Adaptation of a 3D prostate cancer atlas for transrectal ultrasound guided target-specific biopsy

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data

BACKGROUND

The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms.

METHODOLOGY

500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data.

RESULTS

The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards.

CONCLUSIONS

The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review

BACKGROUND

Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field-in particular, image classification. The task of image classification became much easier with machine learning (ML) and subsequently got automated and more accurate by using deep learning (DL). By default, DL consists of a single architecture and is termed solo deep learning (SDL). When two or more DL architectures are fused, the result is termed a hybrid deep learning (HDL) model. The use of HDL models is becoming popular in several applications, but no review of these uses has been designed thus far. Therefore, this study provides the first narrative HDL review by considering all facets of image classification using AI.

APPROACH

Our review employs a PRISMA search strategy using Google Scholar, PubMed, IEEE, and Elsevier Science Direct, through which 127 relevant HDL studies were considered. Based on the computer vision evolution, HDLs were subsequently classified into three categories (spatial, temporal, and spatial-temporal). Each study was then analyzed based on several attributes, including continent, publisher, hybridization of two DL or ML, architecture layout, application type, data set type, dataset size, feature extraction methodology, connecting classifier, performance evaluation metrics, and risk-of-bias.

CONCLUSION

The HDL models have shown stable and superior performance by taking the best aspects of two or more solo DL or fusion of DL with ML models. Our findings indicate that HDL is being applied aggressively to several medical and non-medical applications. Furthermore, risk-of-bias is highly debatable for DL and HDL models.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.

State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound

BACKGROUND AND OBJECTIVE

Accurate, reliable, efficient, and precise measurements of the lumen geometry of the common carotid artery (CCA) are important for (a) managing the progression/regression of atherosclerotic build-up and (b) the risk of stroke. The image-based degree of stenosis in the carotid artery and the plaque burden can be predicted using the automated carotid lumen diameter (LD)/inter-adventitial diameter (IAD) measurements from B-mode ultrasound images. The objective of this review is to present the state-of-the-art methods and systems for the measurement of LD/IAD in CCA based on automated or semi-automated strategies. Further, the performance of these systems is compared based on various metrics for its measurements.

METHODS

The automated algorithms proposed for the segmentation of carotid lumen are broadly classified into two different categories as: region-based and boundary-based. These techniques are discussed in detail specifying their pros and cons. Further, we discuss the challenges encountered in the segmentation process along with its quantitative assessment. Lastly, we present stenosis quantification and risk stratification strategies.

RESULTS

Even though, we have found more boundary-based approaches compared to region-based approaches in the literature, however, the region-based strategy yield more satisfactory performance. Novel risk stratification strategies are presented. On a patient database containing 203 patients, 9 patients are identified as high risk patients, whereas 27 patients are identified as medium risk patients.

CONCLUSIONS

We have presented different techniques for the lumen segmentation of the common carotid artery from B-mode ultrasound images and measurement of lumen diameter and inter-adventitial diameter. We believe that the issue regarding boundary-based techniques can be compensated by taking regional statistics embedded with boundary-based information.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate

PURPOSE

We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.

MATERIALS AND METHODS

In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space.

RESULTS

Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate.

CONCLUSION

We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

Analysis of topographical distribution of prostate cancer and related pathological findings in prostatectomy specimens using cMDX document architecture

INTRODUCTION

Understanding the topographical distribution of prostate cancer (PCa) foci is necessary to optimize the biopsy strategy. This study was done to develop a technical approach that facilitates the analysis of the topographical distribution of PCa foci and related pathological findings (i.e., Gleason score and foci dimensions) in prostatectomy specimens.

