Abiraterone acetate plus prednisolone with or without enzalutamide for patients with metastatic prostate cancer starting androgen deprivation therapy: final results from two randomised phase 3 trials of the STAMPEDE platform protocol.

BACKGROUND: Abiraterone acetate plus prednisolone (herein referred to as abiraterone) or enzalutamide added at the start of androgen deprivation therapy improves outcomes for patients with metastatic prostate cancer. Here, we aimed to evaluate long-term outcomes and test whether combining enzalutamide with abiraterone and androgen deprivation therapy improves survival. METHODS: We analysed two open-label, randomised, controlled, phase 3 trials of the STAMPEDE platform protocol, with no overlapping controls, conducted at 117 sites in the UK and Switzerland. Eligible patients (no age restriction) had metastatic, histologically-confirmed prostate adenocarcinoma; a WHO performance status of 0-2; and adequate haematological, renal, and liver function. Patients were randomly assigned (1:1) using a computerised algorithm and a minimisation technique to either standard of care (androgen deprivation therapy; docetaxel 75 mg/m2 intravenously for six cycles with prednisolone 10 mg orally once per day allowed from Dec 17, 2015) or standard of care plus abiraterone acetate 1000 mg and prednisolone 5 mg (in the abiraterone trial) orally or abiraterone acetate and prednisolone plus enzalutamide 160 mg orally once a day (in the abiraterone and enzalutamide trial). Patients were stratified by centre, age, WHO performance status, type of androgen deprivation therapy, use of aspirin or non-steroidal anti-inflammatory drugs, pelvic nodal status, planned radiotherapy, and planned docetaxel use. The primary outcome was overall survival assessed in the intention-to-treat population. Safety was assessed in all patients who started treatment. A fixed-effects meta-analysis of individual patient data was used to compare differences in survival between the two trials. STAMPEDE is registered with ClinicalTrials.gov (NCT00268476) and ISRCTN (ISRCTN78818544). FINDINGS: Between Nov 15, 2011, and Jan 17, 2014, 1003 patients were randomly assigned to standard of care (n=502) or standard of care plus abiraterone (n=501) in the abiraterone trial. Between July 29, 2014, and March 31, 2016, 916 patients were randomly assigned to standard of care (n=454) or standard of care plus abiraterone and enzalutamide (n=462) in the abiraterone and enzalutamide trial. Median follow-up was 96 months (IQR 86-107) in the abiraterone trial and 72 months (61-74) in the abiraterone and enzalutamide trial. In the abiraterone trial, median overall survival was 76·6 months (95% CI 67·8-86·9) in the abiraterone group versus 45·7 months (41·6-52·0) in the standard of care group (hazard ratio [HR] 0·62 [95% CI 0·53-0·73]; p<0·0001). In the abiraterone and enzalutamide trial, median overall survival was 73·1 months (61·9-81·3) in the abiraterone and enzalutamide group versus 51·8 months (45·3-59·0) in the standard of care group (HR 0·65 [0·55-0·77]; p<0·0001). We found no difference in the treatment effect between these two trials (interaction HR 1·05 [0·83-1·32]; pinteraction=0·71) or between-trial heterogeneity (I2 p=0·70). In the first 5 years of treatment, grade 3-5 toxic effects were higher when abiraterone was added to standard of care (271 [54%] of 498 vs 192 [38%] of 502 with standard of care) and the highest toxic effects were seen when abiraterone and enzalutamide were added to standard of care (302 [68%] of 445 vs 204 [45%] of 454 with standard of care). Cardiac causes were the most common cause of death due to adverse events (five [1%] with standard of care plus abiraterone and enzalutamide [two attributed to treatment] and one (<1%) with standard of care in the abiraterone trial). INTERPRETATION: Enzalutamide and abiraterone should not be combined for patients with prostate cancer starting long-term androgen deprivation therapy. Clinically important improvements in survival from addition of abiraterone to androgen deprivation therapy are maintained for longer than 7 years. FUNDING: Cancer Research UK, UK Medical Research Council, Swiss Group for Clinical Cancer Research, Janssen, and Astellas.

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences.

Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In Training.

A 68-year-old woman with a history of hepatocellular carcinoma underwent conventional transarterial chemoembolization. Manual tumor segmentation on images, which can be used to assess disease progression, is time consuming and may suffer from interobserver reliability issues. The authors present a how-to guide to develop machine learning algorithms for fully automatic segmentation of hepatocellular carcinoma and other tumors for lesion tracking over time.

Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm.

OBJECTIVE: Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans. METHODS: All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity. RESULTS: A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively. CONCLUSIONS: In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.

Risk-adjustment methods for all-payer comparative performance reporting in Vermont.

