Permutations in pacer wire implantation in patients evaluated for transcatheter tricuspid valve interventions.

Pacer wire induced tricuspid regurgitation is not well-understood. The mechanisms behind pacer wired induced tricuspid regurgitation have not been clearly defined. This clinical vignette sets to identify different technical mechanisms behind cardiac lead induced tricuspid regurgitation to help optimize cardiac lead implantation strategies for future device implantation.

Sodium thiosulphate and progression of vascular calcification in end-stage renal disease patients: a double-blind, randomized, placebo-controlled study.

BACKGROUND: Sodium thiosulphate (NaTS) is mostly used in haemodialysis (HD) patients with calcific uraemic arteriolopathy. This double-blind, randomized, placebo-controlled study assessed the effect of NaTS on progression of cardiovascular calcifications in HD patients. METHODS: From 65 screened patients, we recruited 60 patients with an abdominal aorta Agatston calcification score ≥100. Thirty patients were randomized to receive NaTS 25 g/1.73 m2 and 30 patients to receive 100 mL of 0.9% sodium chloride intravenously during the last 15 min of HD over a period of 6 months. The primary endpoint was the absolute change of the abdominal aortic calcification score. RESULTS: The abdominal aortic calcification score and calcification volume of the abdominal aorta increased similarly in both treatment groups during the trial. As compared with the saline group, patients receiving NaTS exhibited a reduction of their iliac artery calcification score (-137 ± 641 versus 245 ± 755; P = 0.049), reduced pulse wave velocity (9.6 ± 2.7 versus 11.4 ± 3.6; P = 0.000) and a lower carotid intima-media thickness (0.77 ± 0.1 versus 0.83 ± 00.17; P = 0.033) and had better preservation of echocardiographic parameters of left ventricular hypertrophy. No patient of the NaTS group developed new cardiac valve calcifications during the trial as compared with 8 of 29 patients in the saline group. By univariate analysis, NaTS therapy was the only predictor of not developing new valvular calcifications. No adverse events possibly related to NaTS infusion were noted. CONCLUSIONS: While NaTS failed to retard abdominal aortic calcification progress, it positively affected calcification progress in iliac arteries and heart valves as well as several other cardiovascular functional parameters.

Validating a prediction modeling tool for left ventricular outflow tract (LVOT) obstruction after transcatheter mitral valve replacement (TMVR).

OBJECTIVE: Demonstrate proof-of-concept validation of a computed tomography (CT) computer-aided design prediction modeling tool to identify patients at risk for left ventricular outflow tract (LVOT) obstruction in transcatheter mitral valve replacement (TMVR). BACKGROUND: LVOT obstruction is a significant and even fatal consequence of TMVR. METHODS: From August 2013 to August 2017, 38 patients in 5 centers underwent TMVR with compassionate use of balloon-expandable valves for severe mitral valve dysfunction because of degenerative surgical mitral ring, bioprosthesis, or severe native mitral stenosis from to severe mitral annular calcification. All patients had preprocedural CT scans performed for anatomic screening, intraprocedural TEE and invasive hemodynamics performed. Preprocedural prediction modeling was performed utilizing computer-aided design (CAD) of the neo-LVOT post-TMVR. Post-TMVR CT scans were obtained and compared to pre-TMVR LVOT modeling datasets for validation. RESULTS: All patients underwent successful TMVR without device embolization. Seven of the 38 patients experienced LVOT obstruction, defined as an increase of ≥10 mmHg LVOT peak gradient post-TMVR. Anatomic screening using CT was validated in 20/38 patients as preprocedural predicted neo-LVOT surface area correlated well with post-TMVR measurements (R2 = 0.8169, P < 0.0001). A receiver operating curve curve found a predicted neo-LVOT surface area of ≤ 189.4 mm2 to have 100% sensitivity and 96.8% specificity for predicting TMVR-induced LVOT obstruction. CONCLUSION: CAD design and CT postprocessing are indispensable tools in predicting LVOT obstruction and necessary for anatomic screening in percutaneous TMVR.

Initial clinical experience with a radiation oncology dedicated open 1.0T MR-simulation.

