Transrectal Ultrasound Images Research Articles

A multiobserver study investigating the effectiveness of prostatic multiparametric magnetic resonance imaging to dose escalate corresponding histologic lesions using high-dose-rate brachytherapy

PurposeUsing multiparametric MRI data and the pathologic data from radical prostatectomy specimens, we simulated the treatment planning of dose-escalated high-dose-rate brachytherapy (HDR-BT) to the Multiparametric MRI dominant intraprostatic lesion (mpMRI-DIL) to compare the dose potentially delivered to the pathologically confirmed locations of the high-grade component of the cancer. Methods and MaterialsPathologist-annotated prostatectomy midgland histology sections from 12 patients were registered to preprostatectomy mpMRI scans that were interpreted by four radiologists. To simulate realistic HDR-BT, we registered each observer's mpMRI-DILs and corresponding histology to two transrectal ultrasound images of other HDR-BT patients with a 15-Gy whole-gland prescription. We used clinical inverse planning to escalate the mpMRI-DILs to 20.25 Gy. We compared the dose that the histopathology would have received if treated with standard treatment plans to the dose mpMRI-targeting would have achieved. The histopathology was grouped as high-grade cancer (any Gleason Grade 4 or 5) and low-grade cancer (only Gleason Grade 3). Results212 mpMRI-targeted HDR-BT plans were analyzed. For high-grade histology, the mpMRI-targeted plans achieved significantly higher median [IQR] D98 and D90 values of 18.2 [16.7–19.5] Gy and 19.4 [17.8–20.9] Gy, respectively, in comparison with the standard plans (p = 0.01 and p = 0.003). For low-grade histology, the targeted treatment plans would have resulted in a significantly higher median D90 of 17.0 [16.1–18.4] Gy in comparison with standard plans (p = 0.015); the median D98 was not significantly higher (p = 0.2). ConclusionsIn this retrospective pilot study of 12 patients, mpMRI-based dose escalation led to increased dose to high-grade, but not low-grade, cancer. In our data set, different observers and mpMRI sequences had no substantial effect on dose to histologic cancer.

Patients presenting with lower urinary tract symptoms who most deserve to be investigated for primary bladder neck obstruction

We aimed to investigate clinical features potentially useful in primary bladder neck obstruction (PBNO) diagnosis in men presenting with lower urinary tract symptoms (LUTS). Data from 1229 men presenting for LUTS as their primary complaint at a single centre were retrospectively analysed. All patients underwent a comprehensive medical and physical assessment, and completed the International Prostate Symptoms Score. All patients were investigated with uroflowmetry, and trans-rectal ultrasound imaging to define prostate volume. Urodynamic evaluation was performed when the diagnosis of benign prostatic enlargement was not confirmed and the patient presented a significant chance of detrusor overactivity or underactivity. As per our internal protocol, patients < 60 years old with bothersome LUTS and > 60 years with a prostate volume (PV) < 40 mL were also investigated with urethrocystoscopy to rule out urethral stricture. Logistic regression analysis tested clinical predictors of possible PBNO. Of 1229 patients, 136 (11%) featured a clinical profile which was consistent with PBNO. Overall, these patients were younger (p < 0.0001), had lower BMI (p < 0.0001), less comorbidities (p = 0.004) and lower PSA values (p < 0.0001), but worse IPSS scores (p = 0.01) and lower PV values (p < 0.0001) compared to patients with other-aetiology LUTS. At multivariable analysis, younger age (OR 0.90; p = 0.003) and higher IPSS scores (OR 1.12; p = 0.01) were more likely to be associated with this subset of patients, after accounting for other clinical variables. One out of ten young/middle-aged men presenting for LUTS may be affected from PBNO. Younger patients with more severe LUTS systematically deserve an extensive assessment to rule out PBNO, thus including urethrocystoscopy and urodynamics with voiding-cysto-urethrogram.

Transrectal Ultrasound and Photoacoustic Imaging Probe for Diagnosis of Prostate Cancer.