BACKGROUND: As the emphasis in health reform shifts to value-based payments, especially through multi-payer initiatives supported by the U.S. Center for Medicare & Medicaid Innovation, and with the increasing availability of statewide all-payer claims databases, the need for an all-payer, "whole-population" approach to facilitate the reporting of utilization, cost, and quality measures has grown. However, given the disparities between the different populations served by Medicare, Medicaid, and commercial payers, risk-adjustment methods for addressing these differences in a single measure have been a challenge. METHODS: This study evaluated different levels of risk adjustment for primary care practice populations - from basic adjustments for age and gender to a more comprehensive "full model" risk-adjustment method that included additional demographic, payer, and health status factors. It applied risk adjustment to populations attributed to patient-centered medical homes (283,153 adult patients and 78,162 pediatric patients) in the state of Vermont that are part of the Blueprint for Health program. Risk-adjusted expenditure and utilization outcomes for calendar year 2014 were reported in 102 adult and 56 pediatric primary-care comparative practice profiles. RESULTS: Using total expenditures as the dependent variable for the adult population, the r2 for the model adjusted for age and gender was 0.028. It increased to 0.265 with the additional adjustment for 3M Clinical Risk Groups and to 0.293 with the full model. For the adult population at the practice level, the no-adjustment model had the highest variation as measured by the coefficient of variation (18.5) compared to the age and gender model (14.8); the age, gender, and CRG model (13.0); and the full model (11.7). Similar results were found for the pediatric population practices. CONCLUSIONS: Results indicate that more comprehensive risk-adjustment models are effective for comparing cost, utilization, and quality measures across multi-payer populations. Such evaluations will become more important for practices, many of which do not distinguish their patients by payer type, and for the implementation of incentive-based or alternative payment systems that depend on "whole-population" outcomes. In Vermont, providers, accountable care organizations, policymakers, and consumers have used Blueprint profiles to identify priorities and opportunities for improving care in their communities.

Magnetization transfer contrast-suppressed imaging of amide proton transfer and relayed nuclear overhauser enhancement chemical exchange saturation transfer effects in the human brain at 7T.

PURPOSE: To use the variable delay multipulse (VDMP) chemical exchange saturation transfer (CEST) approach to obtain clean amide proton transfer (APT) and relayed Nuclear Overhauser enhancement (rNOE) CEST images in the human brain by suppressing the conventional magnetization transfer contrast (MTC) and reducing the direct water saturation contribution. METHODS: The VDMP CEST scheme consists of a train of RF pulses with a specific mixing time. The CEST signal with respect to the mixing time shows distinguishable characteristics for protons with different exchange rates. Exchange rate filtered CEST images are generated by subtracting images acquired at two mixing times at which the MTC signals are equal, while the APT and rNOE-CEST signals differ. Because the subtraction is performed at the same frequency offset for each voxel and the CEST signals are broad, no B0 correction is needed. RESULTS: MTC-suppressed APT and rNOE-CEST images of human brain were obtained using the VDMP method. The APT-CEST data show hyperintensity in gray matter versus white matter, whereas the rNOE-CEST images show negligible contrast between gray and white matter. CONCLUSION: The VDMP approach provides a simple and rapid way of recording MTC-suppressed APT-CEST and rNOE-CEST images without the need for B0 field correction.

Magnetic susceptibility contrast variations in multiple sclerosis lesions.

PURPOSE: Recent magnetic resonance imaging (MRI) studies have revealed heterogeneous magnetic susceptibility contrasts in multiple sclerosis (MS) lesions. Due to its sensitivity to disease-related iron and myelin changes, magnetic susceptibility-based measures may better reflect some pathological features of MS lesions. Hence, we sought to characterize MS lesions using combined R2* mapping and quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: In all, 306 MS lesions were selected from 24 MS patients who underwent 7T MRI. Maps of R2*, frequency, and quantitative susceptibility were calculated using acquired multiecho gradient echo (GRE) phase data. Lesions were categorized based on their image intensity or their anatomical locations. R2* and susceptibility values were quantified in each lesion based on manually drawn lesion masks and compared between lesion groups showing different contrast patterns. Correlations between R2* and susceptibility were also tested in these lesion groups. RESULTS: In 38% of selected lesions the frequency map did not show the same contrast pattern as the susceptibility map. While most lesions (93%) showed hypointensity on R2*, the susceptibility contrast in lesions varied, with 40% being isointense and 58% being hyperintense in the lesion core. Significant correlations (r = 0.31, P < 0.001) between R2* and susceptibility were found in susceptibility hyperintense lesions, but not in susceptibility isointense lesions. In addition, a higher proportion (74%) of periventricular lesions was found to be susceptibility hyperintense as compared to subcortical (53%) or juxtacortical (38%) lesions. CONCLUSION: Combining R2* and QSM is useful to characterize heterogeneity in MS lesions.

Whole-brain amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging in glioma patients using low-power steady-state pulsed chemical exchange saturation transfer (CEST) imaging at 7T.