The purpose of this study was to describe our experience with 1.0T MR-SIM including characterization, quality assurance (QA) program, and features necessary for treatment planning. Staffing, safety, and patient screening procedures were developed. Utilization of an external laser positioning system (ELPS) and MR-compatible couchtop were illustrated. Spatial and volumetric analyses were conducted between CT-SIM and MR-SIM using a stereotactic QA phantom with known landmarks and volumes. Magnetic field inhomogeneity was determined using phase difference analysis. System-related, in-plane distortion was evaluated and temporal changes were assessed. 3D distortion was characterized for regions of interest (ROIs) 5-20 cm away from isocenter. American College of Radiology (ACR) recommended tests and impact of ELPS on image quality were analyzed. Combined ultrashort echotime Dixon (UTE/Dixon) sequence was evaluated. Amplitude-triggered 4D MRI was implemented using a motion phantom (2-10 phases, ~ 2 cm excursion, 3-5 s periods) and a liver cancer patient. Duty cycle, acquisition time, and excursion were evaluated between maximum intensity projection (MIP) datasets. Less than 2% difference from expected was obtained between CT-SIM and MR-SIM volumes, with a mean distance of < 0.2 mm between landmarks. Magnetic field inhomogeneity was < 2 ppm. 2D distortion was < 2 mm over 28.6-33.6 mm of isocenter. Within 5 cm radius of isocenter, mean 3D geometric distortion was 0.59 ± 0.32 mm (maximum = 1.65 mm) and increased 10-15 cm from isocenter (mean = 1.57 ± 1.06 mm, maximum = 6.26 mm). ELPS interference was within the operating frequency of the scanner and was characterized by line patterns and a reduction in signal-to-noise ratio (4.6-12.6% for TE = 50-150 ms). Image quality checks were within ACR recommendations. UTE/Dixon sequences yielded detectability between bone and air. For 4D MRI, faster breathing periods had higher duty cycles than slow (50.4% (3 s) and 39.4% (5 s), p < 0.001) and ~fourfold acquisition time increase was measured for ten-phase versus two-phase. Superior-inferior object extent was underestimated 8% (6 mm) for two-phase as compared to ten-phase MIPs, although < 2% difference was obtained for ≥ 4 phases. 4D MRI for a patient demonstrated acceptable image quality in ~ 7 min. MR-SIM was integrated into our workflow and QA procedures were developed. Clinical applicability was demonstrated for 4D MRI and UTE imaging to support MR-SIM for single modality treatment planning.

Prostate cancer detection and segmentation on MRI using non-local mask R-CNN with histopathological ground truth.

BACKGROUND: Automatic detection and segmentation of intraprostatic lesions (ILs) on preoperative multiparametric-magnetic resonance images (mp-MRI) can improve clinical workflow efficiency and enhance the diagnostic accuracy of prostate cancer and is an essential step in dominant intraprostatic lesion boost. PURPOSE: The goal is to improve the detection and segmentation accuracy of 3D ILs in MRI by a proposed a deep learning (DL)-based algorithm with histopathological ground truth. METHODS: This retrospective study included 262 patients with in vivo prostate biparametric MRI (bp-MRI) scans and were divided into three cohorts based on their data analysis and annotation. Histopathological ground truth was established by using histopathology images as delineation reference standard on cohort 1, which consisted of 64 patients and was randomly split into 20 training, 12 validation, and 32 testing patients. Cohort 2 consisted of 158 patients with bp-MRI based lesion delineation, and was randomly split into 104 training, 15 validation, and 39 testing patients. Cohort 3 consisted of 40 unannotated patients, used in semi-supervised learning. We proposed a non-local Mask R-CNN and boosted its performance by applying different training techniques. The performance of non-local Mask R-CNN was compared with baseline Mask R-CNN, 3D U-Net and an experienced radiologist's delineation and was evaluated by detection rate, dice similarity coefficient (DSC), sensitivity, and Hausdorff Distance (HD). RESULTS: The independent testing set consists of 32 patients with histopathological ground truth. With the training technique maximizing detection rate, the non-local Mask R-CNN achieved 80.5% and 94.7% detection rate; 0.548 and 0.604 DSC; 5.72 and 6.36 95 HD (mm); 0.613 and 0.580 sensitivity for ILs of all Gleason Grade groups (GGGs) and clinically significant ILs (GGG > 2), which outperformed baseline Mask R-CNN and 3D U-Net. For clinically significant ILs, the model segmentation accuracy was significantly higher than that of the experienced radiologist involved in the study, who achieved 0.512 DSC (p = 0.04), 8.21 (p = 0.041) 95 HD (mm), and 0.398 (p = 0.001) sensitivity. CONCLUSION: The proposed DL model achieved state-of-art performance and has the potential to help improve radiotherapy treatment planning and noninvasive prostate cancer diagnosis.

