Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images: evaluation in a cohort of patients on active surveillance.

PURPOSE: To assess a region-of-interest-based computer-assisted diagnosis system (CAD) in characterizing aggressive prostate cancer on magnetic resonance imaging (MRI) from patients under active surveillance (AS). METHODS: A prospective biopsy database was retrospectively searched for patients under AS who underwent MRI and subsequent biopsy at our institution. MRI lesions targeted at baseline biopsy were retrospectively delineated to calculate the CAD score that was compared to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score assigned at baseline biopsy. RESULTS: 186 patients were selected. At baseline biopsy, 51 and 15 patients had International Society of Urological Pathology (ISUP) grade ≥ 2 and ≥ 3 cancer respectively. The CAD score had significantly higher specificity for ISUP ≥ 2 cancers (60% [95% confidence interval (CI): 51-68]) than the PI-RADS score (≥ 3 dichotomization: 24% [CI: 17-33], p = 0.0003; ≥ 4 dichotomization: 32% [CI: 24-40], p = 0.0003). It had significantly lower sensitivity than the PI-RADS ≥ 3 dichotomization (85% [CI: 74-92] versus 98% [CI: 91-100], p = 0.015) but not than the PI-RADS ≥ 4 dichotomization (94% [CI:85-98], p = 0.104). Combining CAD findings and PSA density could have avoided 47/184 (26%) baseline biopsies, while missing 3/51 (6%) ISUP 2 and no ISUP ≥ 3 cancers. Patients with baseline negative CAD findings and PSAd < 0.15 ng/mL2 who stayed on AS after baseline biopsy had a 9% (4/44) risk of being diagnosed with ISUP ≥ 2 cancer during a median follow-up of 41 months, as opposed to 24% (18/74) for the others. CONCLUSION: The CAD could help define AS patients with low risk of aggressive cancer at baseline assessment and during subsequent follow-up.

Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy.

Purpose To determine the performance of a computer-aided diagnosis (CAD) system trained at characterizing cancers in the peripheral zone (PZ) with a Gleason score of at least 7 in patients referred for multiparametric magnetic resonance (MR) imaging before prostate biopsy. Materials and Methods Two institutional review board-approved prospective databases of patients who underwent multiparametric MR imaging before prostatectomy (database 1) or systematic and targeted biopsy (database 2) were retrospectively used. All patients gave informed consent for inclusion in the databases. A CAD combining the 10th percentile of the apparent diffusion coefficient and the time to peak of enhancement was trained to detect cancers in the PZ with a Gleason score of at least 7 in 106 patients from database 1. The CAD was tested in 129 different patients from database 2. All targeted lesions were prospectively scored at biopsy by using a five-level Likert score. The CAD scores were retrospectively calculated. Biopsy results were used as the reference standard. Areas under the receiver operating characteristic curves (AUCs) were computed for CAD and Likert scores by using binormal smoothing for per-lesion and per-lobe analyses, and a density function for per-patient analysis. Results The CAD outperformed the Likert score in the overall population and all subgroups, except in the transition zone. The difference was statistically significant for the overall population (AUC, 0.95 [95% confidence interval {CI}: 0.90, 0.98] vs 0.88 [95% CI: 0.68, 0.96]; P = .02) at per-patient analysis, and for less-experienced radiologists (<1 year) at per-lesion (AUC, 0.90 [95% CI: 0.81, 0.95] vs 0.83 [95% CI: 0.73, 0.90]; P = .04) and per-lobe (AUC, 0.92 [95% CI: 0.80, 0.96] vs 0.84 [95% CI: 0.72, 0.91]; P = .04) analysis. Conclusion The CAD outperformed the Likert score prospectively assigned at biopsy in characterizing cancers with a Gleason score of at least 7. © RSNA, 2018 Online supplemental material is available for this article.

Brain palpation from physiological vibrations using MRI.

We present a magnetic resonance elastography approach for tissue characterization that is inspired by seismic noise correlation and time reversal. The idea consists of extracting the elasticity from the natural shear waves in living tissues that are caused by cardiac motion, blood pulsatility, and any muscle activity. In contrast to other magnetic resonance elastography techniques, this noise-based approach is, thus, passive and broadband and does not need any synchronization with sources. The experimental demonstration is conducted in a calibrated phantom and in vivo in the brain of two healthy volunteers. Potential applications of this "brain palpation" approach for characterizing brain anomalies and diseases are foreseen.

Stiffness of benign and malignant prostate tissue measured by shear-wave elastography: a preliminary study.

