The proteome microenvironment determines the protective effect of preconditioning in cisplatin-induced acute kidney injury

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A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

What are the requirements for patient-centered breast imaging? Communication preferences of mammography patients in Turkey

Abstract

We have investigated communication preferences of mammography results in 90 patients through a structured interview approach. About 81% of patients expressed that they wanted to get the results, and 18% expressed that getting the results does not help if they are incomprehensible. In patients who want to get the results, 80% preferred face-to-face interaction with physicians, whereas the others preferred other modes of communication to prevent loss of time. Majority of patients infavor of face-to-face interaction (57%) preferred both the referring physician and the radiologist. Comprehensibility and fast delivery of reports, plus direct communication with radiologists are the requirements in mammography patients while implementing patient-centered radiology.     

Are Dermatophytid Reactions in Patients with Kerion Celsi Much More Common Than Previously Thought? A Prospective Study

Dermatophytid reactions are secondary eruptions in response to dermatophytosis. Only a few cases demonstrating an association between dermatophytid reactions and tinea capitis have been reported. Dermatophytid reactions were evaluated in patients diagnosed with kerion celsi. Patients admitted to the dermatology clinic of Van Regional Training and Research Hospital between November 22, 2012, and July 1, 2013, diagnosed with kerion celsi were evaluated for dermatophytid reactions. Six girls (32%) and 13 boys (68%) were included in this study. Dermatophytid reactions were detected in 13 of the 19 patients (68%). Seven patients (36.84%) had eczematous patches or plaques and three (15.8%) had papules. Eczematous lesions, papules, and pustules were noted in two patients (10.5%) and one (5.3%) had signs of an angioedema‐like reaction. Dermatophytid reactions in all patients were observed before the initiation of therapy. According to our clinical experiences, dermatophytid reactions in patients with kerion celsi were more common than reported. Eczematous scaly patches or plaques were the most frequently seen forms of dermatophytid in patients with kerion celsi. Dermatophytid reactions may occur before or after initiation of systemic antifungal therapy. Recognition of this reaction is important so that dermatophytids can be distinguished from drug reactions and the decision can be made whether to continue or to stop the systemic antifungal treatment.     

The effect of metoclopramide on gastric emptying in traumatic brain injury.

OBJECTIVE: Gastric paresis in traumatic brain injury (TBI) hinders the effectiveness of enteral support in this patient group. In this study we have investigated the effect of metoclopramide on gastric emptying in TBI patients. METHOD: In this prospective, randomized, controlled, double-blind study, 19 TBI patients with Glasgow Coma Scale scores of 3-11 were included. In all patients, enteral nutrition was commenced with a nasogastric feeding tube within 48 hours of trauma. Patients were randomized into two groups. In the metoclopramide (M) group, 10 mg metoclopramide was delivered intravenously three times daily for 5 days. In the control (C) group, an equal volume of saline was administered. Besides demographics, gastric emptying according to a paracetamol absorption test at days 0 and 5, time to reach target nutritional requirements, gastric residues, intolerance to feeding, nutritional complications, and clinical outcomes were recorded for each patient. RESULTS: The gastric residue rates were 2.7+/-7.4 mL and 8.1+/-17.7 mL per 100 patient days for groups C and M respectively (p=0.408). Similarly, feeding intolerance and complication rates did not significantly differ between groups C and M, (respectively p=0.543 and 0.930). Gastric emptying parameters also were similar between the study groups. CONCLUSION: We were unable to document any advantage to using metoclopramide in TBI patients. Simple intragastric enteral feeding with close monitoring of the possible complications seems to be sufficient with acceptable morbidity rates.     

Alveolar hydatid disease of the liver: Brief review and spectrum of adjacent organ invasion

The purpose of this study is to show the spectrum of adjacent organ invasion and to make a brief review of hepatic alveolar hydatid disease (AHD), using CT and MR imaging. We retrospectively reviewed CT and MR images of three patients with various adjacent organ invasions surgically and histologically proven to be AHD. Local invasion to right kidney and adrenal, right hemidiaphragm and lung were detected in one patient, right adrenal in another patient and gall bladder, duodenum, gastric wall and pancreas invasion in the other. AHD may rarely extend to the gall bladder, stomach, duodenum, pancreas, right adrenal and kidney, diaphragm, pleura and lung. The extension of the disease outside the liver is usually encountered in patients with large, peripherally located masses in the advanced stage of the disease.