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https://www.readbyqxmd.com/read/29165287/quantitative-evaluation-of-head-and-neck-cancer-treatment-related-dysphagia-in-the-development-of-a-personalized-treatment-deintensification-paradigm
#1
Harry Quon, Xuan Hui, Zhi Cheng, Scott Robertson, Luke Peng, Michael Bowers, Joseph Moore, Amanda Choflet, Alex Thompson, Mariah Muse, Ana Kiess, Brandi Page, Carole Fakhry, Christine Gourin, Jolyne O'Hare, Peter Graham, Michal Szczesniak, Julia Maclean, Ian Cook, Todd McNutt
PURPOSE: To test the hypothesis that quantifying swallow function with multiple patient-reported outcome (PRO) instruments is an important strategy to yield insights in the development of personalized deintensified therapies seeking to reduce the risk of head and neck cancer (HNC) treatment-related dysphagia (HNCTD). METHODS AND MATERIALS: Irradiated HNC subjects seen in follow-up care (April 2015 to December 2015) who prospectively completed the Sydney Swallow Questionnaire (SSQ) and the MD Anderson Dysphagia Inventory (MDADI) concurrently on the web interface to our Oncospace database were evaluated...
December 1, 2017: International Journal of Radiation Oncology, Biology, Physics
https://www.readbyqxmd.com/read/29162841/detection-of-somatic-mutations-in-exome-sequencing-of-tumor-only-samples
#2
Yu-Chin Hsu, Yu-Ting Hsiao, Tzu-Yuan Kao, Jan-Gowth Chang, Grace S Shieh
Due to lack of normal samples in clinical diagnosis and to reduce costs, detection of small-scale mutations from tumor-only samples is required but remains relatively unexplored. We developed an algorithm (GATKcan) augmenting GATK with two statistics and machine learning to detect mutations in cancer. The averaged performance of GATKcan in ten experiments outperformed GATK in detecting mutations of randomly sampled 231 from 241 TCGA endometrial tumors (EC). In external validations, GATKcan outperformed GATK in TCGA breast cancer (BC), ovarian cancer (OC) and melanoma tumors, in terms of Matthews correlation coefficient (MCC) and precision, where MCC takes both sensitivity and specificity into account...
November 21, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29162279/clinical-decision-support-of-radiotherapy-treatment-planning-a-data-driven-machine-learning-strategy-for-patient-specific-dosimetric-decision-making
#3
Gilmer Valdes, Charles B Simone, Josephine Chen, Alexander Lin, Sue S Yom, Adam J Pattison, Colin M Carpenter, Timothy D Solberg
BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy. MATERIAL AND METHODS: Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy...
November 18, 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
https://www.readbyqxmd.com/read/29158798/improvement-in-prediction-of-prostate-cancer-prognosis-with-somatic-mutational-signatures
#4
Shengping Zhang, Yafei Xu, Xinjie Hui, Fei Yang, Yueming Hu, Jianlin Shao, Hui Liang, Yejun Wang
Prostate cancer is a leading male malignancy worldwide, while the prognosis prediction remains quite inaccurate. The study aimed to observe whether there was an association between the prognosis of prostate cancer and genetic mutation profile, and to build an accurate prognostic predictor based on the genetic signatures. The patients diagnosed of prostate cancer from The Cancer Genomic Atlas were used for prognostic stratification, while the somatic gene mutation profiles were compared between different prognostic groups...
2017: Journal of Cancer
https://www.readbyqxmd.com/read/29157442/a-deep-learning-based-multi-model-ensemble-method-for-cancer-prediction
#5
Yawen Xiao, Jun Wu, Zongli Lin, Xiaodong Zhao
BACKGROUND AND OBJECTIVE: Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest...
January 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29156751/identification-of-potential-tissue-specific-cancer-biomarkers-and-development-of-cancer-versus-normal-genomic-classifiers
#6
Akram Mohammed, Greyson Biegert, Jiri Adamec, Tomáš Helikar
Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. Recent "OMICS" studies which include a variety of cancer and normal tissue samples along with machine learning approaches have the potential to further accelerate such discovery. To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class. Using random forests classification and ten-fold cross-validation, we developed nine single-tissue classifiers, two multi-tissue cancer-versus-normal classifiers, and one multi-tissue normal classifier...
October 17, 2017: Oncotarget
https://www.readbyqxmd.com/read/29155996/hierarchical-attention-networks-for-information-extraction-from-cancer-pathology-reports
#7
Shang Gao, Michael T Young, John X Qiu, Hong-Jun Yoon, James B Christian, Paul A Fearn, Georgia D Tourassi, Arvind Ramanthan
Objective: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods: Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program...
November 16, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29152563/integrated-radiomic-framework-for-breast-cancer-and-tumor-biology-using-advanced-machine-learning-and-multiparametric-mri
#8
Vishwa S Parekh, Michael A Jacobs
Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors...
2017: NPJ Breast Cancer
https://www.readbyqxmd.com/read/29152097/identifying-and-analyzing-different-cancer-subtypes-using-rna-seq-data-of-blood-platelets
#9
Yu-Hang Zhang, Tao Huang, Lei Chen, YaoChen Xu, Yu Hu, Lan-Dian Hu, Yudong Cai, Xiangyin Kong
Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes...