OBJECTIVES: To measure benign and malignant prostate tissue stiffness using shear-wave elastography (SWE). METHODS: Thirty consecutive patients underwent transrectal SWE in the axial and sagittal planes before prostatectomy. After reviewing prostatectomy specimens, two radiologists measured stiffness in regions corresponding to cancers, lateral and median benign peripheral zone (PZ) and benign transition zone (TZ). RESULTS: Cancers were stiffer than benign PZ and TZ. All tissue classes were stiffer on sagittal than on axial imaging, in TZ than in PZ, and in median PZ than in lateral PZ. At multivariate analysis, the nature of tissue (benign or malignant; P < 0.00001), the imaging plane (axial or sagittal; P < 0.00001) and the location within the prostate (TZ, median PZ or lateral PZ; P = 0.0065) significantly and independently influenced tissue stiffness. On axial images, the thresholds maximising the Youden index in TZ, lateral PZ and median PZ were respectively 62 kPa, 33 kPa and 49 kPa. On sagittal images, the thresholds were 76 kPa, 50 kPa and 72 kPa, respectively. CONCLUSIONS: SWE can distinguish prostate malignant and benign tissues. Tissue stiffness is influenced by the imaging plane and the location within the gland. KEY POINTS: • Prostate cancers were stiffer than the benign peripheral zone • All tissue classes were stiffer on sagittal than on axial imaging • All tissue classes were stiffer in the transition zone than in the peripheral zone • All tissue classes were stiffer in the median than in the lateral peripheral zone • Taking into account imaging plane and zonal anatomy can improve cancer detection.

Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study.

Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. (©) RSNA, 2016 Online supplemental material is available for this article.

How accurate is multiparametric MR imaging in evaluation of prostate cancer volume?

PURPOSE: To assess the factors influencing multiparametric (MP) magnetic resonance (MR) imaging accuracy in estimating prostate cancer histologic volume (Vh). MATERIALS AND METHODS: A prospective database of 202 patients who underwent MP MR imaging before radical prostatectomy was retrospectively used. Institutional review board approval and informed consent were obtained. Two independent radiologists delineated areas suspicious for cancer on images (T2-weighted, diffusion-weighted, dynamic contrast material-enhanced [DCE] pulse sequences) and scored their degree of suspicion of malignancy by using a five-level Likert score. One pathologist delineated cancers on whole-mount prostatectomy sections and calculated their volume by using digitized planimetry. Volumes of MR true-positive lesions were measured on T2-weighted images (VT2), on ADC maps (VADC), and on DCE images [VDCE]). VT2, VADC, VDCE and the greatest volume determined on images from any of the individual MR pulse sequences (Vmax) were compared with Vh (Bland-Altman analysis). Factors influencing MP MR imaging accuracy, or A, calculated as A = Vmax/Vh, were evaluated using generalized linear mixed models. RESULTS: For both readers, Vh was significantly underestimated with VT2 (P < .0001, both), VADC (P < .0001, both), and VDCE (P = .02 and P = .003, readers 1 and 2, respectively), but not with Vmax (P = .13 and P = .21, readers 1 and 2, respectively). Mean, 25th percentile, and 75th percentile, respectively, for Vmax accuracy were 0.92, 0.54, and 1.85 for reader 1 and 0.95, 0.57, and 1.77 for reader 2. At generalized linear mixed (multivariate) analysis, tumor Likert score (P < .0001), Gleason score (P = .009), and Vh (P < .0001) significantly influenced Vmax accuracy (both readers). This accuracy was good in tumors with a Gleason score of 7 or higher or a Likert score of 5, with a tendency toward underestimation of Vh; accuracy was poor in small (<0.5 cc) or low-grade (Gleason score ≤6) tumors, with a tendency toward overestimation of Vh. CONCLUSION: Vh can be estimated by using Vmax in aggressive tumors or in tumors with high Likert scores.

Performance of a Region of Interest-based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database-CAD-FIRST Study.

BACKGROUND AND OBJECTIVE: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. METHODS: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. KEY FINDINGS AND LIMITATIONS: After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. CONCLUSIONS AND CLINICAL IMPLICATIONS: The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. PATIENT SUMMARY: An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.

Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study.

