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https://www.readbyqxmd.com/read/29789422/machine-learning-based-radiomics-for-molecular-subtyping-of-gliomas
#1
Chia-Feng Lu, Fei-Ting Hsu, Kevin Li-Chun Hsieh, Yu-Chieh Jill Kao, Sho-Jen Cheng, Justin Bo-Kai Hsu, Ping-Huei Tsai, Ray-Jade Chen, Chao-Ching Huang, Yun Yen, Cheng-Yu Chen
PURPOSE: The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in addition to histological phenotypes. We aim to determine whether clinical magnetic resonance imaging (MRI) can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas. EXPERIMENTAL DESIGN: The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive...
May 22, 2018: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/29789232/high-grade-serous-ovarian-cancer-use-of-machine-learning-to-predict-abdominopelvic-recurrence-on-ct-on-the-basis-of-serial-cancer-antigen-125-levels
#2
Atul B Shinagare, Patricia Balthazar, Ivan K Ip, Ronilda Lacson, Joyce Liu, Nikhil Ramaiya, Ramin Khorasani
PURPOSE: The aim of this study was to use machine learning to predict abdominal recurrence on CT on the basis of serial cancer antigen 125 (CA125) levels in patients with advanced high-grade serous ovarian cancer on surveillance. METHODS: This institutional review board-approved, HIPAA-compliant, retrospective, hypothesis-generating study included all 57 patients (mean age, 61 ± 11.2 years) with advanced high-grade serous ovarian cancer who underwent cytoreductive surgery from January to December 2012, followed by surveillance abdominopelvic CT and corresponding CA125 levels...
May 19, 2018: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29788456/kinact-a-computational-approach-for-predicting-activating-missense-mutations-in-protein-kinases
#3
Carlos H M Rodrigues, David B Ascher, Douglas E V Pires
Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models...
May 21, 2018: Nucleic Acids Research
https://www.readbyqxmd.com/read/29787940/survey-on-deep-learning-for-radiotherapy
#4
REVIEW
Philippe Meyer, Vincent Noblet, Christophe Mazzara, Alex Lallement
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning...
May 17, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29785657/novel-non-invasive-early-detection-of-lung-cancer-using-liquid-immunobiopsy-metabolic-activity-profiles
#5
Yochai Adir, Shoval Tirman, Shirley Abramovitch, Cynthia Botbol, Aviv Lutaty, Tali Scheinmann, Eyal Davidovits, Irit Arbel, Giora Davidovits, Sonia Schneer, Michal Shteinberg, Hagit Peretz Soroka, Ruven Tirosh, Fernando Patolsky
Lung cancer is the leading cause of cancer death worldwide. Survival is largely dependent on the stage of diagnosis: the localized disease has a 5-year survival greater than 55%, whereas, for spread tumors, this rate is only 4%. Therefore, the early detection of lung cancer is key for improving prognosis. In this study, we present an innovative, non-invasive, cancer detection approach based on measurements of the metabolic activity profiles of immune system cells. For each Liquid ImmunoBiopsy test, a 384 multi-well plate is loaded with freshly separated PBMCs, and each well contains 1 of the 16 selected stimulants in several increasing concentrations...
May 21, 2018: Cancer Immunology, Immunotherapy: CII
https://www.readbyqxmd.com/read/29785121/association-between-angiogenesis-and-cytotoxic-signatures-in-the-tumor-microenvironment-of-gastric-cancer
#6
Yi Feng, Ying Dai, Zhihua Gong, Jia-Nan Cheng, Longhui Zhang, Chengdu Sun, Xianghua Zeng, Qingzhu Jia, Bo Zhu
Background: A suppressive immune microenvironment and pathological angiogenesis are hallmarks of gastric cancer. Theoretically, immune checkpoint inhibitors (ICIs) stimulate pre-primed neoantigen-specific T cells, and antiangiogenic agents then facilitate their infiltration into the tumor niche by promoting vascular normalization. Currently, the interconnections of these two phenotypes and their relevance to the tumor microenvironment (TME) have not been fully characterized in gastric cancer...
2018: OncoTargets and Therapy
https://www.readbyqxmd.com/read/29783760/diagnosing-breast-cancer-with-microwave-technology-remaining-challenges-and-potential-solutions-with-machine-learning
#7
Bárbara L Oliveira, Daniela Godinho, Martin O'Halloran, Martin Glavin, Edward Jones, Raquel C Conceição
Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours...