A combined transrectal ultrasound and photoacoustic (TRUS–PA) imaging probe was developed for the clear visualization of morphological changes and microvasculature distribution in the prostate, as this is required for accurate diagnosis and biopsy. The probe consisted of a miniaturized 128-element 7 MHz convex array transducer with 134.5° field-of-view (FOV), a bifurcated optical fiber bundle, and two optical lenses. The design goal was to make the size of the TRUS–PA probe similar to that of general TRUS probes (i.e., about 20 mm), for the convenience of the patients. New flexible printed circuit board (FPCB), acoustic structure, and optical lens were developed to meet the requirement of the probe size, as well as to realize a high-performance TRUS–PA probe. In visual assessment, the PA signals obtained with the optical lens were 2.98 times higher than those without the lens. Moreover, the in vivo experiment with the xenograft BALB/c (Albino, Immunodeficient Inbred Strain) mouse model showed that TRUS–PA probe was able to acquire the entire PA image of the mouse tight behind the porcine intestine about 25 mm depth. From the ex vivo and in vivo experimental results, it can be concluded that the developed TRUS–PA probe is capable of improving PA image quality, even though the TRUS–PA probe has a cross-section size and an FOV comparable to those of general TRUS probes.

Development and Preliminary Evaluation of an Anthropomorphic Trans-rectal Ultrasound Prostate Brachytherapy Training Phantom

The quality of the trans-rectal ultrasound (TRUS) image, and thus seed placement during the prostate brachytherapy (PBT) procedure, relies on the user's technical and clinical competency. Simulation-based medical education can provide a structured approach for the acquisition of clinical competencies, but the efficacy of the training relies on the fidelity of the training simulators. In this work, the design, development and preliminary evaluation of an anthropomorphic training phantom for TRUS PBT is described. TRUS clinical patient data informed the design of 3-D printed moulds to fabricate prostate targets. Tissue-mimicking materials were included that had the sonographic characteristics of the prostate and overlying tissues, as well as the clinically relevant physical response, to provide haptic feedback to the user. Through an iterative design process, prototypes were constructed. These prototypes were quantitatively evaluated using a specification list and evaluated by an experienced clinical brachytherapy oncologist; their feedback was implemented, and the results of this evaluation are presented.

The efficiency of prostate-specific antigen density measurement using three different methods on the prediction of biochemical recurrence

Background The aim of this study was to evaluate the efficiency of prostate-specific antigen (PSA) density (PSAD) calculated through prostate volume (PV) obtained via transrectal ultrasound (TRUS) and magnetic resonance imaging (MRI) and actual prostate weight (PW) methods obtained via pathological evaluation on the prediction of biochemical recurrence (BCR) in the follow-ups of patients who had undergone radical prostatectomy (RP). Methods A total of 335 clinically localized prostate cancer (PCa) patients who had received open RP between January 2015 and December 2018 were enrolled in the study. Pre and postoperative demographic data, clinical and pathological findings and BCR conditions were recorded. The PSAD was calculated using information obtained through preoperative TRUS examinations, MRI, and collected pathological specimens after RP by dividing the maximum preoperative PSA value and PV/PW. Results In a mean follow-up duration of 20.2 ± 8.5 months, recurrence was observed in 52 patients (24.4%) and progression was observed in 8 (3.8%) patients. The TRUS-PSAD, MRI-PSAD, and PW-PSAD values were statistically significantly higher in BCR patients compared to non-BCR patients. The International Society of Urologic Pathologists (ISUP) grade 5 and pT3b as a pathological stage were detected as independent variables in the prediction of BCR formation. Actual PW had a high prediction value compared to other PSAD measurements at <40 g prostate weights, but it had a low prediction value in prostates with an actual PW >60 g. Conclusions In this study, it was stated that PSAD acquired through different imaging methods does not affect the usability of PSAD in BCR prediction in clinical practice. The ISUP grade 5 and pT3b stage PCa were detected as independent markers in BCR prediction after RP.

Artificial intelligence in the diagnosis of prostate cancer

To discuss the possibilities and prospects of using artificial intelligence (AI) in the diagnosis of prostate cancer (PC). The laboratory diagnosis of PC is considered and prostate images are analyzed according to transrectal ultrasound and magnetic resonance imaging using AI algorithms. Particular emphasis is placed on prostate histologic evaluation.

