Deep Learning-based Prescription of Cardiac MRI Planes.

PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Advancing Cardiovascular MRI Acquisition Through Deep Convolutional Neural Network-Based Localization

Cardiac MRI is the gold standard for quantification of cardiac volumetry, function, and blood flow. Despite the wealth of information that may be gleamed from these acquisitions, its use has been limited primarily to academic and specialty clinics due to the need for specialty trained physicians and technologists required for planning of these scans.Recently, deep convolutional neural networks (DCNNs) have shown promise in automating various aspects of radiological workflows, such as landmark localization. However, a primary limitation to applying DCNNs to clinical practice include uncertainty of how well an algorithm will perform outside of the environment in which it was trained. Moreover, these systems are often seen as “black boxes”, which fail to provide an explanation of how an answer was achieved. Providing a way in which clinical end users may have confidence in these systems is therefore essential for clinical adoption of any medically focused DCNN system.With these concerns in mind, I explore the potential automating the planning of Cardiac MR imaging planes using DCNN. In the first chapter, I explore the potential of automating the prescription of long-axis and short axis imaging planes by localizing the landmarks. To preserve the iterability in the DCNN, I regress pseudoprobability heatmaps (termed heatmap regression) centered at the valve and apex landmarks. I demonstrate that this approach of heatmap regression not only accurately identifies the landmarks, it is additionally able to recreate imaging planes similar to those defined by the ground truth landmarks or those acquired by a technologist at the time of original acquisition.In my second chapter, I explore the potential to applying these DCNNs within a clinical setting. I first established the importance of our angulation metric for assessing the accuracy imaging plane. To assess the generalizability of this system to different clinical environments, I calculated the angulation error between ground truth defined and DCNN predicted imaging planes. As an additional level of comparison, angulation error was calculated for technologist acquired imaging planes. I found that this system of DCNNs generally achieved similar or better performance compared to a technologist.Finally, in my third chapter, I explore the potential of adapting my DCNN algorithm to different clinical environments, using the differences in imaging characteristics seen at 1.5T vs 3T as a model system. To achieve this, I developed a methodology for selecting cases with greatest model uncertainty for transfer learning. I moreover developed two novel uncertainty metrics based either strength of prediction or test-time augmentation spatial variance pseudoprobability maps. To assess the performance of this approach, I used a model trained on only 1.5T long-axis images, and calculated pseudoprobability metrics of 3T long-axis images. We assessed the potential of each pseudoprobability metric by ranking 3T long-axis images by either increasing, decreasing, or random values. I found that 3T images with the highest uncertainty most efficiently increased the transfer learning data-efficiency for the apex, consistent with a good uncertainty metric. Moreover, I found that incorporation of 1.5T data into the transfer learning process helped preserve the initial performance at 1.5T

Advancing Cardiovascular MRI Acquisition Through Deep Convolutional Neural Network-Based Localization