May 19, 2018: Diagnostics
https://www.readbyqxmd.com/read/29771528/host-cell-prediction-of-exosomes-using-morphological-features-on-solid-surfaces-analyzed-by-machine-learning
#8
Kazuki Ito, Yuta Ogawa, Keiji Yokota, Sachiko Matsumura, Tamiko Minamisawa, Kanako Suga, Kiyotaka Shiba, Yasuo Kimura, Ayumi Hirano-Iwata, Yuzuru Takamura, Toshio Ogino
Exosomes are extracellular nanovesicles released from any cells and found in any body-fluid. Because exosomes exhibit information of their host cells (secreting cells), their analysis is expected to be a powerful tool for early diagnosis of cancers. To predict the host cells, we extracted multi-dimensional feature data about size, shape, and deformation of exosomes immobilized on solid surfaces by atomic force microscopy (AFM). The key idea is combination of support vector machine (SVM) learning for individual exosome particles and their interpretation by principal component analysis (PCA)...
May 17, 2018: Journal of Physical Chemistry. B
https://www.readbyqxmd.com/read/29763967/machine-learning-algorithms-for-outcome-prediction-in-chemo-radiotherapy-an-empirical-comparison-of-classifiers
#9
Timo M Deist, Frank J W M Dankers, Gilmer Valdes, Robin Wijsman, I-Chow Hsu, Cary Oberije, Tim Lustberg, Johan van Soest, Frank Hoebers, Arthur Jochems, Issam El Naqa, Leonard Wee, Olivier Morin, David R Raleigh, Wouter Bots, Johannes H Kaanders, José Belderbos, Margriet Kwint, Timothy Solberg, René Monshouwer, Johan Bussink, Andre Dekker, Philippe Lambin
PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction...
May 15, 2018: Medical Physics
https://www.readbyqxmd.com/read/29760982/biodynamic-digital-holography-of-chemoresistance-in-a-pre-clinical-trial-of-canine-b-cell-lymphoma
#10
Honggu Choi, Zhe Li, Hao Sun, Dan Merrill, John Turek, Michael Childress, David Nolte
Biodynamic digital holography was used to obtain phenotypic profiles of canine non-Hodgkin B-cell lymphoma biopsies treated with standard-of-care chemotherapy. Biodynamic signatures from the living 3D tissues were extracted using fluctuation spectroscopy from intracellular Doppler light scattering in response to the molecular mechanisms of action of therapeutic drugs that modify a range of internal cellular motions. The standard-of-care to treat B-cell lymphoma in both humans and dogs is a combination CHOP therapy that consists of doxorubicin, prednisolone, cyclophosphamide and vincristine...
May 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29759139/identifying-cytokine-predictors-of-cognitive-functioning-in-breast-cancer-survivors-up-to-10-years-post-chemotherapy-using-machine-learning
#11
Ashley M Henneghan, Oxana Palesh, Michelle Harrison, Shelli R Kesler
INTRODUCTION: The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric approach. METHODS: Cross sectional data collection included completion of a survey, cognitive testing, and non-fasting blood from 66 participants. Data were analyzed using random forest regression to identify the most significant predictors for each of the cognitive test scores...
July 15, 2018: Journal of Neuroimmunology
https://www.readbyqxmd.com/read/29758261/annotating-diseases-using-human-phenotype-ontology-improves-prediction-of-disease-associated-long-non-coding-rnas
#12
Duc-Hau Le, Lan T M Dao
Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs...
May 11, 2018: Journal of Molecular Biology
https://www.readbyqxmd.com/read/29754799/extracting-cancer-mortality-statistics-from-death-certificates-a-hybrid-machine-learning-and-rule-based-approach-for-common-and-rare-cancers
#13
Bevan Koopman, Guido Zuccon, Anthony Nguyen, Anton Bergheim, Narelle Grayson
OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality of certificates written in natural language. This paper proposes an automatic classification system for identifying all cancer related causes of death from death certificates. METHODS: Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates...
May 10, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29748869/classification-of-breast-masses-using-a-computer-aided-diagnosis-scheme-of-contrast-enhanced-digital-mammograms
#14
Gopichandh Danala, Bhavika Patel, Faranak Aghaei, Morteza Heidari, Jing Li, Teresa Wu, Bin Zheng
Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately...