Intrapelvic Intestinal Leiomyoma in a Dog - Diagnostic and Therapeutic Challenges

Background: Rectal tumors are uncommon in dogs and cats. The clinical signs result from intra- and extraluminal compression. Diagnosis and treatment of rectal tumors are challenging due to their intraplevic location. Owing to considerable bone superposition, computed tomography is the best exam to evaluate the tumor and plan surgery; however, poor availability and high costs may hinder its use. The objective of this case report is to describe the successful use of a combination of diagnostic techniques, namely transrectal ultrasound, transrectal fine-needle aspiration, and colonoscopy, for diagnosis and surgery planning in a case of intrapelvic intestinal leiomyoma in a dog.Case: A 13-year-old female mongrel dog with tenesmus, low stool production, and hematochezia for two months was presented for examination. During this two-month period, a symptomatic treatment was administered, but there was no clinical improvement. In the clinical evaluation revealed a painless mass on the left dorsolateral region, at a depth of around 4 cm, with considerable luminal reduction. Abdominal ultrasound revealed a mass close to the descending colon; however, bone superposition precluded identification of its origin or delimitation of its boundaries. The patient was subjected to transrectal ultrasound imaging, colonoscopy, and cytological examination of fine-needle aspiration biopsy material collected under general anesthesia. The mass was located at the final portion of the descending colon; it was extraluminal, and measured around 7 x 7 cm. The integrity of the intestinal wall was preserved. Next, radiographic examination of the thorax using three projections (ventrodorsal, left lateral, and right lateral) was performed to check for metastases, and no alteration was detected. Cytology suggested presence of leiomyoma. The patient underwent exploratory laparotomy with pubic osteotomy for intrapelvic access. The extraluminal mass was found adhered to the dorsal colorectal surface, whose serosa was compromised. The mass, which occupied around 80 to 90% of the pelvic canal, was completely removed and submitted to histopathological examination, which confirmed presence of proliferative neoplastic mesenchymal cells (intestinal leiomyoma). The patient's clinical picture evolved without intercurrences, and the patient was discharged 40 days after the pubic bone consolidation procedure.Discussion: The occurrence of leiomyomas in the colorectal segment of the intestine is rare in dogs. Neoplasms that develop in such a region of the intestine are usually more frequently found in elderly animals, such as the patient of this report. Leiomyomas are benign (non-invasive) neoplasms with slow growth. Consequently, clinical signs emerge when the mass exhibits a large size, which causes intra- or extraluminal compression, tenesmus, diminished production or absence of defecation, and hematochezia, as observed in the present case. Even though the clinical signs are similar in these cases, they are unspecific; consequently, for reaching a diagnosis, biopsy and histological investigation are required. In spite of the usefulness of these procedures for diagnosis, computed tomography is the exam of choice to investigate neoplasms in intrapelvic intestinal segments because it allows three-dimensional reconstruction of the affected structures and facilitates surgical planning. Unfortunately, computed tomography was not available for this case. Consequently, colonoscopy, transrectal ultrasound, and transrectal fine-needle aspiration biopsy were performed. When combined, these procedures allowed determination of the location, size and type of neoplasm, which were crucial pieces of information for the correct diagnosis and surgical planning, thus contributing for the successful management of the patient.

Magnetic resonance imaging and transrectal ultrasound prostate image segmentation based on improved level set for robotic prostate biopsy navigation.

Transrectal ultrasound (TRUS) guided prostate biopsy is a typical early prostate examination. However, the ultrasound imaging suffers from blurred contour, intensity inhomogeneity and small surrounding soft tissue differentiation. To take advantage of clear magnetic resonance imaging (MRI) into robotic prostate biopsy navigation, the prostate regions in the MRI and TRUS images need to be segmented separately. This paper proposes an improved level set segmentation model based on prior shape, which aims to provide a better solution to the prostate segmentation problems in TRUS and MRI. In our segmentation model, the Gaussian probability model is used to establish the statistical learning of the prior shape, and the cosine function is used to represent the energy term fitting of the traditional prior shape and the local intensity information. The experiment results show that the model can adapt to different forms of prostate in MRI and TRUS more accurately, and the prostate biopsy accuracy in our biopsy system can reach 2.24 ± 1.44 mm. This segmentation model has high accuracy, meets the clinical needs in robotic prostate biopsy navigation.

Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.

