Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography

Carrying out clinical diagnosis of retinal vascular degeneration using Fluorescein Angiography (FA) is a time consuming process and can pose significant adverse effects on the patient. Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal. Currently, there are no non-invasive systems capable of generating Fluorescein Angiography images. However, retinal fundus photography is a non-invasive imaging technique that can be completed in a few seconds. In order to eliminate the need for FA, we propose a conditional generative adversarial network (GAN) to translate fundus images to FA images. The proposed GAN consists of a novel residual block capable of generating high quality FA images. These images are important tools in the differential diagnosis of retinal diseases without the need for invasive procedure with possible side effects. Our experiments show that the proposed architecture outperforms other state-of-the-art generative networks. Furthermore, our proposed model achieves better qualitative results indistinguishable from real angiograms.Comment: 14 pages, Accepted to 15th International Symposium on Visual Computing 202

Pulmonary Biomarkers Based on Alterations in Protein Expression after Exposure to Arsenic

OBJECTIVE: Environmental exposure to arsenic results in multiple adverse effects in the lung. Our objective was to identify potential pulmonary protein biomarkers in the lung-lining fluid of mice chronically exposed to low-dose As and to validate these protein changes in human populations exposed to As. METHODS: Mice were administered 10 or 50 ppb As (sodium arsenite) in their drinking water for 4 weeks. Proteins in the lung-lining fluid were identified using two-dimensional gel electrophoresis (n = 3) or multidimensional protein identification technology (MUDPIT) (n = 2) coupled with mass spectrometry. Lung-induced sputum samples were collected from 57 individuals (tap water As ranged from ~ 5 to 20 ppb). Protein levels in sputum were determined by ELISA, and As species were analyzed in first morning void urine. RESULTS: Proteins in mouse lung-lining fluid whose expression was consistently altered by As included glutathione-S-transferase (GST)-omega-1, contraspin, apolipoprotein A-I and A-IV, enolase-1, peroxiredoxin-6, and receptor for advanced glycation end products (RAGE). Validation of the putative biomarkers was carried out by evaluating As-induced alterations in RAGE in humans. Regression analysis demonstrated a significant negative correlation (p = 0.016) between sputum levels of RAGE and total urinary inorganic As, similar to results seen in our animal model. CONCLUSION: Combinations of proteomic analyses of animal models followed by specific analysis of human samples provide an unbiased determination of important, previously unidentified putative biomarkers that may be related to human disease

A bayesian meta-analysis of multiple treatment comparisons of systemic regimens for advanced pancreatic cancer

© 2014 Chan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: For advanced pancreatic cancer, many regimens have been compared with gemcitabine (G) as the standard arm in randomized controlled trials. Few regimens have been directly compared with each other in randomized controlled trials and the relative efficacy and safety among them remains unclear

Clinical actionability of comprehensive genomic profiling for management of rare or refractory cancers

Background. The frequency with which targeted tumor sequencing results will lead to implemented change in care is unclear. Prospective assessment of the feasibility and limitations of using genomic sequencing is critically important. Methods. A prospective clinical study was conducted on 100 patients with diverse-histology, rare, or poor-prognosis cancers to evaluate the clinical actionability of a Clinical Laboratory Improvement Amendments (CLIA)-certified, comprehensive genomic profiling assay (FoundationOne), using formalin-fixed, paraffin-embedded tumors. The primary objectives were to assess utility, feasibility, and limitations of genomic sequencing for genomically guided therapy or other clinical purpose in the setting of a multidisciplinary molecular tumor board. Results. Of the tumors from the 92 patients with sufficient tissue, 88 (96%) had at least one genomic alteration (average 3.6, range 0–10). Commonly altered pathways included p53 (46%), RAS/RAF/MAPK (rat sarcoma; rapidly accelerated fibrosarcoma; mitogen-activated protein kinase) (45%), receptor tyrosine kinases/ligand (44%), PI3K/AKT/mTOR (phosphatidylinositol-4,5-bisphosphate 3-kinase; protein kinase B; mammalian target of rapamycin) (35%), transcription factors/regulators (31%), and cell cycle regulators (30%). Many low frequency but potentially actionable alterations were identified in diverse histologies. Use of comprehensive profiling led to implementable clinical action in 35% of tumors with genomic alterations, including genomically guided therapy, diagnostic modification, and trigger for germline genetic testing. Conclusion. Use of targeted next-generation sequencing in the setting of an institutional molecular tumor board led to implementable clinical action in more than one third of patients with rare and poor-prognosis cancers. Major barriers to implementation of genomically guided therapy were clinical status of the patient and drug access. Early and serial sequencing in the clinical course and expanded access to genomically guided early-phase clinical trials and targeted agents may increase actionability. Implications for Practice: Identification of key factors that facilitate use of genomic tumor testing results and implementation of genomically guided therapy may lead to enhanced benefit for patients with rare or difficult to treat cancers. Clinical use of a targeted next-generation sequencing assay in the setting of an institutional molecular tumor board led to implementable clinical action in over one third of patients with rare and poor prognosis cancers. The major barriers to implementation of genomically guided therapy were clinical status of the patient and drug access both on trial and off label. Approaches to increase actionability include early and serial sequencing in the clinical course and expanded access to genomically guided early phase clinical trials and targeted agents