Characterizing Sensitive Cardiac Substructure Excursion Due to Respiration.

PURPOSE: Whole-heart dose metrics are not as strongly linked to late cardiac morbidities as radiation doses to individual cardiac substructures. Our aim was to characterize the excursion and dosimetric variation throughout respiration of sensitive cardiac substructures for future robust safety margin design. METHODS AND MATERIALS: Eleven patients with cancer treatments in the thorax underwent 4-phase noncontrast 4-dimensional computed tomography (4DCT) with T2-weighted magnetic resonance imaging in end-exhale. The end-exhale phase of the 4DCT was rigidly registered with the magnetic resonance imaging and refined with an assisted alignment surrounding the heart from which 13 substructures (chambers, great vessels, coronary arteries, etc) were contoured by a radiation oncologist on the 4DCT. Contours were deformed to the other respiratory phases via an intensity-based deformable registration for radiation oncologist verification. Measurements of centroid and volume were evaluated between phases. Mean and maximum dose to substructures were evaluated across respiratory phases for the breast (n = 8) and thoracic cancer (n = 3) cohorts. RESULTS: Paired t tests revealed reasonable maintenance of geometric and anatomic properties (P < .05 for 4/39 volume comparisons). Maximum displacements >5 mm were found for 24.8%, 8.5%, and 64.5% of the cases in the left-right, anterior-posterior, and superior-inferior axes, respectively. Vector displacements were largest for the inferior vena cava and the right coronary artery, with displacements up to 17.9 mm. In breast, the left anterior descending artery Dmean varied 3.03 ± 1.75 Gy (range, 0.53-5.18 Gy) throughout respiration whereas lung showed patient-specific results. Across all patients, whole heart metrics were insensitive to breathing phase (mean and maximum dose variations <0.5 Gy). CONCLUSIONS: This study characterized the intrafraction displacement of the cardiac substructures through the respiratory cycle and highlighted their increased dosimetric sensitivity to local dose changes not captured by whole heart metrics. Results suggest value of cardiac substructure margin generation to enable more robust cardiac sparing and to reduce the effect of respiration on overall treatment plan quality.

Quantifying inter-fraction cardiac substructure displacement during radiotherapy via magnetic resonance imaging guidance.

PURPOSE: Emerging evidence suggests cardiac substructures are highly radiosensitive during radiation therapy for cancer treatment. However, variability in substructure position after tumor localization has not been well characterized. This study quantifies inter-fraction displacement and planning organ at risk volumes (PRVs) of substructures by leveraging the excellent soft tissue contrast of magnetic resonance imaging (MRI). METHODS: Eighteen retrospectively evaluated patients underwent radiotherapy for intrathoracic tumors with a 0.35 T MRI-guided linear accelerator. Imaging was acquired at a 17-25 s breath-hold (resolution 1.5 × 1.5 × 3 mm3). Three to four daily MRIs per patient (n = 71) were rigidly registered to the planning MRI-simulation based on tumor matching. Deep learning or atlas-based segmentation propagated 13 substructures (e.g., chambers, coronary arteries, great vessels) to daily MRIs and were verified by two radiation oncologists. Daily centroid displacements from MRI-simulation were quantified and PRVs were calculated. RESULTS: Across substructures, inter-fraction displacements for 14% in the left-right, 18% in the anterior-posterior, and 21% of fractions in the superior-inferior were > 5 mm. Due to lack of breath-hold compliance, ~4% of all structures shifted > 10 mm in any axis. For the chambers, median displacements were 1.8, 1.9, and 2.2 mm in the left-right, anterior-posterior, and superior-inferior axis, respectively. Great vessels demonstrated larger displacements (> 3 mm) in the superior-inferior axis (43% of shifts) and were only 25% (left-right) and 29% (anterior-posterior) elsewhere. PRVs from 3 to 5 mm were determined as anisotropic substructure-specific margins. CONCLUSIONS: This exploratory work derived substructure-specific safety margins to ensure highly effective cardiac sparing. Findings require validation in a larger cohort for robust margin derivation and for applications in prospective clinical trials.

Additive Value of Preprocedural Computed Tomography Planning Versus Stand-Alone Transesophageal Echocardiogram Guidance to Left Atrial Appendage Occlusion: Comparison of Real-World Practice.