PurposeTo develop, and evaluate the performance of, a deep learning‐based three‐dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) algorithm aimed at finding needles in ultrasound images used in prostate brachytherapy.MethodsTransrectal ultrasound (TRUS) image volumes from 1102 treatments were used to create a clinical ground truth (CGT) including 24422 individual needles that had been manually digitized by medical physicists during brachytherapy procedures. A 3D CNN U‐net with 128 × 128 × 128 TRUS image volumes as input was trained using 17215 needle examples. Predictions of voxels constituting a needle were combined to yield a 3D linear function describing the localization of each needle in a TRUS volume. Manual and AI digitizations were compared in terms of the root‐mean‐square distance (RMSD) along each needle, expressed as median and interquartile range (IQR). The method was evaluated on a data set including 7207 needle examples. A subgroup of the evaluation data set (n = 188) was created, where the needles were digitized once more by a medical physicist (G1) trained in brachytherapy. The digitization procedure was timed.ResultsThe RMSD between the AI and CGT was 0.55 (IQR: 0.35–0.86) mm. In the smaller subset, the RMSD between AI and CGT was similar (0.52 [IQR: 0.33–0.79] mm) but significantly smaller (P < 0.001) than the difference of 0.75 (IQR: 0.49–1.20) mm between AI and G1. The difference between CGT and G1 was 0.80 (IQR: 0.48–1.18) mm, implying that the AI performed as well as the CGT in relation to G1. The mean time needed for human digitization was 10 min 11 sec, while the time needed for the AI was negligible.ConclusionsA 3D CNN can be trained to identify needles in TRUS images. The performance of the network was similar to that of a medical physicist trained in brachytherapy. Incorporating a CNN for needle identification can shorten brachytherapy treatment procedures substantially.

Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection.

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

Multiparametric Ultrasound for Targeting Prostate Cancer: Combining ARFI, SWEI, QUS and B-Mode

Diagnosing prostate cancer through standard transrectal ultrasound (TRUS)-guided biopsy is challenging because of the sensitivity and specificity limitations of B-mode imaging. We used a linear support vector machine (SVM) to combine standard TRUS imaging data with acoustic radiation force impulse (ARFI) imaging data, shear wave elasticity imaging (SWEI) data and quantitative ultrasound (QUS) midband fit data to enhance lesion contrast into a synthesized multiparametric ultrasound volume. This SVM was trained and validated using a subset of 20 patients and tested on a second subset of 10 patients. Multiparametric US led to a statistically significant improvements in contrast, contrast-to-noise ratio (CNR) and generalized CNR (gCNR) when compared with standard TRUS B-mode and SWEI; in contrast and CNR when compared with MF; and in CNR when compared with ARFI. ARFI, MF and SWEI also outperformed TRUS B-mode in contrast, with MF outperforming B-mode in CNR and gCNR as well. ARFI, although only yielding statistically significant differences in contrast compared with TRUS B-mode, captured critical qualitative features for lesion identification. Multiparametric US enhanced lesion visibility metrics and is a promising technique for targeted TRUS-guided prostate biopsy in the future.

Deep adaptive registration of multi-modal prostate images

Artificial intelligence, especially the deep learning paradigm, has posed a considerable impact on cancer imaging and interpretation. For instance, fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images to guide prostate cancer biopsy can significantly improve the diagnosis. However, multi-modal image registration is still challenging, even with the latest deep learning technology, as it requires large amounts of labeled transformations for network training. This paper aims to address this problem from two angles: (i) a new method of generating large amount of transformations following a targeted distribution to improve the network training and (ii) a coarse-to-fine multi-stage method to gradually map the distribution from source to target. We evaluate both innovations based on a multi-modal prostate image registration task, where a T2-weighted MR volume and a reconstructed 3D ultrasound volume are to be aligned. Our results demonstrate that the use of data generation can significantly reduce the registration error by up to 62%. Moreover, the multi-stage coarse-to-fine registration technique results in a mean surface registration error (SRE) of 3.66 mm (with the initial mean SRE of 9.42 mm), which is found to be significantly better than the one-step registration with a mean SRE of 4.08 mm.