MATERIAL & METHODS

The topographical distribution of PCa foci and related pathologic evaluations were documented using the cMDX documentation system. The project was performed in three steps. First, we analyzed the document architecture of cMDX, including textual and graphical information. Second, we developed a data model supporting the topographic analysis of PCa foci and related pathologic parameters. Finally, we retrospectively evaluated the analysis model in 168 consecutive prostatectomy specimens of men diagnosed with PCa who underwent total prostate removal. The distribution of PCa foci were analyzed and visualized in a heat map. The color depth of the heat map was reduced to 6 colors representing the PCa foci frequencies, using an image posterization effect. We randomly defined 9 regions in which the frequency of PCa foci and related pathologic findings were estimated.

RESULTS

Evaluation of the spatial distribution of tumor foci according to Gleason score was enabled by using a filter function for the score, as defined by the user. PCa foci with Gleason score (Gls) 6 were identified in 67.3% of the patients, of which 55 (48.2%) also had PCa foci with Gls between 7 and 10. Of 1173 PCa foci, 557 had Gls 6, whereas 616 PCa foci had Gls>6. PCa foci with Gls 6 were mostly concentrated in the posterior part of the peripheral zone of the prostate, whereas PCa foci with Gls>6 extended toward the basal and anterior parts of the prostate. The mean size of PCa foci with Gls 6 was significantly lower than that of PCa with Gls>6 (P<0.0001).

CONCLUSION

The cMDX-based technical approach facilitates analysis of the topographical distribution of PCa foci and related pathologic findings in prostatectomy specimens.

Prostatome: a combined anatomical and disease based MRI atlas of the prostate

PURPOSE

In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap.

METHODS

The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping thein vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides "ground truth" mapping of cancer extent on in vivo imaging for 23 subjects.

RESULTS

AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90%, and prostate DSC of 88%, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage.

CONCLUSIONS

The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists. Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics studies.

Creation of 3-dimensional prostate cancer maps: methodology and clinical and research implications

CONTEXT

The creation of 3-dimensional prostate cancer maps could assist with surgical intervention, radiotherapy treatment planning and for correlative pathology-imaging research.

OBJECTIVES

To develop methodology for creating detailed, 3-dimensional, prostate cancer maps (3DPCM) of tumor location, extra prostatic extension sites, and positive margins and to assess the adequacy of current clinical target volumes for postoperative radiotherapy to the prostate using 3DPCM coregistered with preoperative magnetic resonance imaging.

DESIGN

Parallel slices of prostatectomy specimens were created with ProCUT, and 2-dimensional cancer maps were generated as line diagrams after microscopic examination of each slice. The 2-dimensional cancer maps were aligned and stacked to create a 3DPCM, which was coregistered with the preoperative magnetic resonance imaging scan. The map was exported to the radiotherapy planning system and was used to determine the areas at greater risk, which were then compared against the current Radiation Therapy Oncology Group guidelines for contouring postoperative clinical target volumes to assess the adequacy of coverage.

RESULTS

Twenty-eight patients with a mean age of 66 years (range, 52-73) underwent radical prostatectomy and postoperative radiotherapy. Seventeen patients (61%) received adjuvant radiotherapy for pT3 disease and/or positive margins, and the rest underwent salvage radiotherapy. Thirty-nine percent (11 of 28) of the patients had Gleason scores of 8 or 9. The contours based on the Radiation Therapy Oncology Group guidelines for postoperative radiotherapy resulted in inadequate coverage of extraprostatic extensions in 79% (22 of 28) and positive margins in 64% (18 of 28) of the cases.

CONCLUSIONS

We have developed a methodology for creation of 3DPCM. Modification of the radiotherapy contours, based on the 3DPCM coregistered with pretreatment magnetic resonance imaging, covers the areas at high risk of recurrence. The 3DPCM could become an important clinical and research tool for urologists, pathologists, radiologists, and oncologists.

