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https://www.readbyqxmd.com/read/28934238/a-tool-to-automatically-analyze-electromagnetic-tracking-data-from-high-dose-rate-brachytherapy-of-breast-cancer-patients
#1
Th I Götz, G Lahmer, V Strnad, Ch Bert, B Hensel, A M Tomé, E W Lang
During High Dose Rate Brachytherapy (HDR-BT) the spatial position of the radiation source inside catheters implanted into a female breast is determined via electromagnetic tracking (EMT). Dwell positions and dwell times of the radiation source are established, relative to the patient's anatomy, from an initial X-ray-CT-image. During the irradiation treatment, catheter displacements can occur due to patient movements. The current study develops an automatic analysis tool of EMT data sets recorded with a solenoid sensor to assure concordance of the source movement with the treatment plan...
2017: PloS One
https://www.readbyqxmd.com/read/28931937/high-accuracy-label-free-classification-of-single-cell-kinetic-states-from-holographic-cytometry-of-human-melanoma-cells
#2
Miroslav Hejna, Aparna Jorapur, Jun S Song, Robert L Judson
Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions...
September 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28931888/automatic-classification-of-cancerous-tissue-in-laserendomicroscopy-images-of-the-oral-cavity-using-deep-learning
#3
Marc Aubreville, Christian Knipfer, Nicolai Oetter, Christian Jaremenko, Erik Rodner, Joachim Denzler, Christopher Bohr, Helmut Neumann, Florian Stelzle, Andreas Maier
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis...
September 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28927002/predicting-variation-of-dna-shape-preferences-in-protein-dna-interaction-in-cancer-cells-with-a-new-biophysical-model
#4
Kirill Batmanov, Junbai Wang
DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor-DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein-DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features...
September 18, 2017: Genes
https://www.readbyqxmd.com/read/28921845/detection-and-delineation-of-squamous-neoplasia-with-hyperspectral-imaging-in-a-mouse-model-of-tongue-carcinogenesis
#5
Guolan Lu, Dongsheng Wang, Xulei Qin, Susan Muller, Xu Wang, Amy Y Chen, Zhuo Georgia Chen, Baowei Fei
yperspectral imaging (HSI) holds the potential for the noninvasive detection of cancers. Oral cancers are often diagnosed at a late stage when treatment is less effective and the mortality and morbidity rates are high. Early detection of oral cancer is, therefore, crucial in order to improve the clinical outcomes. To investigate the potential of HSI as a non-invasive diagnostic tool, an animal study was designed to acquire hyperspectral images of in vivo and ex vivo mouse tongues from a chemically induced tongue carcinogenesis model...
September 17, 2017: Journal of Biophotonics
https://www.readbyqxmd.com/read/28918937/a-predictive-model-for-selective-targeting-of-the-warburg-effect-through-gapdh-inhibition-with-a-natural-product
#6
Maria V Liberti, Ziwei Dai, Suzanne E Wardell, Joshua A Baccile, Xiaojing Liu, Xia Gao, Robert Baldi, Mahya Mehrmohamadi, Marc O Johnson, Neel S Madhukar, Alexander A Shestov, Iok I Christine Chio, Olivier Elemento, Jeffrey C Rathmell, Frank C Schroeder, Donald P McDonnell, Jason W Locasale
Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE...
September 8, 2017: Cell Metabolism
https://www.readbyqxmd.com/read/28916782/predicting-clinical-outcomes-from-large-scale-cancer-genomic-profiles-with-deep-survival-models
#7
Safoora Yousefi, Fatemeh Amrollahi, Mohamed Amgad, Chengliang Dong, Joshua E Lewis, Congzheng Song, David A Gutman, Sameer H Halani, Jose Enrique Velazquez Vega, Daniel J Brat, Lee A D Cooper
Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes...
September 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28915974/rapid-and-accurate-intraoperative-pathological-diagnosis-by-artificial-intelligence-with-deep-learning-technology
#8
Jing Zhang, Yanlin Song, Fan Xia, Chenjing Zhu, Yingying Zhang, Wenpeng Song, Jianguo Xu, Xuelei Ma
Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm...
September 2017: Medical Hypotheses
https://www.readbyqxmd.com/read/28915930/predicting-activities-of-daily-living-for-cancer-patients-using-an-ontology-guided-machine-learning-methodology
#9
Hua Min, Hedyeh Mobahi, Katherine Irvin, Sanja Avramovic, Janusz Wojtusiak
BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset...
September 16, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28915659/data-driven-analysis-of-immune-infiltrate-in-a-large-cohort-of-breast-cancer-and-its-association-with-disease-progression-er-activity-and-genomic-complexity
#10
Ruth Dannenfelser, Marianne Nome, Andliena Tahiri, Josie Ursini-Siegel, Hans Kristian Moen Vollan, Vilde D Haakensen, Åslaug Helland, Bjørn Naume, Carlos Caldas, Anne-Lise Børresen-Dale, Vessela N Kristensen, Olga G Troyanskaya
The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry...
August 22, 2017: Oncotarget
https://www.readbyqxmd.com/read/28911905/gaussian-process-classification-of-superparamagnetic-relaxometry-data-phantom-study
#11
Javad Sovizi, Kelsey B Mathieu, Sara L Thrower, Wolfgang Stefan, John D Hazle, David Fuentes
MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty...
