Targeted prostate biopsy using statistical image analysis

Abstract-In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies. Index Terms-Biopsy optimization, prostate cancer, spatial normalization, statistical image analysis

The SmartTarget BIOPSY trial: A prospective, within-person randomised, blinded trial comparing the accuracy of visual-registration and MRI/ultrasound image-fusion targeted biopsies for prostate cancer risk stratification

Background: Multiparametric magnetic resonance imaging (mpMRI)-targeted prostate biopsies can improve detection of clinically significant prostate cancer and decrease the overdetection of insignificant cancers. Whether visual-registration targeting is sufficient or if augmentation with image-fusion software is needed is unknown. Objective: To assess concordance between the two methods. Design, Setting, and Participants: We conducted a blinded, within-person randomised, paired validating clinical trial. From 2014 to 2016, 141 men who had undergone a prior (positive or negative) transrectal ultrasound biopsy and had a discrete lesion on mpMRI (score 3 to 5) requiring targeted transperineal biopsy were enrolled at a UK academic hospital; 129 underwent both biopsy strategies and completed the study. Intervention: The order of performing biopsies using visual-registration and a computer-assisted MRI/ultrasound image-fusion system (SmartTarget) on each patient was randomised. The equipment was reset between biopsy strategies to mitigate incorporation bias. Outcome Measurements and Statistical Analysis: The proportion of clinically significant prostate cancer (primary outcome: Gleason pattern ≥3+4=7, maximum cancer core length ≥4 mm; secondary outcome: Gleason pattern ≥4+3=7, maximum cancer core length ≥6 mm) detected by each method was compared using McNemar's test of paired proportions. Results and Limitations: The two strategies combined detected 93 clinically significant prostate cancers (72% of the cohort). Each strategy individually detected 80/93 (86%) of these cancers; each strategy detected 13 cases missed by the other. Three patients experienced adverse events related to biopsy (urinary retention, urinary tract infection, nausea and vomiting). No difference in urinary symptoms, erectile function, or quality of life between baseline and follow-up (median 10.5 weeks) was observed. The key limitation was lack of parallel-group randomisation and limit on number of targeted cores. Conclusions: Visual-registration and image-fusion targeting strategies combined had the highest detection rate for clinically significant cancers. Targeted prostate biopsy should be performed using both strategies together. Patient Summary: We compared two prostate cancer biopsy strategies: visual-registration and image-fusion. The combination of the two strategies found the most clinically important cancers and should be used together whenever targeted biopsy is being performed

Initial validation of a virtual-reality learning environment for prostate biopsies: realism matters!

: Introduction-objectives: A virtual-reality learning environment dedicated to prostate biopsies was designed to overcome the limitations of current classical teaching methods. The aim of this study was to validate reliability, face, content and construct of the simulator. Materials and methods: The simulator is composed of a) a laptop computer, b) a haptic device with a stylus that mimics the ultrasound probe, c) a clinical case database including three dimensional (3D) ultrasound volumes and patient data and d) a learning environment with a set of progressive exercises including a randomized 12-core biopsy procedure. Both visual (3D biopsy mapping) and numerical (score) feedback are given to the user. The simulator evaluation was conducted in an academic urology department on 7 experts and 14 novices who each performed a virtual biopsy procedure and completed a face and content validity questionnaire. Results: The overall realism of the biopsy procedure was rated at a median of 9/10 by non-experts (7.1-9.8). Experts rated the usefulness of the simulator for the initial training of urologists at 8.2/10 (7.9-8.3), but reported the range of motion and force feedback as significantly less realistic than novices (p=0.01 and 0.03 respectively). Pearson's r correlation coefficient between correctly placed biopsies on the right and left side of the prostate for each user was 0.79 (p<0.001). The 7 experts had a median score of 64% (59-73), and the 14 novices a median score of 52% (43-67), without reaching statistical significance (p=0,19). Conclusion: The newly designed virtual reality learning environment proved its versatility and its reliability, face and content were validated. Demonstrating the construct validity will require improvements to the realism and scoring system used

The SmartTarget Biopsy Trial: A Prospective, Within-person Randomised, Blinded Trial Comparing the Accuracy of Visual-registration and Magnetic Resonance Imaging/Ultrasound Image-fusion Targeted Biopsies for Prostate Cancer Risk Stratification