Dosimetric evaluation of MRI-to-ultrasound automated image registration algorithms for prostate brachytherapy

PurposeIdentifying dominant intraprostatic lesions (DILs) on transrectal ultrasound (TRUS) images during prostate high-dose-rate brachytherapy treatment planning remains a significant challenge. Multiparametric MRI (mpMRI) is the tool of choice for DIL identification; however, the geometry of the prostate on mpMRI and on the TRUS may differ significantly, requiring image registration. This study assesses the dosimetric impact attributed to differences in DIL contours generated using commonly available MRI to TRUS automated registration: rigid, semi-rigid, and deformable image registration, respectively. Methods and MaterialsTen patients, each with mpMRI and TRUS data sets, were included in this study. Five radiation oncologists with expertise in TRUS-based high-dose-rate brachytherapy were asked cognitively to transfer the DIL from the mpMRI images of each patient to the TRUS image. The contours were analyzed for concordance using simultaneous truth and performance level estimation (STAPLE) algorithm. The impact of DIL contour differences due to registration variability was evaluated by comparing the STAPLE-DIL dosimetry from the reference (STAPLE) plan with that from the evaluation plans (manual and automated registration) for each patient. The dosimetric impact of the automatic registration approach was also validated using a margin expansion that normalizes the volume of the autoregistered DILs to the volumes of the STAPLE-DILs. Dose metrics including D90, Dmean, V150, and V200 to the prostate and DIL were reported. For urethra and rectum, D10 and V80 were reported. ResultsSignificant differences in DIL coverage between reference and evaluation plans were found regardless of the algorithm methodology. No statistical difference was reported in STAPLE-DIL dosimetry when manual registration was used. A margin of 1.5 ± 0.8 mm, 1.1 ± 0.8 mm, and 2.5 ± 1.6 mm was required to be added for rigid, semi-rigid, and deformable registration, respectively, to mitigate the difference in STAPLE-DIL coverage between the evaluation and reference plans. ConclusionThe dosimetric impact of integrating an MRI-delineated DIL into a TRUS-based brachytherapy workflow has been validated in this study. The results show that rigid, semi-rigid, and deformable registration algorithms lead to a significant undercoverage of the DIL D90 and Dmean. A margin of at least 1.5 ± 0.8 mm, 1.1 ± 0.8 mm, and 2.5 ± 1.6 mm is required to be added to the rigid, semi-rigid, and deformable DIL registration to be suitable for DIL-boosting during prostate brachytherapy.

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoperative TRUS images is clinically important to have faster and reproducible interventions that can benefit both the clinical workflow and patient health. We present a multi-task deep learning method for an automatic prostate CTV boundary detection in intraoperative TRUS images by leveraging both the low-level and high-level (prior shape) information. Our method includes a channel-wise feature calibration strategy for low-level feature extraction and learning-based prior knowledge modeling for prostate CTV shape reconstruction. It employs CTV shape reconstruction from automatically sampled boundary surface coordinates (pseudo-landmarks) to detect the low-contrast and noisy regions across the prostate boundary, while being less biased from shadowing, inherent speckles, and artifact signals from the needle and implanted radioactive seeds. The proposed method was evaluated on a clinical database of 145 patients who underwent permanent prostate brachytherapy under TRUS guidance. Our method achieved a mean accuracy of [Formula: see text] and a mean surface distance error of [Formula: see text]. Extensive ablation and comparison studies show that our method outperformed previous deep learning-based methods by more than 7% for the Dice similarity coefficient and 6.9 mm reduced 3D Hausdorff distance error. Our study demonstrates the potential of shape model-based deep learning methods for an efficient and accurate CTV segmentation in an ultrasound-guided intervention. Moreover, learning both low-level features and prior shape knowledge with channel-wise feature calibration can significantly improve the performance of deep learning methods in medical image segmentation.

Prostate brachytherapy intraoperative dosimetry using a combination of radiographic seed localization with a C-arm and deformed ultrasound prostate contours