July 24, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28911111/combinatorial-ensemble-mirna-target-prediction-of-co-regulation-networks-with-non-prediction-data
#12
Jason A Davis, Sita J Saunders, Martin Mann, Rolf Backofen
MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms. Simultaneously, the inverse dataset of negative interactions not likely to occur was extracted to increase classifier performance, as measured using an expansive set of experimentally validated interactions from a variety of sources...
September 6, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28910336/application-of-unsupervised-analysis-techniques-to-lung-cancer-patient-data
#13
Chip M Lynch, Victor H van Berkel, Hermann B Frieboes
This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into groups based on clinically measurable disease-specific variables in order to estimate survival. Variables selected as inputs for machine learning include Number of Primaries, Age, Grade, Tumor Size, Stage, and TNM, which are numeric or can readily be converted to numeric type...
2017: PloS One
https://www.readbyqxmd.com/read/28880325/computational-study-on-the-origin-of-the-cancer-immunotherapeutic-potential-of-b-and-t-cell-epitope-peptides
#14
Hao Li, Nalini Schaduangrat, Saw Simeon, Chanin Nantasenamat
Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities...
September 7, 2017: Molecular BioSystems
https://www.readbyqxmd.com/read/28872366/breast-cancer-patients-depression-prediction-by-machine-learning-approach
#15
Jovana Cvetković
One of the most common cancer in females is breasts cancer. This cancer can has high impact on the women including health and social dimensions. One of the most common social dimension is depression caused by breast cancer. Depression can impairs life quality. Depression is one of the symptom among the breast cancer patients. One of the solution is to eliminate the depression in breast cancer patients is by treatments but these treatments can has different unpredictable impacts on the patients. Therefore it is suitable to develop algorithm in order to predict the depression range...
September 5, 2017: Cancer Investigation
https://www.readbyqxmd.com/read/28866267/how-well-can-carcinogenicity-be-predicted-by-high-throughput-characteristics-of-carcinogens-mechanistic-data
#16
Richard A Becker, David A Dreier, Mary K Manibusan, Louis A Tony Cox, Ted W Simon, James S Bus
IARC has begun using ToxCast/Tox21 data in efforts to represent key characteristics of carcinogens to organize and weigh mechanistic evidence in cancer hazard determinations and this implicit inference approach also is being considered by USEPA. To determine how well ToxCast/Tox21 data can explicitly predict cancer hazard, this approach was evaluated with statistical analyses and machine learning prediction algorithms. Substances USEPA previously classified as having cancer hazard potential were designated as positives and substances not posing a carcinogenic hazard were designated as negatives...
September 1, 2017: Regulatory Toxicology and Pharmacology: RTP
https://www.readbyqxmd.com/read/28861708/breast-cancer-cell-nuclei-classification-in-histopathology-images-using-deep-neural-networks
#17
REVIEW
Yangqin Feng, Lei Zhang, Zhang Yi
PURPOSE: Cell nuclei classification in breast cancer histopathology images plays an important role in effective diagnose since breast cancer can often be characterized by its expression in cell nuclei. However, due to the small and variant sizes of cell nuclei, and heavy noise in histopathology images, traditional machine learning methods cannot achieve desirable recognition accuracy. To address this challenge, this paper aims to present a novel deep neural network which performs representation learning and cell nuclei recognition in an end-to-end manner...
August 31, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28855608/finding-disagreement-pathway-signatures-and-constructing-an-ensemble-model-for-cancer-classification
#18
Qiaosheng Zhang, Jie Li, Dong Wang, Yadong Wang
Cancer classification based on molecular level is a relatively routine research procedure with advances in high-throughput molecular profiling techniques. However, the number of genes typically far exceeds the number of the sample size in gene expression studies. The existing gene selection methods are almost based on statistics and machine learning, overlooking relevant biological principles or knowledge while working with biological data. Here, we propose a robust ensemble learning paradigm, which incorporates multiple pathways information, to predict cancer classification...
August 30, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28853484/integrating-multiple-fitting-regression-and-bayes-decision-for-cancer-diagnosis-with-transcriptomic-data-from-tumor-educated-blood-platelets
#19
Guangzao Huang, Mingshun Yuan, Moliang Chen, Lei Li, Wenjie You, Hanjie Li, James J Cai, Guoli Ji
The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory...
August 30, 2017: Analyst
https://www.readbyqxmd.com/read/28852119/automated-classification-of-benign-and-malignant-proliferative-breast-lesions
#20
Evani Radiya-Dixit, David Zhu, Andrew H Beck
Misclassification of breast lesions can result in either cancer progression or unnecessary chemotherapy. Automated classification tools are seen as promising second opinion providers in reducing such errors. We have developed predictive algorithms that automate the categorization of breast lesions as either benign usual ductal hyperplasia (UDH) or malignant ductal carcinoma in situ (DCIS). From diagnosed breast biopsy images from two hospitals, we obtained 392 biomarkers using Dong et al.'s (2014) computational tools for nuclei identification and feature extraction...
August 29, 2017: Scientific Reports
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