Large-scale inference of liver fat with neural networks on UK Biobank body MRI

The UK Biobank Imaging Study has acquired medical scans of more than 40,000 volunteer participants. The resulting wealth of anatomical information has been made available for research, together with extensive metadata including measurements of liver fat. These values play an important role in metabolic disease, but are only available for a minority of imaged subjects as their collection requires the careful work of image analysts on dedicated liver MRI. Another UK Biobank protocol is neck-to-knee body MRI for analysis of body composition. The resulting volumes can also quantify fat fractions, even though they were reconstructed with a two- instead of a three-point Dixon technique. In this work, a novel framework for automated inference of liver fat from UK Biobank neck-to-knee body MRI is proposed. A ResNet50 was trained for regression on two-dimensional slices from these scans and the reference values as target, without any need for ground truth segmentations. Once trained, it performs fast, objective, and fully automated predictions that require no manual intervention. On the given data, it closely emulates the reference method, reaching a level of agreement comparable to different gold standard techniques. The network learned to rectify non-linearities in the fat fraction values and identified several outliers in the reference. It outperformed a multi-atlas segmentation baseline and inferred new estimates for all imaged subjects lacking reference values, expanding the total number of liver fat measurements by factor six

Epidemiology and natural history of central venous access device use and infusion pump function in the NO16966 trial

Background: Central venous access devices in fluoropyrimidine therapy are associated with complications; however, reliable data are lacking regarding their natural history, associated complications and infusion pump performance in patients with metastatic colorectal cancer.<p></p> Methods: We assessed device placement, use during treatment, associated clinical outcomes and infusion pump perfomance in the NO16966 trial.<p></p> Results: Device replacement was more common with FOLFOX-4 (5-fluorouracil (5-FU)+oxaliplatin) than XELOX (capecitabine+oxaliplatin) (14.1% vs 5.1%). Baseline device-associated events and post-baseline removal-/placement-related events occurred more frequently with FOLFOX-4 than XELOX (11.5% vs 2.4% and 8.5% vs 2.1%). Pump malfunctions, primarily infusion accelerations in 16% of patients, occurred within 1.6–4.3% of cycles. Fluoropyrimidine-associated grade 3/4 toxicity was increased in FOLFOX-4-treated patients experiencing a malfunction compared with those who did not (97 out of 155 vs 452 out of 825 patients), predominantly with increased grade 3/4 neutropenia (53.5% vs 39.8%). Febrile neutropenia rates were comparable between patient cohorts±malfunction. Efficacy outcomes were similar in patient cohorts±malfunction.<p></p> Conclusions: Central venous access device removal or replacement was common and more frequent in patients receiving FOLFOX-4. Pump malfunctions were also common and were associated with increased rates of grade 3/4 haematological adverse events. Oral fluoropyrimidine-based regimens may be preferable to infusional 5-FU based on these findings

A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features

Cardiovascular diseases are a public health concern; they remain the leading cause of morbidity and mortality in patients with type 2 diabetes. Phenotypic information available from retinal fundus images and clinical measurements, in addition to genomic data, can identify relevant biomarkers of cardiovascular health. In this study, we assessed whether such biomarkers stratified risks of major adverse cardiac events (MACE). A retrospective analysis was carried out on an extract from the Tayside GoDARTS bioresource of participants with type 2 diabetes (n = 3,891). A total of 519 features were incorporated, summarising morphometric properties of the retinal vasculature, various single nucleotide polymorphisms (SNPs), as well as routine clinical measurements. After imputing missing features, a predictive model was developed on a randomly sampled set (n = 2,918) using L1-regularised logistic regression (lasso). The model was evaluated on an independent set (n = 973) and its performance associated with overall hazard rate after censoring (log-rank p < 0.0001), suggesting that multimodal features were able to capture important knowledge for MACE risk assessment. We further showed through a bootstrap analysis that all three sources of information (retinal, genetic, routine clinical) offer robust signal. Particularly robust features included: tortuousity, width gradient, and branching point retinal groupings; SNPs known to be associated with blood pressure and cardiovascular phenotypic traits; age at imaging; clinical measurements such as blood pressure and high density lipoprotein. This novel approach could be used for fast and sensitive determination of future risks associated with MACE

Deep Learning for Predicting Refractive Error From Retinal Fundus Images

PURPOSE. We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging. METHODS. Retinal fundus images used in this study were 45- and 30-degree field of view images from the UK Biobank and Age-Related Eye Disease Study (AREDS) clinical trials, respectively. Refractive error was measured by autorefraction in UK Biobank and subjective refraction in AREDS. We trained a deep learning algorithm to predict refractive error from a total of 226,870 images and validated it on 24,007 UK Biobank and 15,750 AREDS images. Our model used the ‘‘attention’’ method to identify features that are correlated with refractive error. RESULTS. The resulting algorithm had a mean absolute error (MAE) of 0.56 diopters (95% confidence interval [CI]: 0.55–0.56) for estimating spherical equivalent on the UK Biobank data set and 0.91 diopters (95% CI: 0.89–0.93) for the AREDS data set. The baseline expected MAE (obtained by simply predicting the mean of this population) was 1.81 diopters (95% CI: 1.79–1.84) for UK Biobank and 1.63 (95% CI: 1.60–1.67) for AREDS. Attention maps suggested that the foveal region was one of the most important areas used by the algorithm to make this prediction, though other regions also contribute to the prediction. CONCLUSIONS. To our knowledge, the ability to estimate refractive error with high accuracy from retinal fundus photos has not been previously known and demonstrates that deep learning can be applied to make novel predictions from medical images