Fibronectin Biosynthesis in the Rat Aorta In Vitro Changes Due to Experimental Hypertension

This study was undertaken to determine if changes in fibronectin biosynthesis accompany the phenotypic changes that occur in aortic tissue following experimental hypertension. An in vitro procedure was developed to measure fibronectin synthesis in aortic rings obtained from normotensive or hypertensive rats. There was a three to sixfold increase in fibronectin biosynthesis by aortic rings taken from rats treated with deoxycorticosterone/salt for 7 and 21 d, the change being more pronounced at 21 d. In contrast, there was no major change at either time point in net incorporation into total protein. Studies comparing fibronectin biosynthesis in aortic rings from Wistar rats and spontaneously hypertensive rats at ages between 10 and 40 wk showed increased fibronectin biosynthesis in older animals of both strains, but only slight differences between strains. Studies using rats infused with angiotensin II showed a correlation between blood pressure elevation and increased aortic fibronectin biosynthesis. Western blot analysis of aortic extracts showed that the fibronectin content was increased in the hypertensive models. The in vitro procedure for measuring fibronectin biosynthesis appears to provide a reliable reflection of in vivo changes in fibronectin expression, and the methodology could prove useful for studying the factors influencing protein expression in vascular tissue. (J. Clin. Invest. 1991. 88:1182-1189.) Key words: angiotensin * vascular * extracellular matrix * immunoprecipitatio

Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks

PurposeSegmentation of multiple organs-at-risk (OARs) is essential for magnetic resonance (MR)-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency.MethodsWe developed Automated deep Learning-based abdominal multiorgan segmentation (ALAMO) technique based on two-dimensional U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance.ResultsThe ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within 1 min for a three-dimensional volume of 320 × 288 × 180. Overall, the ALAMO model matched the state-of-the-art techniques in performance.ConclusionThe proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands

Prospective Pilot Trial to Evaluate a High Resolution Diffusion-Weighted MRI in Prostate Cancer Patients.

ObjectivesHigh-resolution prostate imaging may allow for detection of subtle changes in tumor size, decrease the reliance on biopsies, and help define tumor boundaries during ablation. This pilot clinical trial evaluates a novel high-resolution prostate MRI for detection of small, biopsy-proven prostate tumors.MethodsOur team developed a software that can be loaded on any modern MRI to generate high resolution diffusion-weighted imaging sequences (HR-DWI), which were compared to standard diffusion-weighted imaging sequence (S-DWI) in a prospective pilot trial in active surveillance patients. HR-DWI captures the entire volume of the prostate rather than sections, reducing streaking artifacts and geometric distortions. Multiple shots, rather than single shots, are used to differentiate signal and noise, enhancing resolution. All images were read by two radiologists. The primary outcome was the percent of biopsy-proven zones seen in 17 patients. The trial was powered to detect discordant proportions of 0.04 and 0.40 at one-sided alpha=0.05.ResultsThe resolution was defined using standard phantoms. HR-DWI produced a 5-fold improvement in spatial resolution when compared to S-DWI. Multiparametric (MP)-MRI incorporating S-DWI was useful for predicting biopsy results (AUC 0.72, Fisher's exact p<0.001); however, using HR-DWI allowed MP-MRI to be more highly predictive of biopsy results (AUC 0.88, Fisher's exact p<0.001). AUC for MP-MRI incorporating HR-DWI was significantly larger than MP-MRI incorporating S-DWI (p=0.002). MP-MRI with HR-DWI had a sensitivity of 95.7% and identified tumor in 22 of 23 zones proven to have cancer on biopsy. In contrast, MP-MRI with S-DWI had a sensitivity of 60.9% and only identified 14 of 23 biopsy-positive zones (p=0.004).ConclusionWe developed a novel DWI and evaluated its improved resolution in a clinical setting. This technology has many potential applications and should be evaluated in future clinical trials as a patient management tool

Prospective Pilot Trial to Evaluate a High Resolution Diffusion-Weighted MRI in Prostate Cancer Patients.