OBJECTIVES: To assess factors influencing prostate cancer detection on multiparametric (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced) MRI. METHODS: One hundred and seventy-five patients who underwent radical prostatectomy were included. Pre-operative MRI performed at 1.5 T (n = 71) or 3 T (n = 104), with (n = 58) or without (n = 117) an endorectal coil were independently interpreted by two radiologists. A five-point subjective suspicion score (SSS) was assigned to all focal abnormalities (FAs). MR findings were then compared with whole-mount sections. RESULTS: Readers identified 192-214/362 cancers, with 130-155 false positives. Detection rates for tumours of <0.5 cc (cm(3)), 0.5-2 cc and >2 cc were 33-45/155 (21-29 %), 15-19/35 (43-54 %) and 8-9/12 (67-75 %) for Gleason ≤6, 17/27 (63 %), 42-45/51 (82-88 %) and 34/35 (97 %) for Gleason 7 and 4/5 (80 %), 13/14 (93 %) and 28/28 (100 %) for Gleason ≥8 cancers respectively. At multivariate analysis, detection rates were influenced by tumour Gleason score, histological volume, histological architecture and location (P < 0.0001), but neither by field strength nor coils used for imaging. The SSS was a significant predictor of both malignancy of FAs (P < 0.005) and aggressiveness of tumours (P < 0.00001). CONCLUSIONS: Detection rates were significantly influenced by tumour characteristics, but neither by field strength nor coils used for imaging. The SSS significantly stratified the risk of malignancy of FAs and aggressiveness of detected tumours. KEY POINTS: • Prostate cancer volume, Gleason score, architecture and location are MRI predictors of detection. • Field strength and coils used do not influence the tumour detection rate. • Multiparametric MRI is accurate for detecting aggressive tumours. • A subjective suspicion score can stratify the risk of malignancy and tumour aggressiveness.

Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? - A systematic review.

PURPOSE: The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI). MATERIALS AND METHODS: Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting. RESULTS: Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings. CONCLUSION: Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study).

OBJECTIVE: To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience. METHODS: Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 'predefined' lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored 'additional' lesions if needed. Per-lesion analysis assessed the 'predefined' lesions, using targeted biopsy as reference; per-lobe analysis included 'predefined' and 'additional' lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement. RESULTS: At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60-0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43-0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70-0.79) than experienced seniors (0.80; 95%CI 0.76-0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70-0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6-29), of which 2 (IQR: 1-3) were csPCa; it upgraded 4 lesions/reader (IQR: 2-7), of which 1 (IQR: 0-2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25-73) 'additional' lesions/reader, yielded similar results. CONCLUSIONS: Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.

Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader.

PURPOSE: The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers. MATERIALS AND METHODS: ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions. RESULTS: The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively. CONCLUSION: The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.

Measurement of pulsatile motion with millisecond resolution by MRI.

We investigated a technique based on phase-contrast cine MRI combined with deconvolution of the phase shift waveforms to measure rapidly varying pulsatile motion waveforms. The technique does not require steady-state displacement during motion encoding. Simulations and experiments were performed in porcine liver samples in view of a specific application, namely the observation of transient displacements induced by acoustic radiation force. Simulations illustrate the advantages and shortcomings of the methods. For experimental validation, the waveforms were acquired with an ultrafast ultrasound scanner (Supersonic Imagine Aixplorer), and the rates of decay of the waveforms (relaxation time) were compared. With bipolar motion-encoding gradient of 8.4 ms, the method was able to measure displacement waveforms with a temporal resolution of 1 ms over a time course of 40 ms. Reasonable agreement was found between the rate of decay of the waveforms measured in ultrasound (2.8 ms) and in MRI (2.7-3.3 ms).

Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI.

PURPOSE: The purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ). MATERIALS AND METHODS: Fifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference. RESULTS: Forty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21-37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10-6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0-500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10-6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10-6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10-6 to 311 × 10-6 mm²/s across scanners, b-value combinations and periods of the day. CONCLUSIONS: The MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.

Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study).