Background Transesophageal echocardiogram is currently the standard preprocedural imaging for left atrial appendage occlusion. This study aimed to assess the additive value of preprocedural computed tomography (CT) planning versus stand-alone transesophageal echocardiogram imaging guidance to left atrial appendage occlusion. Methods and Results We retrospectively reviewed 485 Watchman implantations at a single center to compare the outcomes of using additional CT preprocedural planning (n=328, 67.6%) versus stand-alone transesophageal echocardiogram guidance (n=157, 32.4%) for left atrial appendage occlusion. The primary end point was the rate of successful device implantation without major peri-device leak (>5 mm). Secondary end points included major adverse events, total procedural time, delivery sheath and devices used, risk of major peri-device leak and device-related thrombus at follow-up imaging. A single/anterior-curve delivery sheath was used more commonly in those who underwent CT imaging (35.9% versus 18.8%; P<0.001). Additional preprocedural CT planning was associated with a significantly higher successful device implantation rate (98.5% versus 94.9%; P=0.02), a shorter procedural time (median, 45.5 minutes versus 51.0 minutes; P=0.03) and a less frequent change of device size (5.6% versus 12.1%; P=0.01), particularly device upsize (4% versus 9.4%; P=0.02). However, there was no significant difference in the risk of major adverse events (2.1% versus 1.9%; P=0.87). Only 1 significant peri-device leak (0.2%) and 5 device-related thrombi were detected in follow-up (1.2%) with no intergroup difference. Conclusions Additional preprocedural planning using CT in Watchman implantation was associated with a higher successful device implantation rate, a shorter total procedural time, and a less frequent change of device sizes.

Ductal carcinoma in situ of the breast: MR imaging findings with histopathologic correlation.

Ductal carcinoma in situ (DCIS) is a noninvasive malignancy that is commonly encountered at routine breast imaging. It may be a primary tumor or may be seen in association with other focal higher-grade tumors. Early detection is important because of the large proportion of DCIS that can progress to invasive carcinoma. The extent of DCIS involvement is frequently underestimated at mammography, which can reliably help detect only calcified DCIS; consequently, magnetic resonance (MR) imaging evaluation can alter the course of treatment. Seven biopsy-proved cases of DCIS were evaluated with T2-weighted MR imaging sequences, as well as T1-weighted sequences performed both before and after contrast material administration. The signal intensity and enhancement patterns of the tumors were analyzed, and the findings were correlated with the relevant underlying histopathologic features. Common enhancement patterns of DCIS include clumped linear-ductal enhancement, clumped focal enhancement, and masslike enhancement. The most common enhancement distribution pattern is segmental, followed by focal, diffuse, linear-ductal, and regional patterns. At T2-weighted MR imaging, DCIS is typically isointense relative to breast parenchyma; less commonly, it is hypointense or hyperintense. The use of MR imaging in the evaluation of DCIS is controversial, and many questions remain with regard to treatment and management. However, breast MR imaging can be extremely useful in the preoperative diagnosis and evaluation of DCIS when used in conjunction with other imaging modalities.

Cardiac substructure segmentation with deep learning for improved cardiac sparing.

PURPOSE: Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input. MATERIALS/METHODS: Thirty-two left-sided whole-breast cancer patients underwent cardiac T2 MRI and CT-simulation. A rigid cardiac-confined MR/CT registration enabled ground truth delineations of 12 substructures (chambers, great vessels (GVs), coronary arteries (CAs), etc.). Paired MRI/CT data (25 patients) were placed into separate image channels to train a three-dimensional (3D) neural network using the entire 3D image. Deep supervision and a Dice-weighted multi-class loss function were applied. Results were assessed pre/post augmentation and post-processing (3D conditional random field (CRF)). Results for 11 test CTs (seven unique patients) were compared to ground truth and a multi-atlas method (MA) via Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Wilcoxon signed-ranks tests. Three physicians evaluated clinical acceptance via consensus scoring (5-point scale). RESULTS: The model stabilized in ~19 h (200 epochs, training error <0.001). Augmentation and CRF increased DSC 5.0 ± 7.9% and 1.2 ± 2.5%, across substructures, respectively. DL provided accurate segmentations for chambers (DSC = 0.88 ± 0.03), GVs (DSC = 0.85 ± 0.03), and pulmonary veins (DSC = 0.77 ± 0.04). Combined DSC for CAs was 0.50 ± 0.14. MDA across substructures was <2.0 mm (GV MDA = 1.24 ± 0.31 mm). No substructures had statistical volume differences (P > 0.05) to ground truth. In four cases, DL yielded left main CA contours, whereas MA segmentation failed, and provided improved consensus scores in 44/60 comparisons to MA. DL provided clinically acceptable segmentations for all graded patients for 3/4 chambers. DL contour generation took ~14 s per patient. CONCLUSIONS: These promising results suggest DL poses major efficiency and accuracy gains for cardiac substructure segmentation offering high potential for rapid implementation into RTP for improved cardiac sparing.