Imaging and intervention in prostate cancer: Current perspectives and future trends

Prostate cancer is the commonest malignancy in men that causes significant morbidity and mortality worldwide. Screening by digital rectal examination (DRE) and serum prostate-specific antigen (PSA) is used despite its limitations. Gray-scale transrectal ultrasound (TRUS), used to guide multiple random prostatic biopsies, misses up to 20% cancers and frequently underestimates the grade of malignancy. Increasing the number of biopsy cores marginally increases the yield. Evolving techniques of real-time ultrasound elastography (RTE) and contrast-enhanced ultrasound (CEUS) are being investigated to better detect and improve the yield by allowing “targeted” biopsies. Last decade has witnessed rapid developments in magnetic resonance imaging (MRI) for improved management of prostate cancer. In addition to the anatomical information, it is capable of providing functional information through diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic contrast-enhanced (DCE) MRI. Multi-parametric MRI has the potential to exclude a significant cancer in majority of cases. Inclusion of MRI before prostatic biopsy can reduce the invasiveness of the procedure by limiting the number of cores needed to make a diagnosis and support watchful waiting in others. It is made possible by targeted biopsies as opposed to random. With the availability of minimally invasive therapeutic modalities like high-intensity focused ultrasound (HIFU) and interstitial laser therapy, detecting early cancer is even more relevant today. [18F]--fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸FDG PET/CT) has no role in the initial evaluation of prostate cancer. Choline PET has been recently found to be more useful. Fluoride-PET has a higher sensitivity and resolution than a conventional radionuclide bone scan in detecting skeletal metastases.

Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.

Fast GPU based adaptive filtering of 4D echocardiography

Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.

Development and validation of a virtual reality transrectal ultrasound guided prostatic biopsy simulator

OBJECTIVE

We present the design, reliability, face, content and construct validity testing of a virtual reality simulator for transrectal ultrasound (TRUS), which allows doctors-in-training to perform multiple different biopsy schemes.

METHODS

This biopsy system design uses a regular "end-firing" TRUS probe. Movements of the probe are tracked with a micro-magnetic sensor to dynamically slice through a phantom patient's 3D prostate volume to provide real-time continuous TRUS views. 3D TRUS scans during prostate biopsy clinics were recorded. Intrinsic reliability was assessed by comparing the left side of the prostate to the right side of the prostate for each biopsy. A content and face validity questionnaire was administered to 26 doctors to assess the simulator. Construct validity was assessed by comparing notes from experts and novices with regards to the time taken and the accuracy of each biopsy.

RESULTS

Imaging data from 50 patients were integrated into the simulator. The completed VR TRUS simulator uses real patient images, and is able to provide simulation for 50 cases, with a haptic interface that uses a standard TRUS probe and biopsy needle. Intrinsic reliability was successfully demonstrated by comparing results from the left and right sides of the prostate. Face and content validity respondents noted the realism of the simulator, and its appropriateness as a teaching model. The simulator was able to distinguish between experts and novices during construct validity testing.

CONCLUSIONS

A virtual reality TRUS simulator has successfully been created. It has promising face, content and construct validity results.

Tandem-robot assisted laparoscopic radical prostatectomy to improve the neurovascular bundle visualization: a feasibility study

OBJECTIVES

To examine the feasibility of image-guided navigation using transrectal ultrasound (TRUS) to visualize the neurovascular bundle (NVB) during robot-assisted laparoscopic radical prostatectomy (RALP). The preservation of the NVB during radical prostatectomy improves the postoperative recovery of sexual potency. The accompanying blood vessels in the NVB can serve as a macroscopic landmark to localize the microscopic cavernous nerves in the NVB.

METHODS

A novel, robotic transrectal ultrasound probe manipulator (TRUS Robot) and three-dimensional (3-D) reconstruction software were developed and used concurrently with the daVinci surgical robot (Intuitive Surgical, Inc., Sunnyvale, CA) in a tandem-robot assisted laparoscopic radical prostatectomy (T-RALP).

