A PET/CT Directed, 3D Ultrasound-Guided Biopsy System for Prostate Cancer

Feasibility and Initial Results: Fluciclovine Positron Emission Tomography/Ultrasound Fusion Targeted Biopsy of Recurrent Prostate Cancer

PURPOSE

We assessed the feasibility and cancer detection rate of fluciclovine (¹⁸F) positron emission tomography-ultrasound fusion targeted biopsy vs standard template biopsy in the same patient with biochemical failure after nonsurgical therapy for prostate cancer.

MATERIALS AND METHODS

A total of 21 patients with a mean ± SD prostate specific antigen of 7.4 ± 6.8 ng/ml and biochemical failure after nonoperative prostate cancer treatment underwent fluciclovine (¹⁸F) positron emission tomography-computerized tomography (mean 364.1 ± 37.7 MBq) and planning transrectal prostate ultrasound with 3-dimensional image reconstruction. Focal prostatic activity on positron emission tomography was delineated and co-registered with planning ultrasound. During the subsequent biopsy session computer generated 12-core template biopsies were performed and then fluciclovine defined targets were revealed and biopsied. Histological analysis of template and targeted cores were completed.

RESULTS

Template biopsy was positive for malignancy in 6 of 21 patients (28.6%), including 10 of 124 regions and 11 of 246 cores, vs targeted biopsy in 10 of 21 (47.6%), including 17 of 50 regions and 40 of 125 cores. Five of 21 patients had positive findings on targeted biopsy only and 1 of 21 had positive findings on template biopsy only. An additional case was upgraded from Grade Group 2 to 3 on targeted biopsy. Extraprostatic disease was detected in 8 of 21 men (38.1%) with histological confirmation in all 3 who underwent lesion biopsy.

CONCLUSIONS

Fluciclovine positron emission tomography real-time ultrasound fusion guidance for biopsy is feasible in patients with biochemical failure after nonsurgical therapy for prostate cancer. It identifies more recurrent prostate cancer using fewer cores compared with template biopsy in the same patient. Further study is required to determine in what manner targeted biopsy may augment template biopsy of recurrent prostate cancer.

A random walk-based segmentation framework for 3D ultrasound images of the prostate

PURPOSE

Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images.

METHODS

The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance.

RESULTS

We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist.

CONCLUSIONS

The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.

PET Molecular Imaging-Directed Biopsy: A Review

OBJECTIVE

The purpose of this review is to summarize the applications of PET molecular imaging-directed biopsy of a variety of organs in the management of various diseases with a focus on cancers.

CONCLUSION

PET can yield metabolic information at the cellular and molecular levels, and PET-directed biopsy is playing an increasing role in the diagnosis and staging of diseases.

Molecular imaging and fusion targeted biopsy of the prostate

PURPOSE

This paper provides a review on molecular imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI) for prostate cancer detection and its applications in fusion targeted biopsy of the prostate.

METHODS

Literature search was performed through the PubMed database using the keywords "prostate cancer", "MRI/ultrasound fusion", "molecular imaging", and "targeted biopsy". Estimates in autopsy studies indicate that 50% of men older than 50 years of age have prostate cancer. Systematic transrectal ultrasound (TRUS) guided prostate biopsy is considered the standard method for prostate cancer detection and has a significant sampling error and a low sensitivity. Molecular imaging technology and new biopsy approaches are emerging to improve the detection of prostate cancer.

RESULTS

Molecular imaging with PET and MRI shows promising results in the early detection of prostate cancer. MRI/TRUS fusion targeted biopsy has become a new clinical standard for the diagnosis of prostate cancer. PET molecular image-directed, three-dimensional ultrasound-guided biopsy is a new technology that has great potential for improving prostate cancer detection rate and for distinguishing aggressive prostate cancer from indolent disease.

CONCLUSION

Molecular imaging and fusion targeted biopsy are active research areas in prostate cancer research.

Accuracy Evaluation of a 3D Ultrasound-guided Biopsy System

Early detection of prostate cancer is critical in maximizing the probability of successful treatment. Current systematic biopsy approach takes 12 or more randomly distributed core tissue samples within the prostate and can have a high potential, especially with early disease, for a false negative diagnosis. The purpose of this study is to determine the accuracy of a 3D ultrasound-guided biopsy system. Testing was conducted on prostate phantoms created from an agar mixture which had embedded markers. The phantoms were scanned and the 3D ultrasound system was used to direct the biopsy. Each phantom was analyzed with a CT scan to obtain needle deflection measurements. The deflection experienced throughout the biopsy process was dependent on the depth of the biopsy target. The results for markers at a depth of less than 20 mm, 20-30 mm, and greater than 30 mm were 3.3 mm, 4.7 mm, and 6.2 mm, respectively. This measurement encapsulates the entire biopsy process, from the scanning of the phantom to the firing of the biopsy needle. Increased depth of the biopsy target caused a greater deflection from the intended path in most cases which was due to an angular incidence of the biopsy needle. Although some deflection was present, this system exhibits a clear advantage in the targeted biopsy of prostate cancer and has the potential to reduce the number of false negative biopsies for large lesions.

A 5-MHz cylindrical dual-layer transducer array for 3-D transrectal ultrasound imaging

Two-dimensional transrectal ultrasound (TRUS) is being used in guiding prostate biopsies and treatments. In many cases, the TRUS probes are moved manually or mechanically to acquire volumetric information, making the imaging slow, user dependent, and unreliable. A real-time three-dimensional (3-D) TRUS system could improve reliability and volume rates of imaging during these procedures. In this article, the authors present a 5-MHz cylindrical dual-layer transducer array capable of real-time 3-D transrectal ultrasound without any mechanically moving parts. Compared with fully sampled 2-D arrays, this design substantially reduces the channel count and fabrication complexity. This dual-layer transducer uses PZT elements for transmit and P[VDF-TrFE] copolymer elements for receive, respectively. The mechanical flexibility of both diced PZT and copolymer makes it practical for transrectal applications. Full synthetic aperture 3-D data sets were acquired by interfacing the transducer with a Verasonics Data Acquisition System. Offline 3-D beamforming was then performed to obtain volumes of two wire phantoms and a cyst phantom. Generalized coherence factor was applied to improve the contrast of images. The measured -6-dB fractional bandwidth of the transducer was 62% with a center frequency of 5.66 MHz. The measured lateral beamwidths were 1.28 mm and 0.91 mm in transverse and longitudinal directions, respectively, compared with a simulated beamwidth of 0.92 mm and 0.74 mm.

3D ultrasound image segmentation using wavelet support vector machines

PURPOSE

Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy.

METHODS

This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method.

RESULTS

The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%.

CONCLUSIONS

The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.

3D Prostate Segmentation of Ultrasound Images Combining Longitudinal Image Registration and Machine Learning

We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.