PurposeThe purpose of the study was to assess the feasibility of performing intraoperative dosimetry for permanent prostate brachytherapy by combining transrectal ultrasound (TRUS) and fluoroscopy/cone beam CT [CBCT] images and accounting for the effect of prostate deformation. Methods and Materials13 patients underwent TRUS and multiview two-dimensional fluoroscopic imaging partway through the implant, as well as repeat fluoroscopic imaging with the TRUS probe inserted and retracted, and finally three-dimensional CBCT imaging at the end of the implant. The locations of all the implanted seeds were obtained from the fluoroscopy/CBCT images and were registered to prostate contours delineated on the TRUS images based on a common subset of seeds identified on both image sets. Prostate contours were also deformed, using a finite-element model, to take into account the effect of the TRUS probe pressure. Prostate dosimetry parameters were obtained for fluoroscopic and CBCT-dosimetry approaches and compared with the standard-of-care Day-0 postimplant CT dosimetry. ResultsHigh linear correlation (R2 > 0.8) was observed in the measured values of prostate D90%, V100%, and V150%, between the two intraoperative dosimetry approaches. The prostate D90% and V100% obtained from intraoperative dosimetry methods were in agreement with the postimplant CT dosimetry. Only the prostate V150% was on average 4.1% (p-value <0.05) higher in the CBCT-dosimetry approach and 6.7% (p-value <0.05) higher in postimplant CT dosimetry compared with the fluoroscopic dosimetry approach. Deformation of the prostate by the ultrasound probe appeared to have a minimal effect on prostate dosimetry. ConclusionsThe results of this study have shown that both of the proposed dosimetric evaluation approaches have potential for real-time intraoperative dosimetry.

Hybrid TRUS/CT with optical tracking for target delineation in image-guided adaptive brachytherapy for cervical cancer

ObjectiveTo prospectively compare the interobserver variability of combined transrectal ultrasound (TRUS)/computed tomography (CT)- vs. CT only- vs. magnetic resonance imaging (MRI) only-based contouring of the high-risk clinical target volume (CTVHR) in image-guided adaptive brachytherapy (IGABT) for locally advanced cervical cancer (LACC).MethodsFive patients with LACC (FIGO stages IIb–IVa) treated with radiochemotherapy and IGABT were included. CT, TRUS, and T2-weighted MRI images were performed after brachytherapy applicator insertion. 3D-TRUS image acquisition was performed with a customized ultrasound stepper device and software. Automatic applicator reconstruction using optical tracking was performed in the TRUS dataset and TRUS and CT images were fused with rigid image registration with the applicator as reference structure. The CTVHR (based on the GEC-ESTRO recommendations) was contoured by five investigators on the three modalities (CTVHR_CT, CTVHR_TRUS-CT, and CTVHR_MRI). A consensus reference CTVHR_MRI (MRIref) was defined for each patient. Descriptive statistics and overlap measures were calculated using RTslicer (SlicerRT Community and Percutaneous Surgery Laboratory, Queen’s University, Canada), comparing contours of every observer with one another and with the MRIref.ResultsThe interobserver coefficient of variation was 0.18 ± 0.05 for CT, 0.10 ± 0.04 for TRUS-CT, and 0.07 ± 0.03 for MRI. Interobserver concordance in relation to the MRIref expressed by the generalized conformity index was 0.75 ± 0.04 for MRI, 0.51 ± 0.10 for TRUS-CT, and 0.48 ± 0.06 for CT. The mean CTVHR_CT volume of all observers was 71% larger than the MRIref volume, whereas the mean CTVHR_TRUS-CT volume was 15% larger.ConclusionHybrid TRUS-CT as an imaging modality for contouring the CTVHR in IGABT for LACC is feasible and reproducible among multiple observers. TRUS-CT substantially reduces overestimation of the CTVHR volume of CT alone while maintaining similar interobserver variability.

Single-Center Experience in Targeted Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging‑Transrectal Ultrasound Elastic Fusion Technique

Introduction: Transrectal ultrasound (TRUS)-guided random biopsies are used to be the gold standard when diagnosing prostate cancer. A relatively new system with organ tracking that fuses real-time TRUS images with previously acquired multiparametric magnetic resonance imaging (mpMRI) images for prostate biopsy guidance is presented here. The primary goal of the study is to correlate (1) the mpMRI findings with the Gleason score grading of the prostate biopsies performed under mpMRI-TRUS elastic fusion and (2) the prostate-specific antigen (PSA) levels with the Gleason grading. Materials and Methods: Between January 2017 and August 2018, 58 patients had targeted prostate biopsy using mpMRI-TRUS elastic fusion technique (Urostation). These patients had previously the mpMRI of the prostate at our center using three-dimensional T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhanced imaging. Of all 58 patients, 32 patients were classified as having Prostate Imaging-Reporting And Data System (PI-RADS) 4/5, 21 patients as PI-RADS 3, and five as PI-RADS 2. Results: Twenty-seven patients had positive biopsies for prostate cancer. Positive results were found in 25 patients having PI-RADS 4/5 (25 out of 32). Of these patients, 20 had positive specimens from the dominant lesion, four from both the targeted lesion and nontargeted areas, and one from a nontargeted area. Positive results were found in two patients classified as PI-RADS 3 from targeted and nontargeted areas. These results show that 78% of the patients classified by mpMRI as PI-RADS 4/5 and 10% of the patients classified as PI-RADS 3 had positive biopsies for prostate cancer. The results also showed a correlation between the PI-RADS score on mpMRI, the Gleason score, and the PSA levels. Conclusion: mpMRI-TRUS fusion biopsy is a safe and accurate method for targeted prostate biopsies. Our preliminary results are comparable to the published international numbers and show a good correlation between the PI-RADS classification and histopathology, as well as correlation between PI-RADS, Gleason scores, and PSA levels of positive biopsies.

Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy

Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of the prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio, and signal-to-noise ratio in TRUS. In this study, we proposed a fully automated deep learning approach based on a weakly supervised method to address these issues. We employed deep learning techniques to combine image segmentation and registration, including affine and nonrigid registration, to perform an automated deformable MRI-TRUS registration. To start with, we trained two separate fully convolutional neural networks (CNNs) to perform a pixel-wise prediction for MRI and TRUS prostate segmentation. Then, to provide the initialization of the registration, a 2D CNN was used to register MRI-TRUS prostate images using an affine registration. After that, a 3D UNET-like network was applied for nonrigid registration. For both the affine and nonrigid registration, pairs of MRI-TRUS labels were concatenated and fed into the neural networks for training. Due to the unavailability of ground-truth voxel-level correspondences and the lack of accurate intensity-based image similarity measures, we propose to use prostate label-derived volume overlaps and surface agreements as an optimization objective function for weakly supervised network training. Specifically, we proposed a hybrid loss function that integrated a Dice loss, a surface-based loss, and a bending energy regularization loss for the nonrigid registration. The Dice and surface-based losses were used to encourage the alignment of the prostate label between the MRI and the TRUS. The bending energy regularization loss was used to achieve a smooth deformation field. Thirty-six sets of patient data were used to test our registration method. The image registration results showed that the deformed MR image aligned well with the TRUS image, as judged by corresponding cysts and calcifications in the prostate. The quantitative results showed that our method produced a mean target registration error (TRE) of 2.53 ± 1.39 mm and a mean Dice loss of 0.91 ± 0.02. The mean surface distance (MSD) and Hausdorff distance (HD) between the registered MR prostate shape and TRUS prostate shape were 0.88 and 4.41 mm, respectively. This work presents a deep learning-based, weakly supervised network for accurate MRI-TRUS image registration. Our proposed method has achieved promising registration performance in terms of Dice loss, TRE, MSD, and HD.

Can transrectal prostate ultrasound compete with multiparametric MRI in the detection of clinically significant prostate cancer?

We consider the current and future role of transrectal ultrasound imaging in the diagnosis of prostate cancer, with a particular focus on the pre-biopsy localization and targeting role that multiparametric MRI (mpMRI) has come to occupy for some men in recent years. We draw a distinction between transrectal ultrasound (TRUS) used only as a means of distributing zonal biopsies with its employment as a means for identifying and targeting sonographically abnormal lesions. The role of AI in lesion identification and targeting will be reviewed. Comparisons of cost and availability, frequency of contraindications and diagnostic accuracy between these two imaging modalities will be drawn.

Multi-Channel Feature Pyramid Networks for Prostate Segmentation, Based on Transrectal Ultrasound Imaging

Accurate segmentation for transrectal ultrasound imaging (TRUS) is often a challenging medical image processing task. The problem of weak boundary between adjacent prostate tissue and non-prostate tissue, and high similarity between artifact area and prostate area has always been the difficulty of TRUS image segmentation. In this paper, we construct a multi-channel feature pyramid network (MFPN) based on deep convolutional neural network-based prostate segmentation method to process multi-scale feature maps. Each level enhances the edge characteristics of the prostate by controlling the scale of the channel. The optimized regression mechanism of the target area was used to accurately locate the prostate. Experimental results showed that the proposed method achieved the key indicator Dice similarity coefficient and average absolute distance of 0.9651 mm and 0.504 mm, which outperformed state-of-the-art approaches.