RESULTS

After appropriate approval and informed consent were obtained, 3 subjects underwent T-RALP without associated complications. The TRUS Robot allowed a steady handling and remote manipulation of the TRUS probe during T-RALP. It also tracked the TRUS probe position accurately and allowed 3-D image reconstruction of the prostate and surrounding structures. Image navigation was performed by observing the tips of the daVinci surgical instruments in the live TRUS image. Blood vessels in the NVB were visualized using Doppler ultrasound.

CONCLUSIONS

Intraoperative 3-D image-guided navigation in T-RALP is feasible. The use of TRUS during radical prostatectomy can potentially improve the visualization and preservation of the NVB. Further studies are needed to assess the clinical benefit of T-RALP.

Prostate biopsy in selecting candidates for hemiablative focal therapy

Focal therapy (FT) for the management of clinically localized prostate cancer (PCa) is growing from a concept to reality because of increased interest of both patients and physicians. Selection protocols, however, are yet to be established. We discuss the role of prostate biopsy in candidate selection for FT and highlight the different strategies and technical aspects of the use of prostate biopsy in this setting. In our opinion, prostate biopsy plays a major role in the selection process and tailoring appropriate treatment strategy to the patient. FT necessitates dedicated biopsy schemes that would reliably predict the extent, nature, and location of PCa in selected patients. Currently, there is insufficient scientific evidence to propose a specific biopsy scheme that could fit every candidate, providing accurate characterization of the disease in the individual patient. Further research is necessary to establish solid selection protocols that would reliably identify appropriate candidates for FT of PCa.

Ultrasound image segmentation and tissue characterization

Ultrasound image segmentation deals with delineating the boundaries of structures, as a step towards semi-automated or fully automated measurement of dimensions or for characterizing tissue regions. Ultrasound tissue characterization (UTC) is driven by knowledge of the physics of ultrasound and its interactions with biological tissue, and has traditionally used signal modelling and analysis to characterize and differentiate between healthy and diseased tissue. Thus, both aim to enhance the capabilities of ultrasound as a quantitative tool in clinical medicine, and the two end goals can be the same, namely to characterize the health of tissue. This article reviews both research topics, and finds that the two fields are becoming more tightly coupled, even though there are key challenges to overcome in each area, influenced by factors such as more open software-based ultrasound system architectures, increased computational power, and advances in imaging transducer design.

Utility of a 3-Dimensional Transrectal Ultrasound-guided Prostate Biopsy System for Prostate Cancer Detection

The 3-D transrectal ultrasound (TRUS)-guided prostate biopsy system is a novel device that allows precise needle placement in a template fashion. We evaluate its utility for prostate cancer (PCa) detection. A retrospective analysis was performed evaluating 68 prospective patients at the Duke Prostate Center who underwent a prostate biopsy using a 3-D TRUS-guided system. After creation of a three-dimensional map of the prostate, a computer algorithm identified an ideal biopsy scheme based on the measured dimensions of the prostate. The system then used a fixed template that allowed prostate biopsy at specific locations with the ability to target the same region of the prostate in the future if needed. For all patients, a 12-core biopsy pattern was used to cover medial and lateral areas of the base, mid-gland, and apex. In total, 68 patients underwent 3-D TRUS-guided prostate biopsies between April 2006 and November 2007 for prostate cancer detection. The indication for prostate biopsy was PSA ≥ 4.0 ng/ml in 47 (69%) patients, abnormal digital rectal examination (DRE) in 17 (25%), and atypia on previous biopsy in 4 (6%) patients. Prostate cancer was detected in 18 patients (26.5%) and 7 (10.3%) had atypical small acinar proliferation (ASAP). The highest frequency (55.5%) from all cases of cancer detected was identified when 3-D TRUS biopsy was used as the initial biopsy. This study demonstrates that a 3-D TRUS-guided biopsy system translates to a more frequent detection of prostate cancer among patients undergoing an initial prostate biopsy than a subsequent one. More comprehensive studies are warranted to corroborate and extend the results of this study.