A comparison of prostate tumor targeting strategies using magnetic resonance imaging-targeted, transrectal ultrasound-guided fusion biopsy

Optimizing multiparametric magnetic resonance imaging-targeted biopsy and detection of clinically significant prostate cancer: the role of core number and location

PURPOSE

There is currently no consensus regarding the optimal number of multiparametric magnetic resonance imaging (MRI)-targeted biopsy (TB) cores and their spatial distribution within the MRI lesion. We aim to determine the number of TB cores and location needed to adequately detect csPCa.

METHODS

We conducted a retrospective cohort study of 505 consecutive patients undergoing TB for positive MRI lesions defined by a PI-RADS score ≥ 3 between June 2016 and January 2022. Cores chronology and locations were prospectively recorded. The co-primary outcomes were the first core to detect clinically significant prostate cancer (csPCa) and the first highest ISUP grade group. The incremental benefit of each additional core was evaluated. Analysis was then performed by distinguishing central (cTB) and peripheral (pTB) within the MRI lesion.

RESULTS

Overall, csPCa was detected in 37% of patients. To reach a csPCa detection rate of 95%, a 3-core strategy was required, except for patients with PI-RADS 5 lesions and those with PSA density ≥ 0.2 ng/ml/cc who benefited from a fourth TB core. At multivariable analysis, only a PSA density ≥ 0.2 ng/ml/cc was an independent predictive factor of having the highest ISUP grade group on the fourth TB cores (p = 0.03). No significant difference in the cancer detection rate was found between cTB and pTB (p = 0.9). Omitting pTB would miss 18% of all csPCa.

CONCLUSION

A 3-core strategy should be considered for TB to optimize csPCa detection with additional cores needed for PI-RADS 5 lesions and high PSA density. Biopsy cores from both central and peripheral zones are required.

Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention

PURPOSE

Understanding the three-dimensional anatomy of percutaneous intervention in prostate cancer is essential to avoid complications. Recently, attempts have been made to use machine learning to automate the segmentation of functional structures such as the prostate gland, rectum, and bladder. However, a paucity of material is available to segment extracapsular structures that are known to cause needle deflection during percutaneous interventions. This research aims to explore the feasibility of the automatic segmentation of prostate and extracapsular structures to predict needle deflection.

METHODS

Using pelvic magnetic resonance imagings (MRIs), 3D U-Net was trained and optimized for the prostate and extracapsular structures (bladder, rectum, pubic bone, pelvic diaphragm muscle, bulbospongiosus muscle, bull of the penis, ischiocavernosus muscle, crus of the penis, transverse perineal muscle, obturator internus muscle, and seminal vesicle). The segmentation accuracy was validated by putting intra-procedural MRIs into the 3D U-Net to segment the prostate and extracapsular structures in the image. Then, the segmented structures were used to predict deflected needle path in in-bore MRI-guided biopsy using a model-based approach.

RESULTS

The 3D U-Net yielded Dice scores to parenchymal organs (0.61-0.83), such as prostate, bladder, rectum, bulb of the penis, crus of the penis, but lower in muscle structures (0.03-0.31), except and obturator internus muscle (0.71). The 3D U-Net showed higher Dice scores for functional structures ([Formula: see text]0.001) and complication-related structures ([Formula: see text]0.001). The segmentation of extracapsular anatomies helped to predict the deflected needle path in MRI-guided prostate interventions of the prostate with the accuracy of 0.9 to 4.9 mm.

CONCLUSION

Our segmentation method using 3D U-Net provided an accurate anatomical understanding of the prostate and extracapsular structures. In addition, our method was suitable for segmenting functional and complication-related structures. Finally, 3D images of the prostate and extracapsular structures could simulate the needle pathway to predict needle deflections.

Concurrently bendable and rotatable continuum tubular robot for omnidirectional multi-core transurethral prostate biopsy

A transurethral prostate biopsy device is proposed in this paper, which can shoot a biopsy needle at different angles to take samples from multiple locations within the prostate. Firstly, the traditional prostate biopsy methods, including transrectal prostate biopsy and transperineal prostate biopsy, are introduced and compared. Then, the working principles of the new prostate biopsy procedure are illustrated. The designs of the needle bending system and the flexible needle are presented, and a proofs-of-concept study of the robotic biopsy device is demonstrated. Design parameters, material selection, and control unit are introduced. Experiments are carried out to test and demonstrate the functions of the prototype. Theoretical and measured bending angles are compared and analyzed. The bending system can effectively bend the biopsy needle to any angle between 15 and 45°. The penetration force of the biopsy needle decreases with the increase of the bending angle. The range of rotation of the bending system on one hemisphere is ±25°. Together with the translational motion, the biopsy needle can reach any point within the workspace. Finally, a phantom test and a cadaver experiment were carried out to simulate biopsy.

Magnetic resonance imaging/transrectal ultrasonography fusion guided seed placement in a phantom: Accuracy between 2-seed versus 1-seed strategies

PURPOSE

To investigate whether the 2-seed placement per Magnetic Resonance Imaging (MRI) suspicious lesion yields a higher seed placement accuracy than a 1-seed strategy on a phantom.