Cardiac MRI is the gold standard for quantification of cardiac volumetry, function, and blood flow. Despite the wealth of information that may be gleamed from these acquisitions, its use has been limited primarily to academic and specialty clinics due to the need for specialty trained physicians and technologists required for planning of these scans.Recently, deep convolutional neural networks (DCNNs) have shown promise in automating various aspects of radiological workflows, such as landmark localization. However, a primary limitation to applying DCNNs to clinical practice include uncertainty of how well an algorithm will perform outside of the environment in which it was trained. Moreover, these systems are often seen as “black boxes”, which fail to provide an explanation of how an answer was achieved. Providing a way in which clinical end users may have confidence in these systems is therefore essential for clinical adoption of any medically focused DCNN system.With these concerns in mind, I explore the potential automating the planning of Cardiac MR imaging planes using DCNN. In the first chapter, I explore the potential of automating the prescription of long-axis and short axis imaging planes by localizing the landmarks. To preserve the iterability in the DCNN, I regress pseudoprobability heatmaps (termed heatmap regression) centered at the valve and apex landmarks. I demonstrate that this approach of heatmap regression not only accurately identifies the landmarks, it is additionally able to recreate imaging planes similar to those defined by the ground truth landmarks or those acquired by a technologist at the time of original acquisition.In my second chapter, I explore the potential to applying these DCNNs within a clinical setting. I first established the importance of our angulation metric for assessing the accuracy imaging plane. To assess the generalizability of this system to different clinical environments, I calculated the angulation error between ground truth defined and DCNN predicted imaging planes. As an additional level of comparison, angulation error was calculated for technologist acquired imaging planes. I found that this system of DCNNs generally achieved similar or better performance compared to a technologist.Finally, in my third chapter, I explore the potential of adapting my DCNN algorithm to different clinical environments, using the differences in imaging characteristics seen at 1.5T vs 3T as a model system. To achieve this, I developed a methodology for selecting cases with greatest model uncertainty for transfer learning. I moreover developed two novel uncertainty metrics based either strength of prediction or test-time augmentation spatial variance pseudoprobability maps. To assess the performance of this approach, I used a model trained on only 1.5T long-axis images, and calculated pseudoprobability metrics of 3T long-axis images. We assessed the potential of each pseudoprobability metric by ranking 3T long-axis images by either increasing, decreasing, or random values. I found that 3T images with the highest uncertainty most efficiently increased the transfer learning data-efficiency for the apex, consistent with a good uncertainty metric. Moreover, I found that incorporation of 1.5T data into the transfer learning process helped preserve the initial performance at 1.5T

MiR-378 as a biomarker for response to anti-angiogenic treatment in ovarian cancer.

ObjectiveTo determine the role of miR-378 as a biomarker for anti-angiogenic therapy response in ovarian cancer.MethodsExpression of miR-378 was analyzed in ovarian cancer cell lines and human tumors vs. normal ovarian epithelial cells by qRT-PCR. After miR-378 transfection in SKOV3 cells, dysregulated genes were identified using microarray. Data from The Cancer Genome Atlas (TCGA) was utilized to correlate miR-378 expression with progression-free survival (PFS) among patients treated with anti-angiogenic therapy by using Kaplan-Meier and Cox proportional hazards.ResultsMiR-378 was overexpressed in ovarian cancer cells and tumors vs. normal ovarian epithelial cells. Overexpressing miR-378 in ovarian cancer cells altered expression of genes associated with angiogenesis (ALCAM, EHD1, ELK3, TLN1), apoptosis (RPN2, HIPK3), and cell cycle regulation (SWAP-70, LSM14A, RDX). In the TCGA dataset, low vs. high miR-378 expression was associated with longer PFS in a subset of patients with recurrent ovarian cancer treated with bevacizumab (9.2 vs. 4.2months; p=0.04). On multivariate analysis, miR-378 expression was an independent predictor for PFS after anti-angiogenic treatment (HR=2.04, 95% CI: 1.12-3.72; p=0.02). Furthermore, expression levels of two miR-378 targets (ALCAM and EHD1) were associated with PFS in this subgroup of patients who received anti-angiogenic therapy (9.4 vs. 4.2months, p=0.04 for high vs. low ALCAM; 7.9 vs. 2.3months, p<0.01 for low vs. high EHD1).ConclusionsOur data suggest that miR-378 is overexpressed in ovarian cancer cells and tumors vs. normal ovarian epithelial cells. MiR-378 and its downstream targets may serve as markers for response to anti-angiogenic therapy

MiR-378 as a biomarker for response to anti-angiogenic treatment in ovarian cancer.