Background: Multiparametric magnetic resonance imaging (mpMRI)-targeted prostate biopsies can improve detection of clinically significant prostate cancer and decrease the overdetection of insignificant cancers. It is unknown whether visual-registration targeting is sufficient or augmentation with image-fusion software is needed. Objective: To assess concordance between the two methods. Design, setting, and participants: We conducted a blinded, within-person randomised, paired validating clinical trial. From 2014 to 2016, 141 men who had undergone a prior (positive or negative) transrectal ultrasound biopsy and had a discrete lesion on mpMRI (score 3–5) requiring targeted transperineal biopsy were enrolled at a UK academic hospital; 129 underwent both biopsy strategies and completed the study. Intervention: The order of performing biopsies using visual registration and a computer-assisted MRI/ultrasound image-fusion system (SmartTarget) on each patient was randomised. The equipment was reset between biopsy strategies to mitigate incorporation bias. Outcome measurements and statistical analysis: The proportion of clinically significant prostate cancer (primary outcome: Gleason pattern ≥3 + 4 = 7, maximum cancer core length ≥4 mm; secondary outcome: Gleason pattern ≥4 + 3 = 7, maximum cancer core length ≥6 mm) detected by each method was compared using McNemar's test of paired proportions. Results and limitations: The two strategies combined detected 93 clinically significant prostate cancers (72% of the cohort). Each strategy detected 80/93 (86%) of these cancers; each strategy identified 13 cases missed by the other. Three patients experienced adverse events related to biopsy (urinary retention, urinary tract infection, nausea, and vomiting). No difference in urinary symptoms, erectile function, or quality of life between baseline and follow-up (median 10.5 wk) was observed. The key limitations were lack of parallel-group randomisation and a limit on the number of targeted cores. Conclusions: Visual-registration and image-fusion targeting strategies combined had the highest detection rate for clinically significant cancers. Targeted prostate biopsy should be performed using both strategies together. Patient summary: We compared two prostate cancer biopsy strategies: visual registration and image fusion. A combination of the two strategies found the most clinically important cancers and should be used together whenever targeted biopsy is being performed. Image-fusion results in a clinically significant prostate cancer detection rate were similar to those of visual registration performed by an experienced operator. Detection could be improved by 14% with no adverse effect on patient safety by adding image fusion to conventional visual-registration targeting

Negative multiparametric magnetic resonance imaging for prostate cancer: what's next?

Multiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csPCa). Nevertheless, the clinical utility of negative mpMRI (nMRI) is less clearMultiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csPCa). Nevertheless, the clinical utility of negative mpMRI (nMRI) is less clear. OBJECTIVE: To assess outcomes of men with nMRI and clinical follow-up after 7 yr of activity at a reference center. DESIGN, SETTING, AND PARTICIPANTS: All mpMRI performed from January 2010 to May 2015 were reviewed. We selected all patients with nMRI and divided them in group A (naïve patients) and group B (previous negative biopsy). All patients without a diagnosis of PCa had a minimum follow-up of 2 yr and at least two consecutive nMRI. Patients with positive mpMRI were also identified to assess their biopsy outcomes. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A Kaplan-Meier analysis was performed to assess both any-grade PCa and csPCa diagnosis-free survival probabilities. Univariable and multivariable Cox regression models were fitted to identify predictors of csPCa diagnosis. RESULTS AND LIMITATIONS: We identified 1545 men with nMRI, and 1255 of them satisfied the inclusion criteria; 659 belonged to group A and 596 to group B. Any-grade PCa and csPCa diagnosis-free survival probabilities after 2 yr of follow-up were 94% and 95%, respectively, in group A; in group B, they were 96%. After 48 mo of follow-up, any-grade PCa diagnosis-free survival probability was 84% in group A and 96% in group B (log rank p&lt;0.001). Diagnosis-free survival probability for csPCa was unchanged after 48 mo of follow-up. On multivariable Cox regression analysis, increasing age (p=0.005) was an independent predictor of lower csPCa diagnosis probability, while increasing prostate-specific antigen (PSA) and PSA density (&lt;0.001) independently predicted higher csPCa diagnosis probability. The prevalence of and positive predictive value for csPCa were 31.6% and 45.5%, respectively. Limitations include limited follow-up and the inability to calculate true csPCa prevalence in the study population. CONCLUSIONS: mpMRI is highly reliable to exclude csPCa. Nevertheless, systematic biopsy should be recommended even after nMRI, especially in younger patients with high or raising PSA levels

The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p &lt; 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P &lt; 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)

Accuracy of multiparametric magnetic resonance imaging to detect significant prostate cancer and index lesion location

Background: Multiparametric magnetic resonance imaging (mpMRI) of the prostate appears to improve prostate cancer detection, but studies comparing mpMRI to histopathology at the time of radical prostatectomy (RP) are lacking. This retrospective study determined the accuracy of mpMRI predicting Gleason score and index lesion location at the time of RP, the current gold standard for diagnosis. Methods: Between April 2013 and April 2016, a database of all men aged more than 40 years who underwent RP after positive transrectal ultrasound biopsy by an experienced urological surgeon was collated at a single regional centre. This was cross‐referenced with a database of all men who had mpMRIs performed at a single centre and reported according to Prostate Imaging Reporting and Data System (PI‐RADS version 1) during this period to generate a sample size of 64 men. A Spearman\u27s rho test was utilized to calculate correlation. Results: Median age of patients was 64 years, the median prostate‐specific antigen at RP was 6.22 ng/mL. mpMRI was positive (≥PI‐RADS 3) in 85.9% of patients who underwent RP. More than 92% of participants had Gleason ≥7 disease. A positive relationship between mpMRI prostate PI‐RADS score and RP cancer volume was demonstrated. An anatomical location correlation calculated in octants was found to be 89.1% accurate. Conclusion: mpMRI accurately detects prostate cancer location and severity when compared with gold standard histopathology at the time of RP. It thus has an important role in planning for future prostate biopsy and cancer treatment

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

Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed