Outcomes of patients with non-small cell lung cancer and poor performance status treated with immune checkpoint inhibitors in the real-world setting

International Journal of Clinical Oncology - Immune-checkpoint inhibitors (ICIs) are standard treatments for metastatic non-small cell lung cancer (NSCLC). Patients with poor performance status...     

Dry eye in chronic stroke patients with hemiplegia: A cross-sectional study

ABSTRACT

Objective: Dry eye is reported to be associated with several neurological diseases. The aim of this study is to evaluate the patients with hemiplegia after stroke for dry eye and compare their results with a control group.

Materials and methods: Forty-five patients with hemiplegia and 45 individuals as the control group were included in the study. Tear function tests (Schirmer and tear breakup time) and a dry eye questionnaire for dry eye symptoms (ocular surface disease index) were performed and the results of the two groups were compared.

Results: Schirmer test results were significantly lower in the post-stroke hemiplegia group compared to the control group (11.3 ± 8.2 mm and 20.6 ± 11.6 mm, respectively, p < .001). Tear breakup time results were significantly lower in the post-stroke hemiplegia group compared to the control group (7.9 ± 3.1 s and 12.1 ± 4.3 s, respectively, p < .001). Ocular surface disease index scores were not significantly different between hemiplegia and control groups (21.6 ± 20.0 and 19.8 ± 13.9, respectively, p = .635). Schirmer scores lower than 10 mm (60% and 30%, p < .001) and tear breakup time results lower than 10 s (65.6% and 28.9%, p < .001) were also higher in the hemiplegia group compared to control group.

Conclusion: We found lower Schirmer test and tear breakup time results and similar OSDI scores in hemiplegia patients compared to controls. Hemiplegia patients may have dry eye without typical symptoms. This should be taken into consideration in the follow-up and rehabilitation of post-stroke hemiplegia patients.     

Treatment of classical Hodgkin’s lymphoma in children and adolescents

Introduction: The treatment of classical Hodgkin lymphoma (cHL) in children is a story of success. Nowadays, more than 90% of patients are cured and overall survival is nearly 100% at 5 years. Efforts have been made to avoid related effects of therapies; therefore, children are treated using different chemotherapy schemes in comparison with adults.

Areas covered: This review includes a view of the clinical classification and risk assessment in children suffering from HL. The chemotherapy more commonly employed is revisited. The use of PET/CT to evaluate the disease in order to guide therapy is analyzed. New options of chemotherapy and emerging immunotherapy are also included.

Expert opinion: In order to make the right treatment choice, a proper initial assessment of risk is mandatory. The choice of therapy in these kinds of patients must be done according to the experience of the team, and also, the cost and logistics related to the eligible scheme are very important. If possible, efforts must be made to include PET/CT in guiding therapy and avoiding overtreatment and long-term adverse effects in children. New options in immunotherapy are emerging and must be considered with caution in selected patients.     

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

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Matrix Metalloproteinases 2 and 9 Polymorphism in Patients With Myeloproliferative Diseases: A STROBE-Compliant Observational Study

Chronic myeloproliferative disorders such as polycythemia vera (PV), essential thrombocytosis (ET), and idiopathic myelofibrosis arise from clonal proliferation of neoplastic stem cells in the bone marrow. Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases that have potential to degrade all types of extracellular matrix (ECM) and also play a role in remodeling of the ECM. It is known that MMPs play a role in bone marrow remodeling.The primary goal of our study is to explore the relationship between chronic myeloproliferative diseases and some of MMP gene polymorphisms. The demonstration of a relationship will help to understand whether these polymorphisms may be a potential early diagnosis marker of the diseases.Patients were selected from outpatient clinics of Turgut Ozal University Hospital, Ankara, Turkey, between December 2010 and May 2011. Twenty-eight patients that previously diagnosed and followed-up with PV, 17 with secondary polycythemia (SP), and 12 with ET were enrolled in the study, along with a control group of 22 healthy people.DNA was isolated from peripheral blood. Using polymerase chain reaction–restriction fragment length polymorphism method, MMP2 and MMP9 gene polymorphisms were analyzed with agarose gel electrophoresis. There was a statistically significant difference between the study groups and the control group in terms of Gln279Arg polymorphisms rates of MMP9. The highest MMP9 Gln279Arg polymorphism rate was observed in the ET group. But nobody from the control group had polymorphic MMP9. There was no statistically significant difference between the groups in terms of MMP2-735 C > T polymorphism rates.In conclusion, MMP9 gene Gln279Arg polymorphism was associated with ET, SP, and PV diseases. Hence, we believe that these gene polymorphisms may play a role in the mechanism of bone marrow fibrosis and may be a factor that increases the risk of thrombosis. Illumination of the molecular basis of the relationship between MMP-thrombosis and MMP-fibrosis provides a better understanding of the pathophysiology of PV and ET diseases and will allow new approaches to diagnosis and treatment.     

Association of Early Kidney Allograft Failure with Preformed IgA Antibodies to β2-Glycoprotein I

In the current immunosuppressive therapy era, vessel thrombosis is the most common cause of early graft loss after renal transplantation. The prevalence of IgA anti–β₂-glycoprotein I antibodies (IgA-aB2GPI-ab) in patients on dialysis is elevated (>30%), and these antibodies correlate with mortality and cardiovascular morbidity. To evaluate the effect of IgA-aB2GPI-ab in patients with transplants, we followed all patients transplanted from 2000 to 2002 in the Hospital 12 de Octubre prospectively for 10 years. Presence of IgA-aB2GPI-ab in pretransplant serum was examined retrospectively. Of 269 patients, 89 patients were positive for IgA-aB2GPI-ab (33%; group 1), and the remaining patients were negative (67%; group 2). Graft loss at 6 months post-transplant was significantly higher in group 1 (10 of 89 versus 3 of 180 patients in group 2; P=0.002). The most frequent cause of graft loss was thrombosis of the vessels, which was observed only in group 1 (8 of 10 versus 0 of 3 patients in group 2; P=0.04). Multivariate analysis showed that the presence of IgA-aB2GPI-ab was an independent risk factor for early graft loss (P=0.04) and delayed graft function (P=0.04). There were no significant differences regarding patient survival between the two groups. Graft survival was similar in both groups after 6 months. In conclusion, patients with pretransplant IgA-aB2GPI-ab have a high risk of early graft loss caused by thrombosis and a high risk of delayed graft function. Therefore, pretransplant IgA-aB2GPI-ab may have a detrimental effect on early clinical outcomes after renal transplantation.     

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.