ObjectiveTo determine the role of miR-378 as a biomarker for anti-angiogenic therapy response in ovarian cancer.MethodsExpression of miR-378 was analyzed in ovarian cancer cell lines and human tumors vs. normal ovarian epithelial cells by qRT-PCR. After miR-378 transfection in SKOV3 cells, dysregulated genes were identified using microarray. Data from The Cancer Genome Atlas (TCGA) was utilized to correlate miR-378 expression with progression-free survival (PFS) among patients treated with anti-angiogenic therapy by using Kaplan-Meier and Cox proportional hazards.ResultsMiR-378 was overexpressed in ovarian cancer cells and tumors vs. normal ovarian epithelial cells. Overexpressing miR-378 in ovarian cancer cells altered expression of genes associated with angiogenesis (ALCAM, EHD1, ELK3, TLN1), apoptosis (RPN2, HIPK3), and cell cycle regulation (SWAP-70, LSM14A, RDX). In the TCGA dataset, low vs. high miR-378 expression was associated with longer PFS in a subset of patients with recurrent ovarian cancer treated with bevacizumab (9.2 vs. 4.2months; p=0.04). On multivariate analysis, miR-378 expression was an independent predictor for PFS after anti-angiogenic treatment (HR=2.04, 95% CI: 1.12-3.72; p=0.02). Furthermore, expression levels of two miR-378 targets (ALCAM and EHD1) were associated with PFS in this subgroup of patients who received anti-angiogenic therapy (9.4 vs. 4.2months, p=0.04 for high vs. low ALCAM; 7.9 vs. 2.3months, p<0.01 for low vs. high EHD1).ConclusionsOur data suggest that miR-378 is overexpressed in ovarian cancer cells and tumors vs. normal ovarian epithelial cells. MiR-378 and its downstream targets may serve as markers for response to anti-angiogenic therapy

Robotic versus laparoscopic versus open surgery in morbidly obese endometrial cancer patients - A comparative analysis of total charges and complication rates

Objective To compare the complications and charges of robotic vs. laparoscopic vs. open surgeries in morbidly obese patients treated for endometrial cancer. Methods Data were obtained from the Nationwide Inpatient Sample from 2011. Chi-squared, Wilcoxon rank sum two-sample tests, and multivariate analyses were used for statistical analyses. Results Of 1087 morbidly obese (BMI ≥ 40 kg/m2) endometrial cancer patients (median age: 59 years, range: 22 to 89), 567 (52%) had open surgery (OS), 98 (9%) laparoscopic (LS), and 422 (39%) robotic surgery (RS). 23% of OS, 13% of LS, and 8% of RS patients experienced an intraoperative or postoperative complication including: blood transfusions, mechanical ventilation, urinary tract injury, gastrointestinal injury, wound debridement, infection, venous thromboembolism, and lymphedema (p \u3c 0.0001). RS and LS patients were less likely to receive blood transfusions compared to OS (5% and 6% vs. 14%, respectively; p \u3c 0.0001). The median lengths of hospitalization for OS, LS, and RS patients were 4, 1, and 1 days, respectively (p \u3c 0.0001). Median total charges associated with OS, LS, and RS were $39,281,$40,997, and $45,030 (p = 0.037), respectively. Conclusions In morbidly obese endometrial cancer patients, minimally invasive robotic or laparoscopic surgeries were associated with fewer complications and less days of hospitalization relative to open surgery. Compared to laparoscopic approach, robotic surgeries had comparable rates of complications but higher charges

Bevacizumab in treatment of high-risk ovarian cancer-a cost-effectiveness analysis

Objective. The objective of this study was to evaluate a cost-effectiveness strategy of bevacizumab in a subset of high-risk advanced ovarian cancer patients with survival benefit. Methods. A subset analysis of the International Collaboration on Ovarian Neoplasms 7 trial showed that additions of bevacizumab (B) and maintenance bevacizumab (mB) to paclitaxel (P) and carboplatin (C) improved the overall survival (OS) of high-risk advanced cancer patients. Actual and estimated costs of treatment were determined from Medicare payment. Incremental cost-effectiveness ratio per life-year saved was established. Results. The estimated cost of PC is $535 per cycle; PCB + mB (7.5 mg/kg) is$3,760 per cycle for the first 6 cycles and then $3,225 per cycle for 12 mB cycles. Of 465 high-risk stage IIIC (\u3e1 cm residual) or stage IV patients, the previously reported OS after PC was 28.8 months versus 36.6 months in those who underwent PCB + mB. With an estimated 8-month improvement in OS, the incremental cost-effectiveness ratio of B was$167,771 per life-year saved. Conclusion. In this clinically relevant subset of women with high-risk advanced ovarian cancer with overall survival benefit after bevacizumab, our economic model suggests that the incremental cost of bevacizumab was approximately $170,000. © AlphaMed Press 2014