May 10, 2018: Annals of Biomedical Engineering
https://www.readbyqxmd.com/read/29748206/deep-learning-convolutional-neural-networks-accurately-classify-genetic-mutations-in-gliomas
#15
P Chang, J Grinband, B D Weinberg, M Bardis, M Khy, G Cadena, M-Y Su, S Cha, C G Filippi, D Bota, P Baldi, L M Poisson, R Jain, D Chow
BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation...
May 10, 2018: AJNR. American Journal of Neuroradiology
https://www.readbyqxmd.com/read/29746776/from-cancer-to-pain-target-by-automated-selectivity-inversion-of-a-clinical-candidate
#16
Samo Turk, Benjamin Merget, Sameh Eid, Simone Fulle
Elimination of inadvertent binding is crucial for inhibitor design targeting conserved protein classes like kinases. Compounds in clinical trials provide a rich source for initiating drug design efforts by exploiting such secondary binding events. Considering both aspects, we shifted the selectivity of tozasertib, originally developed against AurA as cancer target, towards the pain target TrkA. First, selectivity-determining features in binding pockets were identified by fusing interaction-grids of several key and off-target conformations...
May 10, 2018: Journal of Medicinal Chemistry
https://www.readbyqxmd.com/read/29745270/qsar-modelling-a-therapeutic-patent-review-2010-present
#17
Amit Kumar Halder, Ana S Moura, M Natalia D S Cordeiro
Quantitative Structure-Activity Relationship (QSAR) models are becoming one of the most interesting fields for developing therapeutics and therapeutics related patents. At present, QSAR methodologies comprise a series of possibilities, including joining forces with machine learning methods and increasing even more the swiftness they might bring to the prospective development of therapeutics in the Health Sciences scope. Areas covered: After evaluating the period from 2010 to the end of 2018, the areas covered by the reviewed QSAR based therapeutics patents comprise three main fields (drug development, risk assessment and novel QSAR methodologies), and several areas, from cancer and cancer related symptomatology to neurodegenerative diseases, such as Parkinson's disease, or even monitoring several chemical particles carrier-mediums or interface frontiers...
May 10, 2018: Expert Opinion on Therapeutic Patents
https://www.readbyqxmd.com/read/29735554/mirna-profiling-of-magnetic-nanopore-isolated-extracellular-vesicles-for-the-diagnosis-of-pancreatic-cancer
#18
Jina Ko, Neha Bhagwat, Taylor Black, Stephanie S Yee, Young-Ji Na, Stephen A Fisher, Junhyong Kim, Erica L Carpenter, Ben Z Stanger, David Issadore
Improved diagnostics for pancreatic ductal adenocarcinoma (PDAC) to detect the disease at earlier, curative stages and to guide treatments is crucial to progress against this disease. The development of a liquid biopsy for PDAC has proven challenging due to the sparsity and variable phenotypic expression of circulating biomarkers. Here we report methods we developed for isolating specific subsets of extracellular vesicles (EV) from plasma using a novel magnetic nanopore capture technique. In addition, we present a workflow for identifying EV miRNA biomarkers using RNA sequencing and machine-learning algorithms, which we used in combination to classify distinct cancer states...
May 7, 2018: Cancer Research
https://www.readbyqxmd.com/read/29734508/using-machine-learning-to-identify-patterns-of-lifetime-health-problems-in-decedents-with-autism-spectrum-disorder
#19
Lauren Bishop-Fitzpatrick, Arezoo Movaghar, Jan S Greenberg, David Page, Leann S DaWalt, Murray H Brilliant, Marsha R Mailick
Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a machine learning algorithm to characterize diagnostic patterns in decedents with ASD and matched decedent community controls. Participants were 91 decedents with ASD and 6,186 sex and birth year matched decedent community controls who had died since 1979, the majority of whom were middle aged or older adults at the time of their death...
May 7, 2018: Autism Research: Official Journal of the International Society for Autism Research
https://www.readbyqxmd.com/read/29734484/radiomic-features-from-pretreatment-biparametric-mri-predict-prostate-cancer-biochemical-recurrence-preliminary-findings
#20
Rakesh Shiradkar, Soumya Ghose, Ivan Jambor, Pekka Taimen, Otto Ettala, Andrei S Purysko, Anant Madabhushi
BACKGROUND: Radiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. PURPOSE: To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. STUDY TYPE: Retrospective. SUBJECTS: In all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N = 70) from I1 and independent validation set D2 (N = 50) from I2 ...
May 7, 2018: Journal of Magnetic Resonance Imaging: JMRI
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