ObjectivesHigh-resolution prostate imaging may allow for detection of subtle changes in tumor size, decrease the reliance on biopsies, and help define tumor boundaries during ablation. This pilot clinical trial evaluates a novel high-resolution prostate MRI for detection of small, biopsy-proven prostate tumors.MethodsOur team developed a software that can be loaded on any modern MRI to generate high resolution diffusion-weighted imaging sequences (HR-DWI), which were compared to standard diffusion-weighted imaging sequence (S-DWI) in a prospective pilot trial in active surveillance patients. HR-DWI captures the entire volume of the prostate rather than sections, reducing streaking artifacts and geometric distortions. Multiple shots, rather than single shots, are used to differentiate signal and noise, enhancing resolution. All images were read by two radiologists. The primary outcome was the percent of biopsy-proven zones seen in 17 patients. The trial was powered to detect discordant proportions of 0.04 and 0.40 at one-sided alpha=0.05.ResultsThe resolution was defined using standard phantoms. HR-DWI produced a 5-fold improvement in spatial resolution when compared to S-DWI. Multiparametric (MP)-MRI incorporating S-DWI was useful for predicting biopsy results (AUC 0.72, Fisher's exact p<0.001); however, using HR-DWI allowed MP-MRI to be more highly predictive of biopsy results (AUC 0.88, Fisher's exact p<0.001). AUC for MP-MRI incorporating HR-DWI was significantly larger than MP-MRI incorporating S-DWI (p=0.002). MP-MRI with HR-DWI had a sensitivity of 95.7% and identified tumor in 22 of 23 zones proven to have cancer on biopsy. In contrast, MP-MRI with S-DWI had a sensitivity of 60.9% and only identified 14 of 23 biopsy-positive zones (p=0.004).ConclusionWe developed a novel DWI and evaluated its improved resolution in a clinical setting. This technology has many potential applications and should be evaluated in future clinical trials as a patient management tool

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

PurposeTo develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.MethodsTwenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.ResultsThe T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).ConclusionA deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans

Aortic Size Assessment by Noncontrast Cardiac Computed Tomography: Normal Limits by Age, Gender, and Body Surface Area

ObjectivesTo determine normal limits for ascending and descending thoracic aorta diameters in a large population of asymptomatic, low-risk adult subjects.BackgroundAssessment of aortic size is possible from gated noncontrast computed tomography (CT) scans obtained for coronary calcium measurements. However, normal limits for aortic size by these studies have yet to be defined.MethodsIn 4,039 adult patients undergoing coronary artery calcium (CAC) scanning, systematic measurements of the ascending and descending thoracic aorta diameters were made at the level of the pulmonary artery bifurcation. Multiple linear regression analysis was used to detect risk factors independently associated with ascending and descending thoracic aorta diameter and exclude subjects with these parameters from the final analysis. The final analysis groups for ascending and descending thoracic aorta included 2,952 and 1,931 subjects, respectively. Subjects were then regrouped by gender, age, and body surface area (BSA) for ascending and descending aorta, separately, and for each group, the mean, standard deviation, and upper normal limit were calculated for aortic diameter as well as for the calculated cross-sectional aortic area. Also, linear regression models were used to create BSA versus aortic diameter nomograms by age groups, and a formula for calculating predicted aortic size by age, gender, and BSA was created.ResultsAge, BSA, gender, and hypertension were directly associated with thoracic aorta dimensions. Additionally, diabetes was associated with ascending aorta diameter, and smoking was associated with descending aorta diameter. The mean diameters for the final analysis group were 33 ± 4 mm for the ascending and 24 ± 3 mm for the descending thoracic aorta, respectively. The corresponding upper limits of normal diameters were 41 and 30 mm, respectively.ConclusionsNormal limits of ascending and descending aortic dimensions by noncontrast gated cardiac CT have been defined by age, gender, and BSA in a large, low-risk population of subjects undergoing CAC scanning