Publications by authors named "Leila Abdelrahman"

4 Publications

  • Page 1 of 1

Convolutional neural networks for breast cancer detection in mammography: A survey.

Comput Biol Med 2021 Apr 9;131:104248. Epub 2021 Feb 9.

University of Miami, Department of Electrical and Computer Engineering, Memorial Dr, Coral Gables, FL, 33146, USA. Electronic address:

Despite its proven record as a breast cancer screening tool, mammography remains labor-intensive and has recognized limitations, including low sensitivity in women with dense breast tissue. In the last ten years, Neural Network advances have been applied to mammography to help radiologists increase their efficiency and accuracy. This survey aims to present, in an organized and structured manner, the current knowledge base of convolutional neural networks (CNNs) in mammography. The survey first discusses traditional Computer Assisted Detection (CAD) and more recently developed CNN-based models for computer vision in mammography. It then presents and discusses the literature on available mammography training datasets. The survey then presents and discusses current literature on CNNs for four distinct mammography tasks: (1) breast density classification, (2) breast asymmetry detection and classification, (3) calcification detection and classification, and (4) mass detection and classification, including presenting and comparing the reported quantitative results for each task and the pros and cons of the different CNN-based approaches. Then, it offers real-world applications of CNN CAD algorithms by discussing current Food and Drug Administration (FDA) approved models. Finally, this survey highlights the potential opportunities for future work in this field. The material presented and discussed in this survey could serve as a road map for developing CNN-based solutions to improve mammographic detection of breast cancer further.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104248DOI Listing
April 2021

Differentiation of soluble aqueous humor metabolites in primary open angle glaucoma and controls.

Exp Eye Res 2020 05 1;194:108024. Epub 2020 Apr 1.

Bascom Palmer Eye Institute, University of Miami, Miami, FL, USA; Miami Integrative Metabolomics Research Center, University of Miami, Miami, FL, USA. Electronic address:

We report an analysis of the aqueous humor (AH) metabolome of primary open angle glaucoma (POAG) in comparison to normal controls. The AH samples were obtained from human donors [control (n = 35), POAG (n = 23)]. The AH samples were subjected to one-dimensional H nuclear magnetic resonance (NMR) analyses on a Bruker Avance 600 MHz instrument with a 1.7 mM NMR probe. The same samples were then subjected to isotopic ratio outlier analysis (IROA) using a Q Exactive orbitrap mass spectrometer after chromatography on an Accela 600 HPLC. Clusterfinder Build 3.1.10 was used for identification and quantification based on long-term metabolite matrix standards. In total, 278 metabolites were identified in control samples and 273 in POAG AH. The metabolites identified were fed into previously reported proteome and genome information and the OmicsNet interaction network generator to construct a protein-metabolite interactions network with an embedded protein-protein network. Significant differences in metabolite composition in POAG compared to controls were identified indicating potential protein/gene pathways associated with these metabolites. These results will expand our previous understanding of the impeded AH metabolite composition, provide new insight into the regulation of AH outflow, and likely aid in future AH and trabecular meshwork multi-omics network analyses.
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http://dx.doi.org/10.1016/j.exer.2020.108024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229990PMC
May 2020

Type 2 diabetes induced microbiome dysbiosis is associated with therapy resistance in pancreatic adenocarcinoma.

Microb Cell Fact 2020 Mar 24;19(1):75. Epub 2020 Mar 24.

Sylvester Comprehensive Cancer Center, Miami, FL, USA.

Resistance to therapy is one of the major factors that contribute to dismal survival statistics in pancreatic cancer. While there are many tumor intrinsic and tumor microenvironment driven factors that contribute to therapy resistance, whether pre-existing metabolic diseases like type 2 diabetes (T2D) contribute to this has remained understudied. It is well accepted that hyperglycemia associated with type 2 diabetes changes the gut microbiome. Further, hyperglycemia also enriches for a "stem-like" population within the tumor. In the current study, we observed that in a T2D mouse model, the microbiome changed significantly as the hyperglycemia developed in these animals. Our results further showed that, tumors implanted in the T2D mice responded poorly to gemcitabine/paclitaxel (Gem/Pac) standard of care compared to those in the control group. A metabolomic reconstruction of the WGS of the gut microbiota further revealed that an enrichment of bacterial population involved in drug metabolism in the T2D group. Additionally, we also observed an increase in the CD133+ tumor cells population in the T2D model. These observations indicated that in an animal model for T2D, microbial dysbiosis is associated with increased resistance to chemotherapeutic compounds.
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http://dx.doi.org/10.1186/s12934-020-01330-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7092523PMC
March 2020

Aqueous humor metabolite profile of pseudoexfoliation glaucoma is distinctive.

Mol Omics 2020 10;16(5):425-435

Bascom Palmer Eye Institute, University of Miami, Miami, Florida, USA. and Miami Integrative Metabolomics Research Center, University of Miami, Miami, Florida, USA.

Pseudoexfoliation (PEX) is a known cause of secondary open angle glaucoma. PEX glaucoma is associated with structural and metabolic changes in the eye. Despite similarities, PEX and primary open angle glaucoma (POAG) may have differences in the composition of metabolites. We analyzed the metabolites of the aqueous humor (AH) of PEX subjects sequentially first using nuclear magnetic resonance (1H NMR: HSQC and TOCSY), and subsequently with liquid chromatography tandem mass spectrometry (LC-MS/MS) implementing isotopic ratio outlier analysis (IROA) quantification. The findings were compared with previous results for POAG and control subjects analyzed using identical sequential steps. We found significant differences in metabolites between the three conditions. Principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) indicated clear grouping based on the metabolomes of the three conditions. We used machine learning algorithms and a percentage set of the data to train, and utilized a different or larger dataset to test whether a trained model can correctly classify the test dataset as PEX, POAG or control. Three different algorithms: linear support vector machines (SVM), deep learning, and a neural network were used for prediction. They all accurately classified the test datasets based on the AH metabolome of the sample. We next compared the AH metabolome with known AH and TM proteomes and genomes in order to understand metabolic pathways that may contribute to alterations in the AH metabolome in PEX. We found potential protein/gene pathways associated with observed significant metabolite changes in PEX.
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http://dx.doi.org/10.1039/c9mo00192aDOI Listing
October 2020