Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology

Wildeboer, Rogier R.; Sloun, Ruud J. G. van; Postema, Arnoud W.; Mannaerts, Christophe K.; Gayet, Maudy; Beerlage, Harrie P.; Wijkstra, Hessel; Mischi, Massimo

doi:10.1007/s40477-018-0311-8

Cited by 20 publications

(16 citation statements)

References 84 publications

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“…In these studies, the comparison is performed in 3-D reconstructed volumes. This process results in substantial spatial errors of a few millimetres (Wildeboer et al 2018). Consequently, no direct quantitative comparison between the vascular structure of the CL and the ultrasound video data was performed here.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of Vascular Characteristics Using Contrast-Enhanced Ultrasound Imaging

Butler

Perperidis

Zahra

et al. 2019

Ultrasound in Medicine & Biology

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Ultrasound contrast imaging has been used to assess tumour growth and regression by assessing the flow through the macro-and micro-vasculature. Our aim was to differentiate the blood kinetics of vessels such as veins, arteries and microvasculature within the limits of the spatial resolution of contrast-enhanced ultrasound imaging. The highly vascularised ovine ovary was used as a biological model. Perfusion of the ovary with Sono-Vue was recorded with a Philips iU22 scanner in contrast imaging mode. One ewe was treated with prostaglandin to induce vascular regression. Time-intensity curves (TIC) for different regions of interest were obtained, a lognormal model was fitted and flow parameters calculated. Parametric maps of the whole imaging plane were generated for 2 £ 2 pixel regions of interest. Further analysis of TICs from selected locations helped specify parameters associated with differentiation into four categories of vessels (arteries, veins, medium-sized vessels and micro-vessels). Time-dependent parameters were associated with large veins, whereas intensity-dependent parameters were associated with large arteries. Further development may enable automation of the technique as an efficient way of monitoring vessel distributions in a clinical setting using currently available scanners.

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Section: Discussionmentioning

confidence: 99%

Differentiation of Vascular Characteristics Using Contrast-Enhanced Ultrasound Imaging

Butler

Perperidis

Zahra

et al. 2019

Ultrasound in Medicine & Biology

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“…Although, in comparison with the prostate imaging reporting and data system version 2 (PI-RADSv2), this is a limited number of regions, we assume that this approach is the best approximation for US and pathology matching as more ROIs per template would increase the risk of registration errors. Besides the well-known errors in the registration procedure of ultrasound imaging and pathology caused by gland deformation, fixation-related shrinkage and a mismatch in US imaging and pathology orientation, the intraoperative frozen section examination have to be taken into account in our study [ 42 , 43 ]. To assess the influence of the registration between imaging and histopathology on the final results, separate analyses including and excluding ROIs with small tumors (with respect to the ROIs) and inconsistencies across multiple ROIs are foreseen.…”

Section: Discussionmentioning

confidence: 99%

Multiparametric ultrasound: evaluation of greyscale, shear wave elastography and contrast-enhanced ultrasound for prostate cancer detection and localization in correlation to radical prostatectomy specimens

et al. 2018

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BackgroundThe diagnostic pathway for prostate cancer (PCa) is advancing towards an imaging-driven approach. Multiparametric magnetic resonance imaging, although increasingly used, has not shown sufficient accuracy to replace biopsy for now. The introduction of new ultrasound (US) modalities, such as quantitative contrast-enhanced US (CEUS) and shear wave elastography (SWE), shows promise but is not evidenced by sufficient high quality studies, especially for the combination of different US modalities. The primary objective of this study is to determine the individual and complementary diagnostic performance of greyscale US (GS), SWE, CEUS and their combination, multiparametric ultrasound (mpUS), for the detection and localization of PCa by comparison with corresponding histopathology.Methods/designIn this prospective clinical trial, US imaging consisting of GS, SWE and CEUS with quantitative mapping on 3 prostate imaging planes (base, mid and apex) will be performed in 50 patients with biopsy-proven PCa before planned radical prostatectomy using a clinical ultrasound scanner. All US imaging will be evaluated by US readers, scoring the four quadrants of each imaging plane for the likelihood of significant PCa based on a 1 to 5 Likert Scale. Following resection, PCa tumour foci will be identified, graded and attributed to the imaging-derived quadrants in each prostate plane for all prostatectomy specimens. Primary outcome measure will be the sensitivity, specificity, negative predictive value and positive predictive value of each US modality and mpUS to detect and localize significant PCa evaluated for different Likert Scale thresholds using receiver operating characteristics curve analyses.DiscussionIn the evaluation of new PCa imaging modalities, a structured comparison with gold standard radical prostatectomy specimens is essential as first step. This trial is the first to combine the most promising ultrasound modalities into mpUS. It complies with the IDEAL stage 2b recommendations and will be an important step towards the evaluation of mpUS as a possible option for accurate detection and localization of PCa.Trial registrationThe study protocol for multiparametric ultrasound was prospectively registered on Clinicaltrials.gov on 14 March 2017 with the registry name ‘Multiparametric Ultrasound-Study for the Detection of Prostate Cancer’ and trial registration number NCT03091231

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“…The annotated PCa regions were used to reconstruct a 3D model of the prostate and its lesions [26]. This model was subsequently digitally cross-sectioned at the apex, mid gland, and base to be matched to the imaging planes, allowing for direct US histopathology comparison [27]. Taking into account registration inaccuracy, a maximum of one unambiguously malignant and one unambiguously benign region of interest (ROI) were delineated in the B-mode image to serve as labeled ground truth for training and validation.…”

Section: Histopathological Examinationmentioning

confidence: 99%

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

et al. 2019

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Objectives The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound. Methods This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation. Results The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored. Conclusions In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa. Key Points • Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

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Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology

Cited by 20 publications

References 84 publications

Differentiation of Vascular Characteristics Using Contrast-Enhanced Ultrasound Imaging

Differentiation of Vascular Characteristics Using Contrast-Enhanced Ultrasound Imaging

Multiparametric ultrasound: evaluation of greyscale, shear wave elastography and contrast-enhanced ultrasound for prostate cancer detection and localization in correlation to radical prostatectomy specimens

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

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