Variability in MRI vs. ultrasound measures of prostate volume and its impact on treatment recommendations for favorable-risk prostate cancer patients: a case series

Murciano‐Goroff, Yonina R.; Wolfsberger, Luciant D.; Parekh, Arti; Fennessy, Fiona M.; Tuncalı, Kemal; Orio, Peter F.; Niedermayr, Thomas; Suh, W. Warren; Devlin, Phillip M.; Tempany, Clare M.; Sugar, Emily H. Neubauer; O’Farrell, Desmond A.; Steele, Graeme S.; O’Leary, Michael P.; Buzurović, Ivan; Damato, Antonio L.; Cormack, Robert A.; Fedorov, Andriy; Nguyen, Paul L.

doi:10.1186/1748-717x-9-200

Cited by 12 publications

(17 citation statements)

References 26 publications

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“…Our evaluation was complicated by the possible inconsistencies in the segmentation of the prostate gland, and the difficulties in placement of some of the anatomical landmarks that resulted in UA/UB points being located outside the prostate gland. Accurate and consistent segmentation of the prostate is challenging in TRUS, especially at the apex and base of the gland [11,23]. We note that the differences between the prostate volumes estimated from 3D TRUS and MR have been recognized earlier in a number of studies.…”

Section: Discussionmentioning

confidence: 99%

Open-source image registration for MRI–TRUS fusion-guided prostate interventions

et al. 2015

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Purpose We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI–TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. Methods The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. Results The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. Conclusions We release open-source tools that may be used for registration during MRI–TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.

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Section: Discussionmentioning

confidence: 99%

Open-source image registration for MRI–TRUS fusion-guided prostate interventions

et al. 2015

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“…By adding 3D ultrasound intraoperatively, improved spatial context and targeting can be achieved to further improve the conventional clinical workflow of biopsy and brachytherapy procedures; however, both procedures rely on accurate segmentation of the prostate in 3D TRUS images to perform necessary clinical tasks. This includes surface-based registration approaches with MRI 2 and subsequent 3D TRUS images, 3 glandular volume measurements, 4,5 and dose-volume calculations, 6 especially when using a commercial guidance system. These segmentations are predominantly performed manually during the procedure, which is time-consuming, variable, and often difficult, which can lead to increased patient risk due to increased anesthesia exposure.…”

Section: Introductionmentioning

confidence: 99%

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images

et al. 2020

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Purpose: Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3D) transrectal ultrasound (TRUS) imaging to improve needle guidance. Using these images effectively typically requires the physician to manually segment the prostate to define the margins used for accurate registration, targeting, and other guidance techniques. However, manual prostate segmentation is a time-consuming and difficult intraoperative process, often occurring while the patient is under sedation (biopsy) or anesthetic (brachytherapy). Minimizing procedure time with a 3D TRUS prostate segmentation method could provide physicians with a quick and accurate prostate segmentation, and allow for an efficient workflow with improved patient throughput to enable faster patient access to care. The purpose of this study was to develop a supervised deep learning-based method to segment the prostate in 3D TRUS images from different facilities, generated using multiple acquisition methods and commercial ultrasound machine models to create a generalizable algorithm for needle-based prostate cancer procedures. Methods: Our proposed method for 3D segmentation involved prediction on two-dimensional (2D) slices sampled radially around the approximate central axis of the prostate, followed by reconstruction into a 3D surface. A 2D U-Net was modified, trained, and validated using images from 84 endfire and 122 side-fire 3D TRUS images acquired during clinical biopsies and brachytherapy procedures. Modifications to the expansion section of the standard U-Net included the addition of 50% dropouts and the use of transpose convolutions instead of standard upsampling followed by convolution to reduce overfitting and improve performance, respectively. Manual contours provided the annotations needed for the training, validation, and testing datasets, with the testing dataset consisting of 20 end-fire and 20 side-fire unseen 3D TRUS images. Since predicting with 2D images has the potential to lose spatial and structural information, comparisons to 3D reconstruction and optimized 3D networks including 3D V-Net, Dense V-Net, and High-resolution 3D-Net were performed following an investigation into different loss functions. An extended selection of absolute and signed error metrics were computed, including pixel map comparisons [dice similarity coefficient (DSC), recall, and precision], volume percent differences (VPD), mean surface distance (MSD), and Hausdorff distance (HD), to assess 3D segmentation accuracy. Results: Overall, our proposed reconstructed modified U-Net performed with a median [first quartile, third quartile] absolute DSC, recall, precision, VPD, MSD, and HD of 94.1 [92.6, 94.9]%, 96.0 [93.1, 98.5]%, 93.2 [88.8, 95.4]%, 5.78 [2.49, 11.50]%, 0.89 [0.73, 1.09] mm, and 2.89 [2.37, 4.35] mm, respectively. When compared to the best-performing optimized 3D network (i.e., 3D V-Net with a Dice plus cross-entropy loss function), our proposed method performed with a significant ...

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“…The gold standard was either histopathological confirmation of PCa or, as in the two papers evaluating the reliability of prostate volume calculations with TRUS, MRI. 31,37 An overview of the study outcomes including the sensitivity and specificity of each imaging mode, where given, is documented in Table 5. Of the 12 articles, reliable sensitivity and specificity data could be extracted in only 6.…”

Section: Sensitivity and Specificitymentioning

confidence: 99%

The role of diagnostic ultrasound imaging for patients with known prostate cancer within an active surveillance pathway: A systematic review

et al. 2021

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Background The use of multiparametric magnetic resonance imaging (mpMRI) within active surveillance of prostate cancer programmes is identified by the UK National Institute for Health and Care Excellence (NICE guideline NG 131 2019) as having a role for monitoring disease. The widespread demands on mpMRI capacity may limit its use in surveillance. It is therefore timely to review the options that modern ultrasound imaging present to this cohort of patients in the monitoring of prostate cancer. Methods Between April and September 2020, 10 databases were searched to recruit studies for the review. Three reviewers evaluated the publications for inclusion. Characteristics including the inclusion criteria for the study cohort, how disease was determined, identification of disease progression, and the modality and mode of imaging used were reviewed. Given the paucity of full text articles, a meta-analysis was not possible. A narrative review was undertaken. Results In total, 12 studies, utilising the range of ultrasound parameters of B-mode, micro-ultrasound, colour Doppler, contrast ultrasound and elastography were included. The review demonstrated that micro-ultrasound offers promise as an imaging tool comparable with mpMRI. However, this is an emerging technology with limited availability. Analysis of the data further demonstrated that by combining the diagnostic features provided by multiple modes reviewed, ultrasound has a role in the diagnostic imaging of patients on active surveillance. Conclusion Providing a multiparametric approach is utilised, stable ultrasound findings may allow for increased intervals between biopsy for men on surveillance. The advent of micro-US offers promise as an imaging modality within an active surveillance pathway but requires further verification.

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Variability in MRI vs. ultrasound measures of prostate volume and its impact on treatment recommendations for favorable-risk prostate cancer patients: a case series

Cited by 12 publications

References 26 publications

Open-source image registration for MRI–TRUS fusion-guided prostate interventions

Open-source image registration for MRI–TRUS fusion-guided prostate interventions

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images

The role of diagnostic ultrasound imaging for patients with known prostate cancer within an active surveillance pathway: A systematic review

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