INTRODUCTION: Prostate multiparametric MRI (mpMRI) has shown good sensitivity in detecting cancers with an International Society of Urological Pathology (ISUP) grade of ≥2. However, it lacks specificity, and its inter-reader reproducibility remains moderate. Biomarkers, such as the Prostate Health Index (PHI), may help select patients for prostate biopsy. Computer-aided diagnosis/detection (CAD) systems may also improve mpMRI interpretation. Different prototypes of CAD systems are currently developed under the Recherche Hospitalo-Universitaire en Santé / Personalized Focused Ultrasound Surgery of Localized Prostate Cancer (RHU PERFUSE) research programme, tackling challenging issues such as robustness across imaging protocols and magnetic resonance (MR) vendors, and ability to characterise cancer aggressiveness. The study primary objective is to evaluate the non-inferiority of the area under the receiver operating characteristic curve of the final CAD system as compared with the Prostate Imaging-Reporting and Data System V.2.1 (PI-RADS V.2.1) in predicting the presence of ISUP ≥2 prostate cancer in patients undergoing prostate biopsy. METHODS: This prospective, multicentre, non-inferiority trial will include 420 men with suspected prostate cancer, a prostate-specific antigen level of ≤30 ng/mL and a clinical stage ≤T2 c. Included men will undergo prostate mpMRI that will be interpreted using the PI-RADS V.2.1 score. Then, they will undergo systematic and targeted biopsy. PHI will be assessed before biopsy. At the end of patient inclusion, MR images will be assessed by the final version of the CAD system developed under the RHU PERFUSE programme. Key secondary outcomes include the prediction of ISUP grade ≥2 prostate cancer during a 3-year follow-up, and the number of biopsy procedures saved and ISUP grade ≥2 cancers missed by several diagnostic pathways combining PHI and MRI findings. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Comité de Protection des Personnes Nord Ouest III (ID-RCB: 2020-A02785-34). After publication of the results, access to MR images will be possible for testing other CAD systems. TRIAL REGISTRATION NUMBER: NCT04732156.

Magnetic resonance elastography of the kidneys: feasibility and reproducibility in young healthy adults.

PURPOSE: To evaluate the feasibility and reproducibility of renal magnetic resonance elastography (MRE) in young healthy volunteers. MATERIALS AND METHODS: Ten volunteers underwent renal MRE twice at a 4-5 week interval. The vibrations (45 and 76 Hz) were generated by a speaker positioned beneath the volunteers' back and centered on their left kidney. For each frequency, three sagittal slices were acquired (eight phase offsets per cycle, motion-encoding gradients successively positioned along the three directions of space). Shear velocity images were reconstructed using the curl operator combined with the local frequency estimation (LFE) algorithm. RESULTS: The mean shear velocities measured in the renal parenchyma during the two examinations were not significantly different and exhibited a mean variation of 6% at 45 Hz and 76 Hz. The mean shear velocities in renal parenchyma were 2.21 ± 0.14 m/s at 45 Hz (shear modulus of 4.9 ± 0.5 kPa) and 3.07 ± 0.17 m/s at 76 Hz (9.4 ± 0.8 kPa, P < 0.01). The mean shear velocities in the renal cortex and medulla were respectively 2.19 ± 0.13 m/s and 2.32 ± 0.16 m/s at 45 Hz (P = 0.002) and 3.06 ± 0.16 m/s and 3.10 ± 0.22 m/s at 76 Hz (P = 0.13). CONCLUSION: Renal MRE was feasible and reproducible. Two independent measurements of shear velocities in the renal parenchyma of the same subjects showed an average variability of 6%.

Transient MR elastography (t-MRE) using ultrasound radiation force: theory, safety, and initial experiments in vitro.

The purpose of our study was to assess the feasibility of using ultrasound radiation force as a safe vibration source for transient MR elastography (t-MRE). We present a theoretical framework to predict the phase shift of the complex MRE signal, the temperature elevation due to ultrasound, and safety indicators (I(SPPA), I(SPTA), MI). Next, we report wave images acquired in porcine liver samples in vitro. MR thermometry was used to estimate the temperature elevation induced by ultrasound. Finally, we discuss the implications of our results with regard to the feasibility of using radiation force for t-MRE in a clinical setting, and a specific echo-planar imaging (EPI) MRE sequence is proposed.

Transrectal high-intensity focused ultrasound ablation of prostate cancer: effective treatment requiring accurate imaging.

Transrectal HIFU ablation has become a reasonable option for the treatment of localized prostate cancer in non-surgical patients, with 5-year disease-free survival similar to that of radiation therapy. It is also a promising salvage therapy of local recurrence after radiation therapy. These favourable results are partly due to recent improvements in prostate cancer imaging. However, further improvements are needed in patient selection, pre-operative localization of the tumor foci, assessment of the volume treated and early detection of recurrence. A better knowledge of the factors influencing the HIFU-induced tissue destruction and a better pre-operative assessment of them by imaging techniques should improve treatment outcome. Whereas prostate HIFU ablation is currently performed under transrectal ultrasound guidance, MR guidance with real-time operative monitoring of temperature will be available in the near future. If this technique will give better targeting and more uniform tissue destruction, its cost-effectiveness will have to be carefully evaluated. Finally, a recently reported synergistic effect between HIFU ablation and chemotherapy opens possibilities for treatment in high-risk or clinically advanced tumors.