Post Prostatectomy Pathologic Findings of Patients With Clinically Significant Prostate Cancer and no Significant PI-RADS Lesions on Preoperative Magnetic Resonance Imaging.

OBJECTIVES: We present postprostatectomy pathology results from a series of prostate cancer (Pca) Gleason grade group ≥2 patients who did not have findings suggestive of cancer on preoperative pelvic magnetic resonance imaging (MRI). METHODS: We performed an institutional retrospective study of prostate magnetic resonance imaging (MRI) examinations done from October 2015 to February 2018. We identified patients who underwent prostatectomy for Pca Gleason ≥3 + 4 diagnosed on prostate biopsy with no associated MRI findings suggestive of malignancy and analyzed their postprostatectomy pathologic findings and MRI imaging results. RESULTS: At our institution, 850 men with Pca received MRI between 2015 and 2018, and 156/850 patients received robotic-assisted radical prostatectomy. Thirty-three patients (33/156 = 21%) had negative MRI for PIRAD 3 or greater but had a biopsy showing significant Pca. Their mean (range) age was 62.7 (50-86) years. Their median (interquartile range) PSA, and PSA density were, 4.6 (3.7) ng/mL and 0.12 (0.05) ng/mL/cm2, respectively; all not significantly different from patients with visible lesions on MRI who underwent surgery. On post prostatectomy pathology, 27/33 (82%) men had Pca Gleason score 7 or greater. The most common pattern was infiltrative growth with cancer glands intermingling between benign glands. CONCLUSION: We describe the pathologic and imaging findings in an extensive series of men with clinically significant Pca with no significant lesions on preoperative MRI. Our results support the importance of patient counseling on the risk of missing significant Pca on MRI in isolation from other clinical variables.

Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks.

PURPOSE: Accurate delineation of the prostate gland and intraprostatic lesions (ILs) is essential for prostate cancer dose-escalated radiation therapy. The aim of this study was to develop a sophisticated deep neural network approach to magnetic resonance image analysis that will help IL detection and delineation for clinicians. METHODS AND MATERIALS: We trained and evaluated mask region-based convolutional neural networks to perform the prostate gland and IL segmentation. There were 2 cohorts in this study: 78 public patients (cohort 1) and 42 private patients from our institution (cohort 2). Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches cropped out. The IL segmentation model was extended to select 5 highly suspicious volumetric lesions within the entire prostate. RESULTS: The mask region-based convolutional neural networks model was able to segment the prostate with dice similarity coefficient (DSC) of 0.88 ± 0.04, 0.86 ± 0.04, and 0.82 ± 0.05; sensitivity (Sens.) of 0.93, 0.95, and 0.95; and specificity (Spec.) of 0.98, 0.85, and 0.90. However, ILs were segmented with DSC of 0.62 ± 0.17, 0.59 ± 0.14, and 0.38 ± 0.19; Sens. of 0.55 ± 0.30, 0.63 ± 0.28, and 0.22 ± 0.24; and Spec. of 0.974 ± 0.010, 0.964 ± 0.015, and 0.972 ± 0.015 in public validation/public testing/private testing patients when trained with patients from cohort 1 only. When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009, 0.969 ± 0.016, and 0.977 ± 0.013. CONCLUSIONS: Our research framework is able to perform as an end-to-end system that automatically segmented the prostate gland and identified and delineated highly suspicious ILs within the entire prostate. Therefore, this system demonstrated the potential for assisting the clinicians in tumor delineation.

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network.

