segmentations from urologists and radiologists (ρ = 0.331, p = 0.002) and radiologists only (ρ = 0.501, p < 0.001). Similarity scores were worse in lesions ≤ 10 mm while other lesion features did not significantly influence similarity scores. CONCLUSION: There is significant mismatch of prostate index lesion segmentations between urologists and radiologists. Segmentation agreement positively correlates with lesion size. PI-RADS scores, zonal location, lesion distinctness, and PSHS show no significant impact on segmentation agreement. These findings could underpin benefits of perilesional biopsies

Variability of Manual Segmentation of the Prostate in Axial T2-weighted MRI: A Multi-Reader Study

Purpose To evaluate the interreader variability in prostate and seminal vesicle (SV) segmentation on T2w MRI. Methods Six readers segmented the peripheral zone (PZ), transitional zone (TZ) and SV slice-wise on axial T2w prostate MRI examinations of n = 80 patients. Twenty different similarity scores, including dice score (DS), Hausdorff distance (HD) and volumetric similarity coefficient (VS), were computed with the VISCERAL EvaluateSegmentation software for all structures combined and separately for the whole gland (WG = PZ + TZ), TZ and SV. Differences between base, midgland and apex were evaluated with DS slice-wise. Descriptive statistics for similarity scores were computed. Wilcoxon testing to evaluate differences of DS, HD and VS was performed. Results Overall segmentation variability was good with a mean DS of 0.859 (±SD = 0.0542), HD of 36.6 (±34.9 voxels) and VS of 0.926 (±0.065). The WG showed a DS, HD and VS of 0.738 (±0.144), 36.2 (±35.6 vx) and 0.853 (±0.143), respectively. The TZ showed generally lower variability with a DS of 0.738 (±0.144), HD of 24.8 (±16 vx) and VS of 0.908 (±0.126). The lowest variability was found for the SV with DS of 0.884 (±0.0407), HD of 17 (±10.9 vx) and VS of 0.936 (±0.0509). We found a markedly lower DS of the segmentations in the apex (0.85 ± 0.12) compared to the base (0.87 ± 0.10, p < 0.01) and the midgland (0.89 ± 0.10, p < 0.001). Conclusions We report baseline values for interreader variability of prostate and SV segmentation on T2w MRI. Variability was highest in the apex, lower in the base, and lowest in the midgland

Prediction of pelvic lymph node metastases and PSMA PET positive pelvic lymph nodes with multiparametric MRI and clinical information in primary staging of prostate cancer

PURPOSE To compare the accuracy of multiparametric MRI (mpMRI), $^{68}$Ga-PSMA PET and the Briganti 2019 nomogram in the prediction of metastatic pelvic lymph nodes (PLN) in prostate cancer, to assess the accuracy of mpMRI and the Briganti nomogram in prediction of PET positive PLN and to investigate the added value of quantitative mpMRI parameters to the Briganti nomogram. METHOD This retrospective IRB-approved study included 41 patients with prostate cancer undergoing mpMRI and $^{68}$Ga-PSMA PET/CT or MR prior to prostatectomy and pelvic lymph node dissection. A board-certified radiologist assessed the index lesion on diffusion-weighted (Apparent Diffusion Coefficient, ADC; mean/volume), T2-weighted (capsular contact length, lesion volume/maximal diameters) and contrast-enhanced (iAUC, k$_{ep}$, K$^{trans}$, v$_{e}$) sequences. The probability for metastatic pelvic lymph nodes was calculated using the Briganti 2019 nomogram. PET examinations were evaluated by two board-certified nuclear medicine physicians. RESULTS The Briganti 2019 nomogram performed superiorly (AUC: 0.89) compared to quantitative mpMRI parameters (AUCs: 0.47-0.73) and $^{68}$Ga-PSMA-11 PET (AUC: 0.82) in the prediction of PLN metastases and superiorly (AUC: 0.77) in the prediction of PSMA PET positive PLN compared to MRI parameters (AUCs: 0.49-0.73). The addition of mean ADC and ADC volume from mpMRI improved the Briganti model by a fraction of new information of 0.21. CONCLUSIONS The Briganti 2019 nomogram performed superiorly in the prediction of metastatic and PSMA PET positive PLN, but the addition of parameters from mpMRI can further improve its accuracy. The combined model could be used to stratify patients requiring ePLND or PSMA PET

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow

External Validation and Comparison of Prostate Cancer Risk Calculators Incorporating Multiparametric Magnetic Resonance Imaging for Prediction of Clinically Significant Prostate Cancer

PURPOSE: To externally validate recently published prostate cancer risk calculators (PCa-RCs) incorporating multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer (csPCa) and compare their performance to mpMRI-naïve PCa-RCs. MATERIAL AND METHODS: Men without previous PCa diagnosis undergoing transperineal template saturation prostate biopsy with fusion-guided targeted biopsy between 11/2014 and 03/2018 in our academic tertiary referral center were identified. Any Gleason pattern ≥4 was defined to be csPCa. Predictors (age, PSA, DRE, prostate volume, family history, previous prostate biopsy and highest region of interest according to PIRADS) were retrospectively collected. Four mpMRI-PCa-RCs and two mpMRI-naïve PCa-RCs were evaluated for their discrimination, calibration and clinical net benefit using a ROC analysis, calibration plots and a decision curve analysis, respectively. RESULTS: Out of 468 men, 193 (41%) were diagnosed with csPCa. Three mpMRI-PCa-RCs showed similar discrimination with area-underneath-the-receiver-operating-characteristic-curves (AUC) from 0.83 to 0.85, which was significantly higher than the other PCa-RCs (AUCs: 0.69-0.74). Calibration-in-the-large showed minimal deviation from the true amount of csPCa by 2% for two mpMRI-PCa-RCs, while the other PCa-RCs showed worse calibration (11-27%). A clinical net benefit could only be observed for three mpMRI-PCa-RCs at biopsy thresholds ≥15%, while none of the six investigated PCa-RCs demonstrated clinical utility against a biopsy all strategy at thresholds <15%. CONCLUSIONS: Performance of the mpMRI-PCa-RCs varies, but they generally outperform mpMRI-naïve PCa-RCs in regard to discrimination, calibration and clinical usefulness. External validation in other biopsy settings is highly encouraged