Multiparametric 3D<i>in vivo</i>ultrasound vibroelastography imaging of prostate cancer: Preliminary results

Moradi, Mehdi; Mahdavi, Sara; Nir, Guy; Mohareri, Omid; Koupparis, Anthony; Gagnon, Louis-Olivier; Fazli, Ladan; Casey, Rowan G.; Ischia, Joseph; Jones, Edward C.; Goldenberg, S. Larry; Salcudean, Septimiu E.

doi:10.1118/1.4884226

Cited by 13 publications

(6 citation statements)

References 57 publications

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“…It has been proposed that the acoustic radiation force of the transmit US signal increases the temperature and changes the speed of sound in different tissue types [4]. It has also been suggested that a combination of micro-vibration of acoustic scatters in microstructures and the density of cells play a role [21]. Our results showed a consistently high classification accuracy in a large dataset in this paper.…”

Section: Discussionsupporting

confidence: 69%

“…Several methods that enable patient-specific targeting have been proposed to improve the detection rate of PCa. Ultrasound (US)-based tissue typing techniques for characterization of PCa include analysis of single radio-frequency (RF) US frame data [8,22,27], elastography [18,21,24] and Doppler imaging [23,30]. A shortcoming of these methods that have limited their clinical uptake is the challenge of identifying a globally effective, tissue-associated threshold that can reliably identify cancerous tissue in the analyzed images, and be ubiquitously generalized to prospective patient data.…”

Section: Introductionmentioning

confidence: 99%

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Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

et al. 2016

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Using temporal ultrasound data in a fusion prostate biopsy study, we achieved a high classification accuracy specifically for moderately scored mp-MRI targets. These targets are clinically common and contribute to the high false-positive rates associated with mp-MRI for prostate cancer detection. Temporal ultrasound data combined with mp-MRI have the potential to reduce the number of unnecessary biopsies in fusion biopsy settings.

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Section: Discussionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

et al. 2016

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“…As a result, conventional systematic biopsy under TRUS guidance has rather poor sensitivity, with positive predictive values between 40–60%[5,23]. In order to enable patient-specific targeting, other modes of ultrasound imaging [18], such as radio frequency (RF) data analysis[3], elastography [13,17,19,20], and Doppler imaging[24,28] have been explored. These technologies, individually, have not entirely succeeded in accurate identification of PCa.…”

Section: Introductionmentioning

confidence: 99%

Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

et al. 2015

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Purpose In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound (TRUS) guided biopsy has enabled targeted prostate biopsy with improved cancer yield. Target identification is solely based on information from mp-MRI, which is subsequently transferred to the subject coordinates through an image registration approach. mp-MRI has shown to be highly sensitive to detect higher grade prostate cancer, but suffers from a high rate of false positives for lower grade cancer, leading to unnecessary biopsies. This paper utilizes a machine-learning framework to further improve the sensitivity of targeted biopsy through analyzing temporal ultrasound data backscattered from the prostate tissue. Methods Temporal ultrasound data was acquired during targeted fusion prostate biopsy from suspicious cancer foci identified in mp-MRI. Several spectral features, representing the signature of backscattered signal from the tissue, were extracted from the temporal ultrasound data. A supervised support vector machine classification model was trained to relate the features to the result of histopathology analysis of biopsy cores obtained from cancer foci. The model was used to predict the label of biopsy cores for mp-MRI identified targets in an independent group of subjects. Results Training of the classier was performed on data obtained from 35 biopsy cores. A five-fold cross-validation strategy was utilized to examine the consistency of the selected features from temporal ultrasound data, where we achieved the classification accuracy and area under receiver operating characteristic curve of 94% and 0.98, respectively. Subsequently, an independent group of 25 biopsy cores was used for validation of the model, in which mp-MRI had identified suspicious cancer foci. Using the trained model, we predicted the tissue pathology using temporal ultrasound data: 16 out of 17 benign cores, as well as all three higher grade cancer cores, were correctly identified. Conclusion The results show that temporal analysis of ultrasound data is potentially an effective approach to complement mp-MRI-TRUS-guided prostate cancer biopsy, specially to reduce the number of unnecessary biopsies and to reliably identify higher grade cancers.

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“…Acoustic radiation force impulse imaging is one of several novel ultrasonic imaging modalities being investigated to improve on the poor performance of B-mode ultrasound imaging in delineating clinically significant PCa. A recent study by Moradi et al (2014) using 3-D vibro-elastography and vector machine classification methods to identify PCa lesions based on image texture (e.g., contrast, homogeneity, standard deviation) yielded an area under the receiver operating characteristic curve of 0.81 ± 0.1 ( Moradi et al 2014 ). Shear wave elasticity imaging (SWEI) has also been studied in the prostate, with one study achieving a positive predictive value of 69.4% using an absolute shear modulus threshold of 37 kPa ( Barr et al 2012 ).…”

Section: Discussionmentioning

confidence: 99%

“…The IOS scores ( Table 2 ) used to quantify the ARFI image reads could be used in more advanced machine learning methods in combination with B-mode ultrasound, multiparametric MRI and other imaging and clinical metrics, as has been done with vibro-elastography ( Moradi et al 2014 ) and quantitative tissue-type imaging (TTI) ( Feleppa et al 2004 ), but that is beyond the scope of this work. Additionally, although the ARFI images in this study were subjectively scored, the images could also be more quantitatively evaluated based on the normalized displacement amplitudes.…”

Section: Discussionmentioning

confidence: 99%

Identifying Clinically Significant Prostate Cancers using 3-D In Vivo Acoustic Radiation Force Impulse Imaging with Whole-Mount Histology Validation

Palmeri

Glass

Miller

et al. 2016

Ultrasound in Medicine & Biology

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Overly aggressive prostate cancer (PCa) treatment adversely affects patients and places an unnecessary burden on our health care system. The inability to identify and grade clinically significant PCa lesions is a factor contributing to excessively aggressive PCa treatment, such as radical prostatectomy, instead of more focal, prostate-sparing procedures such as cryotherapy and high-dose radiation therapy. We have performed 3-D in vivo B-mode and acoustic radiation force impulse (ARFI) imaging using a mechanically rotated, side-fire endorectal imaging array to identify regions suspicious for PCa in 29 patients being treated with radical prostatectomies for biopsy-confirmed PCa. Whole-mount histopathology analyses were performed to identify regions of clinically significant/insignificant PCa lesions, atrophy and benign prostatic hyperplasia. Regions of suspicion for PCa were reader-identified in ARFI images based on boundary delineation, contrast, texture and location. These regions of suspicion were compared with histopathology identified lesions using a nearest-neighbor regional localization approach. Of all clinically significant lesions identified on histopathology, 71.4% were also identified using ARFI imaging, including 79.3% of posterior and 33.3% of anterior lesions. Among the ARFI-identified lesions, 79.3% corresponded to clinically significant PCa lesions, with these lesions having higher indices of suspicion than clinically insignificant PCa. ARFI imaging had greater sensitivity for posterior versus anterior lesions because of greater displacement signal-to-noise ratio and finer spatial sampling. Atrophy and benign prostatic hyperplasia can cause appreciable prostate anatomy distortion and heterogeneity that confounds ARFI PCa lesion identification; however, in general, ARFI regions of suspicion did not coincide with these benign pathologies.

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Multiparametric 3Din vivoultrasound vibroelastography imaging of prostate cancer: Preliminary results

Abstract: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method.

Cited by 13 publications

References 57 publications

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

Identifying Clinically Significant Prostate Cancers using 3-D In Vivo Acoustic Radiation Force Impulse Imaging with Whole-Mount Histology Validation

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