METHODS

Eight olives embedded in gelatin, each simulating a prostate, underwent MRI. Three virtual spherical lesions (3, 5, and 8 mm diameters) were marked in each olive on the MRI images and co-registered to the MRI/Transrectal Ultrasonography (TRUS) fusion biopsy system. Two radiologists placed 0.5 mm fiducials, targeting the center of each virtual lesion under fusion image guidance. Half of the 8 olives in each phantom were assigned either to the 1-seed or 2-seeds per lesion strategy. Post-procedure Computed Tomography (CT) images identified each seed and were fused with MR to localize each virtual lesion and collected the seed placement error - distance between the virtual target and the corresponding seed (using the closer seed for the 2-seed strategy). Seed placement success is defined as fiducial placement within a lesion boundary.

RESULTS

Each operator repeated the procedure on three different phantoms, and data from 209 seeds placed for 137 lesions were analyzed, with an overall error of 3.03 ± 1.52 mm. The operator skill, operator phantom procedural experience, lesion size, and number of seeds, were independently associated with the seed placement error. Seed placement success rate was higher for the 2-seed group compared to 1-seed, although the difference was not statistically significant.

CONCLUSIONS

Placing 2 seeds per MRI lesion yielded a significantly lower error compared to 1-seed strategy, although seed placement success rate was not significantly different.

A prospective analysis of robotic targeted MRI-US fusion prostate biopsy using the centroid targeting approach

Robotic prostate biopsy is an emerging technology. Recent development of this tool has allowed the performance of a transperineal prostate biopsy allowing pre-programmed standardized biopsy schemes. Prospective data collection was undertaken in 86 consecutive men who underwent robotically assisted transperineal prostate biopsy. All underwent a multi-parametric MRI pre-biopsy with centroid targeting followed by systematic template prostate biopsy. For the purposes of this study, our definition of clinically significant prostate cancer (csPCa) is any Gleason score > 6. Mean (SD) age, median (IQR) PSA, and median (IQR) prostate volume were 64.24 (6.97) years, of 7.79 ng/ml (6.5) and 45.06 cc (28), respectively. Overall, 44 (51.2%) men were diagnosed with csPCa. csPCa was detected in the targeted biopsies alone in 35 (40.1%) men. The addition of the 12-zone template biopsy increased the yield of csPCa for another 9 (10.5%) men. Of these 9 men, the majority (7) harbored primary pattern 3 disease and only 1 was identified to have high-grade disease. Out of these 9 men, 7 of them had the identification of csPCa in the sector, where a target was contained within that zone. Robotic-assisted prostate biopsy in our study has demonstrated a high detection of csPCa when combined with limited near-field sampling. Our study suggests the use of more accurate biopsy schemes such as ring-targeting of lesions to mitigate against systematic and random mathematical errors. Adoption of this tool and biopsy strategy would potentially avoid the increased morbidity associated with whole gland systematic unguided biopsies.

Quantitative 3T multiparametric MRI of benign and malignant prostatic tissue in patients with and without local recurrent prostate cancer after external-beam radiation therapy

Background

Post‐radiotherapy locally recurrent prostate cancer (PCa) patients are candidates for focal salvage treatment. Multiparametric MRI (mp‐MRI) is attractive for tumor localization. However, radiotherapy‐induced tissue changes complicate image interpretation. To develop focal salvage strategies, accurate tumor localization and distinction from benign tissue is necessary.

Purpose

To quantitatively characterize radio‐recurrent tumor and benign radiation‐induced changes using mp‐MRI, and investigate which sequences optimize the distinction between tumor and benign surroundings.

Study Type

Prospective case–control.

Subjects

Thirty‐three patients with biochemical failure after external‐beam radiotherapy (cases), 35 patients without post‐radiotherapy recurrent disease (controls), and 13 patients with primary PCa (untreated).

Field Strength/Sequences

3T; quantitative mp‐MRI: T₂‐mapping, ADC, and K^trans and k_ep maps.

Assessment

Quantitative image‐analysis of prostatic regions, within and between cases, controls, and untreated patients.

Statistical Tests

Within‐groups: nonparametric Friedman analysis of variance with post‐hoc Wilcoxon signed‐rank tests; between‐groups: Mann–Whitney tests. All with Bonferroni corrections. Generalized linear mixed modeling to ascertain the contribution of each map and location to tumor likelihood.

Results

Benign imaging values were comparable between cases and controls (P = 0.15 for ADC in the central gland up to 0.91 for k_ep in the peripheral zone), both with similarly high peri‐urethral K^trans and k_ep values (min⁻¹) (median [range]: K^trans = 0.22 [0.14–0.43] and 0.22 [0.14–0.36], P = 0.60, k_ep = 0.43 [0.24–0.57] and 0.48 [0.32–0.67], P = 0.05). After radiotherapy, benign central gland values were significantly decreased for all maps (P ≤ 0.001) as well as T₂, K^trans, and k_ep of benign peripheral zone (all with P ≤ 0.002). All imaging maps distinguished recurrent tumor from benign peripheral zone, but only ADC, K^trans, and k_ep were able to distinguish it from benign central gland. Recurrent tumor and peri‐urethral K^trans values were not significantly different (P = 0.81), but k_ep values were (P < 0.001). Combining all quantitative maps and voxel location resulted in an optimal distinction between tumor and benign voxels.

Data Conclusion

Mp‐MRI can distinguish recurrent tumor from benign tissue.

Level of Evidence: 2

Technical Efficacy Stage: 2

J. Magn. Reson. Imaging 2019;50:269–278.