October 20, 2017: Oncotarget
https://www.readbyqxmd.com/read/29145893/normalization-of-the-microbiota-in-patients-after-treatment-for-colonic-lesions
#10
Marc A Sze, Nielson T Baxter, Mack T Ruffin, Mary A M Rogers, Patrick D Schloss
BACKGROUND: Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26)...
November 16, 2017: Microbiome
https://www.readbyqxmd.com/read/29142749/urine-metabolomics-as-a-predictor-of-patient-tolerance-and-response-to-adjuvant-chemotherapy-in-colorectal-cancer
#11
Mark A Dykstra, Noah Switzer, Roman Eisner, Victor Tso, Rae Foshaug, Kathleen Ismond, Richard Fedorak, Haili Wang
Colorectal cancer is the third leading cause of cancer-associated mortality in the western world. The ability to predict a patient's response to chemotherapy may be of great value for clinicians and patients when planning cancer treatment. The aim of the current study was to develop a urine metabolomics-based biomarker panel to predict adverse events and response to chemotherapy in patients with colorectal cancer. A retrospective chart review of patients diagnosed with stage III or IV colorectal cancer between 2008 and 2012 was performed...
November 2017: Molecular and Clinical Oncology
https://www.readbyqxmd.com/read/29137394/measuring-plasma-levels-of-three-micrornas-can-improve-the-accuracy-for-identification-of-malignant-breast-lesions-in-women-with-bi-rads-4-mammography
#12
Julia Alejandra Pezuk, Thiago Luiz Araujo Miller, José Luiz Barbosa Bevilacqua, Alfredo Carlos Simões Dornellas de Barros, Felipe Eduardo Martins de Andrade, Luiza Freire de Andrade E Macedo, Vera Aguilar, Amanda Natasha Menardo Claro, Anamaria Aranha Camargo, Pedro Alexandre Favoretto Galante, Luiz F L Reis
A BI-RADS category of 4 from a mammogram indicates suspicious breast lesions, which require core biopsies for diagnosis and have an approximately one third chance of being malignant. Human plasma contains many circulating microRNAs, and variations in their circulating levels have been associated with pathologies, including cancer. Here, we present a novel methodology to identify malignant breast lesions in women with BI-RADS 4 mammography. First, we used the miRNome array and qRT-PCR to define circulating microRNAs that were differentially represented in blood samples from women with breast tumor (BI-RADS 5 or 6) in comparison to controls (BI-RADS 1 or 2)...
October 13, 2017: Oncotarget
https://www.readbyqxmd.com/read/29134060/modelling-pyruvate-dehydrogenase-under-hypoxia-and-its-role-in-cancer-metabolism
#13
Filmon Eyassu, Claudio Angione
Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia...
October 2017: Royal Society Open Science
https://www.readbyqxmd.com/read/29133589/precision-oncology-beyond-targeted-therapy-combining-omics-data-with-machine-learning-matches-the-majority-of-cancer-cells-to-effective-therapeutics
#14
Michael Q Ding, Lujia Chen, Gregory F Cooper, Jonathan D Young, Xinghua Lu
Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines...
November 13, 2017: Molecular Cancer Research: MCR
https://www.readbyqxmd.com/read/29132615/prediction-of-lung-cancer-patient-survival-via-supervised-machine-learning-classification-techniques
#15
Chip M Lynch, Behnaz Abdollahi, Joshua D Fuqua, Alexandra R de Carlo, James A Bartholomai, Rayeanne N Balgemann, Victor H van Berkel, Hermann B Frieboes
Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble...
December 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29121286/prediction-of-persistent-post-surgery-pain-by-preoperative-cold-pain-sensitivity-biomarker-development-with-machine-learning-derived-analysis
#16
J Lötsch, A Ultsch, E Kalso
Background: To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain. Methods: We analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up...
October 1, 2017: British Journal of Anaesthesia
https://www.readbyqxmd.com/read/29114182/application-of-deep-learning-in-automated-analysis-of-molecular-images-in-cancer-a-survey
#17
REVIEW
Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically...
2017: Contrast Media & Molecular Imaging
https://www.readbyqxmd.com/read/29110491/deep-learning-accurately-predicts-estrogen-receptor-status-in-breast-cancer-metabolomics-data
#18
Fadhl M Alkawaa, Kumardeep Chaudhary, Lana X Garmire
Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if the deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM)...
November 7, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/29106441/orchid-a-novel-management-annotation-and-machine-learning-framework-for-analyzing-cancer-mutations
#19
Clinton L Cario, John S Witte
Motivation: As whole-genome tumor sequence and biological annotation datasets grow in size, number, and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments, and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results: We present orchid, a python based software package for the management, annotation, and machine learning of cancer mutations...
November 2, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29104344/validation-of-a-machine-learning-approach-for-venous-thromboembolism-risk-prediction-in-oncology
#20
Patrizia Ferroni, Fabio M Zanzotto, Noemi Scarpato, Silvia Riondino, Fiorella Guadagni, Mario Roselli
Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treated cancer patients. This study was designed to validate a model incorporating the two best predictors and to compare their combined performance with that of the currently recommended Khorana score (KS). Age, sex, tumor site/stage, hematological attributes, blood lipids, glycemic indexes, liver and kidney function, BMI, performance status, and supportive and anticancer drugs of 608 cancer outpatients were all entered in the model, with numerical attributes analyzed as continuous values...
2017: Disease Markers
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