Accuracy of Elastic Fusion of Prostate Magnetic Resonance and Transrectal Ultrasound Images under Routine Conditions: A Prospective Multi-Operator Study.

PURPOSE: To evaluate in unselected patients imaged under routine conditions the co-registration accuracy of elastic fusion between magnetic resonance (MR) and ultrasound (US) images obtained by the Koelis Urostation™. MATERIALS AND METHODS: We prospectively included 15 consecutive patients referred for placement of intraprostatic fiducials before radiotherapy and who gave written informed consent by signing the Institutional Review Board-approved forms. Three fiducials were placed in the prostate under US guidance in standardized positions (right apex, left mid-gland, right base) using the Koelis Urostation™. Patients then underwent prostate MR imaging. Four operators outlined the prostate on MR and US images and an elastic fusion was retrospectively performed. Fiducials were used to measure the overall target registration error (TRE3D), the error along the antero-posterior (TREAP), right-left (TRERL) and head-feet (TREHF) directions, and within the plane orthogonal to the virtual biopsy track (TRE2D). RESULTS: Median TRE3D and TRE2D were 3.8-5.6 mm, and 2.5-3.6 mm, respectively. TRE3D was significantly influenced by the operator (p = 0.013), fiducial location (p = 0.001) and 3D axis orientation (p<0.0001). The worst results were obtained by the least experienced operator. TRE3D was smaller in mid-gland and base than in apex (average difference: -1.21 mm (95% confidence interval (95%CI): -2.03; -0.4) and -1.56 mm (95%CI: -2.44; -0.69) respectively). TREAP and TREHF were larger than TRERL (average difference: +1.29 mm (95%CI: +0.87; +1.71) and +0.59 mm (95%CI: +0.1; +0.95) respectively). CONCLUSIONS: Registration error values were reasonable for clinical practice. The co-registration accuracy was significantly influenced by the operator's experience, and significantly poorer in the antero-posterior direction and at the apex.

Monitoring the formation of thermal lesions with heat-induced echo-strain imaging: a feasibility study.

We investigated the feasibility of using echo-strain images to visualize the extent of high-intensity ultrasound (US)-induced thermal lesions during their formation. Echo-strain, defined as the relative deformation of the backscattered ultrasonic signal, is due to tissue expansion and to changes in the speed of sound during heating. First, a theoretical framework was developed to predict the influence of these effects on the echo signal. Then, a simulation tool was developed to create simulated echo-strain images in thermal lesions. Finally, experimental echo-strain images were acquired in 10 porcine liver samples in vitro for various exposure durations and ultrasonic intensities (resulting in lesions that extended 3 to 8 mm deep from the surface). For this purpose, radiofrequency (RF) frames were acquired at 8 frames per s while heating. For each consecutive pair of RF frames, an echo-strain image was calculated using standard elastographic processing. The echo-strain images were cumulated and displayed. The experimental echo-strain images were compared with gross pathology. The (isoechoic) lesions were visible both in simulated and in experimental cumulated echo-strain images as apparent expansion areas (tensile echo-strain), whereas surrounding tissues exhibited apparent compression. The tensile echo-strain area underestimated the lesion in simulations, but was representative of the lesion in experiments. High correspondence was found between the lesion depth measured from experimental cumulative echo-strain images (y) and from gross pathology (x) (Pearson's correlation = 0.90, linear regression y = x-0.1 mm, residual error = 0 +/- 0.9 mm). We hypothesized that significant tissue expansion made the thermal lesions highly visible in the experimental echo-strain images. In two cases, the ultrasonic intensity was too low to induce a lesion, and the corresponding experimental echo-strain images showed no visible lesion. We conclude that cumulative echo-strain images have the potential to monitor the formation of high-intensity US-induced thermal lesions.

Observation of a cavitation cloud in tissue using correlation between ultrafast ultrasound images.

The local application of ultrasound is known to improve drug intake by tumors. Cavitating bubbles are one of the contributing effects. A setup in which two ultrasound transducers are placed confocally is used to generate cavitation in ex vivo tissue. As the transducers emit a series of short excitation bursts, the evolution of the cavitation activity is monitored using an ultrafast ultrasound imaging system. The frame rate of the system is several thousands of images per second, which provides several tens of images between consecutive excitation bursts. Using the correlation between consecutive images for speckle tracking, a decorrelation of the imaging signal appears due to the creation, fast movement, and dissolution of the bubbles in the cavitation cloud. By analyzing this area of decorrelation, the cavitation cloud can be localized and the spatial extent of the cavitation activity characterized.