PURPOSE: Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. In this paper, an efficient convolutional neural network (CNN) was developed and the model interpretation at various convolutional layers was systematically analyzed to improve the understanding of how CNN interprets multimodality medical images and the predictive powers of features at each layer. The problem of small sample size was addressed by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine (wELM), with imbalanced distribution among output categories taken into consideration. METHODS: The training data collection used a retrospective set of prostate MR studies, from SPIE-AAPM-NCI PROSTATEx Challenges held in 2017. Three hundred twenty biopsy samples of lesions from 201 prostate cancer patients were diagnosed and identified as clinically significant (malignant) or not significant (benign). All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE) and diffusion-weighted (DW) imaging. After registration and lesion-based normalization, a CNN with four convolutional layers were developed and trained on tenfold cross validation. The features from intermediate layers were then extracted as input to wELM to test the discriminative power of each individual layer. The best performing model from the tenfolds was chosen to be tested on the holdout cohort from two sources. Feature maps after each convolutional layer were then visualized to monitor the trend, as the layer propagated. Scatter plotting was used to visualize the transformation of data distribution. Finally, a class activation map was generated to highlight the region of interest based on the model perspective. RESULTS: Experimental trials indicated that the best input for CNN was a modality combination of T2W, apparent diffusion coefficient (ADC) and DWIb50 . The convolutional features from CNN paired with a weighted extreme learning classifier showed substantial performance compared to a CNN end-to-end training model. The feature map visualization reveals similar findings on natural images where lower layers tend to learn lower level features such as edges, intensity changes, etc, while higher layers learn more abstract and task-related concept such as the lesion region. The generated saliency map revealed that the model was able to focus on the region of interest where the lesion resided and filter out background information, including prostate boundary, rectum, etc. CONCLUSIONS: This work designs a customized workflow for the small and imbalanced dataset of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patient stratification.

The effect of multiplicity of PI-RADS 3 lesions on cancer detection rate of confirmatory targeted biopsy in patients diagnosed with prostate cancer and managed with active surveillance.

BACKGROUND AND OBJECTIVE: To determine the effect of multiplicity of prostate imaging reporting and data system assessment category 3 (PI-RADS 3) lesions on cancer detection rate (CDR) of confirmatory targeted biopsy of such lesion in patients diagnosed with prostate cancer and managed with active surveillance. METHODS: This study was conducted at a single academic institution. There were 91 men with ≥ 1 PI-RADS 3 lesion detected through magnetic resonance imaging (MRI) after systematic prostate biopsy in the course of management of patients diagnosed with prostate cancer with active surveillance. We compared the CDRs based on targeted biopsy of PI-RADS 3 lesions that occurred (1) as solitary lesions, (2) as 1 of multiple PI-RADS 3 only lesions, or (3) with ≥ 1 higher grade lesion. RESULTS: Median age was 65.0 years (interquartile range 59.5-70.0), median prostate specific antigen was 5.95 ng/ml (interquartile range 4.30-8.83), and median prostate specific antigen density was 0.161 ng/ml2 (0.071-0.194). Forty-three men had solitary PI-RADS 3 lesions, 22 had multiple PI-RADS 3 only lesions, and 26 had multiple lesions with ≥ 1 higher grade lesion. The overall CDR (Gleason score ≥ 3 + 3) based on confirmatory MRI targeted biopsy in a given PI-RADS 3 lesion in each group was 23%, 45%, and 54%, respectively (P = 0.0274). The CDRs for clinically significant disease (Gleason score ≥ 3 + 4) were 16%, 32%, and 35%, respectively (P = 0.1701). CONCLUSIONS: Coexisting lesions increase the CDR of confirmatory MRI targeted biopsy of PI-RADS 3 lesions in patients managed with active surveillance. Risk stratification algorithms for PI-RADS 3 lesion to guide biopsy and management decisions may consider including multiplicity of lesions.

Role of CT imaging in left atrial appendage occlusion for the WATCHMAN™ device.

Computed tomography (CT) plays a key role in the peri-procedural planning of left atrial appendage occlusion (LAAO) device placement and post-procedural evaluation. The geometric variability of the interatrial septum, left atrium, and the left atrial appendage morphology can be fully visualized and intuitively appreciated through CT-derived, patient-specific 3D model unique to each individual's anatomy. This review further defines the strengths and limitations of CT peri-procedural imaging in the planning of LAAO.

Prostate volume measured by magnetic resonance imaging is not a predictor of lower urinary tract symptoms.