PURPOSE: To explore the relationship of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) signal intensities with respect to different World Health Organization (WHO) brain tumor grades (II to IV) at 7T. MATERIALS AND METHODS: APT-based and NOE-based signals at 7T using low-power steady-state chemical exchange saturation transfer (CEST) were compared among de novo primary gliomas of different WHO grades (II to IV). The quantitative APT and NOE signals, calculated by fitting approach using extrapolated semisolid MT reference (EMR) signals, were compared with the magnetization transfer ratio asymmetry (MTRasym ) analysis, commonly used in APT-weighted MRI. RESULTS: The observed NOE signals of all glioma grades were significantly lower than normal brain tissue (P < 0.01). NOE signals significantly differed between low-grade (II) gliomas and high-grade (III, IV) gliomas (P < 0.05). APT signals showed no difference between the tumor regions for any glioma grades (M = 3.08%, 2.64%, and 3.10%, 95% confidence interval [CI] = 2.81% ∼ 3.33%, 2.36% ∼ 2.91%, and 2.85% ∼ 3.36% for grade II, III, and IV, respectively), and between normal brain tissue and all glioma grades (P = 0.08, M = 4.29% and 2.94%, 95% CI = 3.57% ∼ 4.99% and 2.47% ∼ 3.41% for normal and average grade II, III, and IV), while MTRasym differed significantly between normal tissue and all glioma grades (P < 0.05). CONCLUSION: NOE contributes substantially to APT-weighted MRI at 7T at low RF saturation power and provides a promising biomarker for glioma grading.J. Magn. Reson. Imaging 2016;44:41-50.

Measurement of arteriolar blood volume in brain tumors using MRI without exogenous contrast agent administration at 7T.

PURPOSE: Arteriolar cerebral-blood-volume (CBVa) is an important perfusion parameter that can be measured using inflow-based vascular-space-occupancy (iVASO) MRI without exogenous contrast agent administration. The purpose of this study is to assess the potential diagnostic value of CBVa in brain tumor patients by comparing it with total-CBV (including arterial, capillary and venous vessels) measured by dynamic-susceptibility-contrast (DSC) MRI. MATERIALS AND METHODS: Twelve brain tumor patients were scanned using iVASO (on 7T as part of a research project) and DSC (on 3T as part of routine clinical protocols) MRI. Region-of-interest analysis was performed to compare the resulting perfusion measures between tumoral and contralateral regions, and to evaluate their associations with tumor grades. RESULTS: CBVa measured by iVASO MRI significantly correlated with WHO grade (ρ = 0.37, P = 0.04). Total-CBV measured by DSC MRI showed a trend of correlation with WHO grade (ρ = 0.28, P = 0.5). The signal-to-noise ratio was comparable (P > 0.1) between the two methods, while the contrast-to-noise ratio between tumoral and contralateral regions was higher in iVASO-CBVa than DSC-CBV in WHO II/III patients (P < 0.05) but comparable in WHO IV patients (P > 0.1). A trend of positive correlation between DSC-CBV and iVASO-CBVa was observed (R2 = 0.28, P = 0.07). CONCLUSION: In this initial patient study, CBVa demonstrated a stronger correlation with WHO grade than total-CBV. Further investigation with a larger cohort is warranted to validate whether CBVa can be a better classifier than total-CBV for the stratification of brain tumors, and whether iVASO MRI can be a useful alternative method for the assessment of tumor perfusion, especially when exogenous contrast agent administration is difficult in certain patient populations. J. Magn. Reson. Imaging 2016;44:1244-1255.

Involvement of the renin-angiotensin system in the development of nephrogenic systemic fibrosis-like lesions in the RenTag mouse model.

BACKGROUND: Renin-angiotensin system (RAS) activation increases angiotensin II production stimulating profibrotic factors, especially in the setting of chronic kidney disease. Nephrogenic systemic fibrosis (NSF) has been associated with gadolinium (Gd) exposure and renal failure. RAS involvement in NSF is unclear compared to transforming growth factor beta and Smad. RenTag mice were chosen to investigate the role of RAS in NSF-like dermal fibrosis because they demonstrated dermal fibrosis at birth, perturbations of RAS in subcutaneous tissue, and renal failure within 4 weeks of age. METHODS: Wild-type and RenTag mice were injected weekly with a supratherapeutic dose of intravenous gadodiamide (3.0 mmol/kg body weight) and killed at 12 weeks of age for skin and kidney histology. RESULTS: RenTag mice had elevated BUN levels, pitted kidneys, and glomerular damage. RenTag mice skin revealed an increased density of fibroblasts, no mucopolysaccharide deposits, and increased collagen fibril density regardless of Gd exposure. Skin and kidney histopathology of wild-type mice were normal regardless of Gd exposure. CD34 positivity was higher in RenTag compared to wild-type. CONCLUSIONS: Since RenTag dermal lesions remained unchanged after gadolinium exposure in the setting of renal failure, this animal model suggests perturbations of subcutaneous RAS may be involved in Gd-naïve dermal fibrosis.