PURPOSE: Prostate volume is frequently utilized to counsel patients presenting to family medicine physicians with voiding complaints. We evaluated the relation between International Prostate Symptom Score (IPSS) and prostate volume measured by phased-array surface coil magnetic resonance imaging (MRI). METHODS: We performed an institutional review board (IRB)-approved retrospective study of all patients who received a prostate MRI between 2015 and 2017. Correlation between the overall IPSS, IPSS components, prostate volume stratified by prostate specific antigen (PSA) (<1.4 vs. ≥1.4 g/dL), and race (black vs. white) was examined. RESULTS: In all, 592 patients had prostate MRIs performed between 2015 and 2017. Two hundred and twenty-nine of these patients had IPSS and prostate volume information available in their medical records. The mean age of the cohort was 64.67 (SD = ±7.82) and mean PSA was 7.75 (SD = ±8.3). The mean IPSS was 9.77 (SD ± 7.2), and mean prostate volume was 55.88 cubic cm (SD = ±38.9). The correlation coefficient between prostate volume and IPSS was 0.12789 (P = 0.05). The correlation between prostate volume and IPSS was also not significant in 128 men with prostate volume above 40 cubic cm. Stratifying analysis by race and PSA showed no significant correlation between volume and IPSS. Analysis of the correlation between the different dimension of prostate volume and IPSS revealed significant but weak associations. CONCLUSIONS: Even with more precise estimation with MRI, prostate volume does not predict obstruction complaints. This finding is of importance when treating males presenting with voiding dysfunction to primary care.

Volumetric and Voxel-Wise Analysis of Dominant Intraprostatic Lesions on Multiparametric MRI.

Introduction: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying the dominant intraprostatic lesion (DIL) for radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 90 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted (T2W), apparent diffusion coefficient (ADC), and Ktrans images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. Ktrans, and ADC vs. Ktrans). The voxel wise spearman correlation was also obtained between these image pairs. Results: The DILs were located in the anterior fibromuscular stroma, central zone, peripheral zone, and transition zone in 35.2, 5.6, 32.4, and 25.4% of patients, respectively. Gleason grade groups 1-5 represented 29.6, 40.8, 15.5, and 14.1% of the study population, respectively (with group grades 4 and 5 analyzed together). The mean contour volumes for the T2W images, and the ADC and Ktrans maps were 2.14 ± 2.1, 2.22 ± 2.2, and 1.84 ± 1.5 mL, respectively. Ktrans values were indistinguishable between cancerous regions and the rest of prostatic regions for 19 patients. The Dice coefficient and Jaccard index were 0.74 ± 0.13, 0.60 ± 0.15 for T2W-ADC and 0.61 ± 0.16, 0.46 ± 0.16 for T2W-Ktrans. The voxel-based Spearman correlations were 0.20 ± 0.20 for T2W-ADC and 0.13 ± 0.25 for T2W-Ktrans. Conclusions: The DIL contoured on T2W images had a high level of agreement with those contoured on ADC maps, but there was little to no quantitative correlation of these results with tumor location and Gleason grade group. Technical hurdles are yet to be solved for precision radiotherapy to target the DILs based on physiological imaging. A Boolean sum volume (BSV) incorporating all available MR sequences may be reasonable in delineating the DIL boost volume.

Cardiac Substructure Segmentation and Dosimetry Using a Novel Hybrid Magnetic Resonance and Computed Tomography Cardiac Atlas.

PURPOSE: Radiation dose to the heart and cardiac substructures has been linked to cardiotoxicities. Because cardiac substructures are poorly visualized on treatment-planning computed tomography (CT) scans, we used the superior soft-tissue contrast of magnetic resonance (MR) imaging to optimize a hybrid MR/CT atlas for substructure dose assessment using CT. METHODS AND MATERIALS: Thirty-one patients with left-sided breast cancer underwent a T2-weighted MR imaging scan and noncontrast simulation CT scans. A radiation oncologist delineated 13 substructures (chambers, great vessels, coronary arteries, etc) using MR/CT information via cardiac-confined rigid registration. Ground-truth contours for 20 patients were inputted into an intensity-based deformable registration atlas and applied to 11 validation patients. Automatic segmentations involved using majority vote and Simultaneous Truth and Performance Level Estimation (STAPLE) strategies with 1 to 15 atlas matches. Performance was evaluated via Dice similarity coefficient (DSC), mean distance to agreement, and centroid displacement. Three physicians evaluated segmentation performance via consensus scoring by using a 5-point scale. Dosimetric assessment included measurements of mean heart dose, left ventricular volume receiving 5 Gy, and left anterior descending artery mean and maximum doses. RESULTS: Atlas approaches performed similarly well, with 7 of 13 substructures (heart, chambers, ascending aorta, and pulmonary artery) having DSC >0.75 when averaged over 11 validation patients. Coronary artery segmentations were not successful with the atlas-based approach (mean DSC <0.3). The STAPLE method with 10 matches yielded the highest DSC and the lowest mean distance to agreement for all high-performing substructures (omitting coronary arteries). For the STAPLE method with 10 matches, >50% of all validation contours had centroid displacements <3.0 mm, with the largest shifts in the coronary arteries. Atlas-generated contours had no statistical difference from ground truth for left anterior descending artery maximum dose, mean heart dose, and left ventricular volume receiving 5 Gy (P > .05). Qualitative contour grading showed that 8 substructures required minor modifications. CONCLUSIONS: The hybrid MR/CT atlas provided reliable segmentations of chambers, heart, and great vessels for patients undergoing noncontrast CT, suggesting potential widespread applicability for routine treatment planning.

Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis.

Purpose: The aim of this study was to identify and rank discriminant radiomics features extracted from MR multi-modal images to construct an adaptive model for characterization of Dominant Intra-prostatic Lesions (DILs) from normal prostatic gland tissues (NT). Methods and Materials: Two cohorts were retrospectively studied: Group A consisted of 98 patients and Group B 19 patients. Two image modalities were acquired using a 3.0T MR scanner: Axial T2 Weighted (T2W) and axial diffusion weighted (DW) imaging. A linear regression method was used to construct apparent diffusion coefficient (ADC) maps from DW images. DILs and the NT in the mirrored location were drawn on each modality. One hundred and sixty-eight radiomics features were extracted from DILs and NT. A Partial-Least-Squares-Correlation (PLSC) with one-way ANOVA along with bootstrapping ratio techniques were recruited to identify and rank the most discriminant latent variables. An artificial neural network (ANN) was constructed based on the optimal latent variable feature to classify the DILs and NTs. Nineteen patients were randomly chosen to test the contour variability effect on the radiomics analysis and the performance of the ANN. Finally, the trained ANN and a two dimension (2D) convolutional sampling method were combined and used to estimate DIL-NT probability map for two test cases. Results: Among 168 radiomics-based latent variables, only the first four variables of each modality in the PLSC space were found to be significantly different between the DILs and NTs. Area Under Receiver Operating Characteristic (AUROC), Positive Predictive and Negative Predictive values (PPV and NPV) for the conventional method were 94%, 0.95, and 0.92, respectively. When the feature vector was randomly permuted 10,000 times, a very strong permutation-invariant efficiency (p < 0.0001) was achieved. The radiomic-based latent variables of the NTs and DILs showed no statistically significant differences (Fstatistic < Fc = 4.11 with Confidence Level of 95% for all 8 variables) against contour variability. Dice coefficients between DIL-NT probability map and physician contours for the two test cases were 0.82 and 0.71, respectively. Conclusion: This study demonstrates the high performance of combining radiomics information extracted from multimodal MR information such as T2WI and ADC maps, and adaptive models to detect DILs in patients with PCa.

A propensity score matched analysis of the effects of African American race on the characteristics of regions of interests detected by magnetic resonance imaging of the prostate.

OBJECTIVES: To evaluate the effects of African American (AA) race on the number, location, Prostate Imaging Reporting and Data System (PI-RADS) score, cancer detection rate, and cancer upgrade rate of the regions of interest (ROI) discovered on mltiparametric magnetic resonance imaging (mp-MRI) of the prostate. METHODS: We performed an institutional retrospective study of 592 patients who received a prostate mp-MRI. Number of ROI (1-4), their location, and PI-RADS score v2 were evaluated in a matched cohort of Caucasian and AA males. Propensity score matching was performed using the variables of age, prostate specific antigen (PSA) level, and prostate volume. Comparisons utilized chi-square tests and P < 0.05 was considered significant. RESULTS: One hundred and twenty three AA patients were matched with an equal number of Caucasian men of similar characteristics. The AA population's median age was 63 years (57.3-69.3), median PSA 6.6 (4.6-12.1), and median prostate volume 55 ml (33-90.8). The Caucasian population's median age was 66.3 years (60.9-71.1), median PSA 5.4 (3.8-8), and median prostate volume 52.5 ml (33.2-83). The number of ROI was 2 or more in 24% of AA men and 12% of Caucasian men (P = 0.035), and 3 or more in 10% of AA and 2% of Caucasian men (P = 0.034). There was no significant difference in location, PI-RADS scores, cancer detection rate, and cancer upgrade rate of the ROI between the 2 groups. CONCLUSIONS: AA patients, as compared to Caucasian counterparts, have a higher number of ROI detected on prostate mp-MRI.