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cancer machine learning

Runyu Jing, Yu Liang, Yi Ran, Shengzhong Feng, Yanjie Wei, Li He
In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies...
2018: International Journal of Genomics
Sara Moccia, Gabriele O Vanone, Elena De Momi, Andrea Laborai, Luca Guastini, Giorgio Peretti, Leonardo S Mattos
BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance...
May 2018: Computer Methods and Programs in Biomedicine
Irene Fondón, Auxiliadora Sarmiento, Ana Isabel García, María Silvestre, Catarina Eloy, António Polónia, Paulo Aguiar
Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images...
March 8, 2018: Computers in Biology and Medicine
Kimberley Lau, Joseph Wilkinson, Ram Moorthy
OBJECTIVE: Following the announcement of the NHS Cancer Plan in 2000, anyone suspected of having cancer has to be seen by a specialist within two weeks of referral. Since this introduction, studies have shown that only 6.3-14.6% of 2-week referrals were diagnosed with a head and neck cancer and that majority of the cancer diagnoses were via other referral routes. These studies suggest that the referral scheme is not currently cost effective. Our aim is to develop a scoring system that determines the risk of head and neck cancer in a patient, which can then be used to aid GP referrals...
March 15, 2018: Clinical Otolaryngology
Kelly Aubertin, Vincent Quoc Trinh, Michael Jermyn, Paul Baksic, Andrée-Anne Grosset, Joannie Desroches, Karl St-Arnaud, Mirela Birlea, Maria Claudia Vladoiu, Mathieu Latour, Roula Albadine, Fred Saad, Frédéric Leblond, Dominique Trudel
OBJECTIVE: To test if Raman spectroscopy is an appropriate tool for the diagnosis and possibly the grading of prostate cancer. PATIENTS & METHODS: Between 20 and 50 Raman spectra were acquired on 32 fresh and non-processed post-prostatectomy specimens using a macroscopic hand-held Raman spectroscopy probe. Each measured area was characterized and categorized following histopathological criterions like tissue type (extra-prostatic or prostatic), tissue malignancy (benign or malignant), cancer grade (Grade Groups (GG) 1-5), and tissue glandular level...
March 15, 2018: BJU International
David Capper, David T W Jones, Martin Sill, Volker Hovestadt, Daniel Schrimpf, Dominik Sturm, Christian Koelsche, Felix Sahm, Lukas Chavez, David E Reuss, Annekathrin Kratz, Annika K Wefers, Kristin Huang, Kristian W Pajtler, Leonille Schweizer, Damian Stichel, Adriana Olar, Nils W Engel, Kerstin Lindenberg, Patrick N Harter, Anne K Braczynski, Karl H Plate, Hildegard Dohmen, Boyan K Garvalov, Roland Coras, Annett Hölsken, Ekkehard Hewer, Melanie Bewerunge-Hudler, Matthias Schick, Roger Fischer, Rudi Beschorner, Jens Schittenhelm, Ori Staszewski, Khalida Wani, Pascale Varlet, Melanie Pages, Petra Temming, Dietmar Lohmann, Florian Selt, Hendrik Witt, Till Milde, Olaf Witt, Eleonora Aronica, Felice Giangaspero, Elisabeth Rushing, Wolfram Scheurlen, Christoph Geisenberger, Fausto J Rodriguez, Albert Becker, Matthias Preusser, Christine Haberler, Rolf Bjerkvig, Jane Cryan, Michael Farrell, Martina Deckert, Jürgen Hench, Stephan Frank, Jonathan Serrano, Kasthuri Kannan, Aristotelis Tsirigos, Wolfgang Brück, Silvia Hofer, Stefanie Brehmer, Marcel Seiz-Rosenhagen, Daniel Hänggi, Volkmar Hans, Stephanie Rozsnoki, Jordan R Hansford, Patricia Kohlhof, Bjarne W Kristensen, Matt Lechner, Beatriz Lopes, Christian Mawrin, Ralf Ketter, Andreas Kulozik, Ziad Khatib, Frank Heppner, Arend Koch, Anne Jouvet, Catherine Keohane, Helmut Mühleisen, Wolf Mueller, Ute Pohl, Marco Prinz, Axel Benner, Marc Zapatka, Nicholas G Gottardo, Pablo Hernáiz Driever, Christof M Kramm, Hermann L Müller, Stefan Rutkowski, Katja von Hoff, Michael C Frühwald, Astrid Gnekow, Gudrun Fleischhack, Stephan Tippelt, Gabriele Calaminus, Camelia-Maria Monoranu, Arie Perry, Chris Jones, Thomas S Jacques, Bernhard Radlwimmer, Marco Gessi, Torsten Pietsch, Johannes Schramm, Gabriele Schackert, Manfred Westphal, Guido Reifenberger, Pieter Wesseling, Michael Weller, Vincent Peter Collins, Ingmar Blümcke, Martin Bendszus, Jürgen Debus, Annie Huang, Nada Jabado, Paul A Northcott, Werner Paulus, Amar Gajjar, Giles W Robinson, Michael D Taylor, Zane Jaunmuktane, Marina Ryzhova, Michael Platten, Andreas Unterberg, Wolfgang Wick, Matthias A Karajannis, Michel Mittelbronn, Till Acker, Christian Hartmann, Kenneth Aldape, Ulrich Schüller, Rolf Buslei, Peter Lichter, Marcel Kool, Christel Herold-Mende, David W Ellison, Martin Hasselblatt, Matija Snuderl, Sebastian Brandner, Andrey Korshunov, Andreas von Deimling, Stefan M Pfister
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting...
March 14, 2018: Nature
Lokmane Chebouba, Bertrand Miannay, Dalila Boughaci, Carito Guziolowski
BACKGROUND: During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs...
March 8, 2018: BMC Bioinformatics
Nathalie Harder, Maria Athelogou, Harald Hessel, Nicolas Brieu, Mehmet Yigitsoy, Johannes Zimmermann, Martin Baatz, Alexander Buchner, Christian G Stief, Thomas Kirchner, Gerd Binnig, Günter Schmidt, Ralf Huss
Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction...
March 13, 2018: Scientific Reports
Deling Wang, Jia-Rui Li, Yu-Hang Zhang, Lei Chen, Tao Huang, Yu-Dong Cai
Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors...
March 12, 2018: Genes
Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A Gutman, Jill S Barnholtz-Sloan, José E Velázquez Vega, Daniel J Brat, Lee A D Cooper
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
Xiao He, Lukas Folkman, Karsten Borgwardt
Motivation: Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (1) medical records only contain the response of a patient to very few drugs, (2) drugs are recommended by doctors based on their expert judgment, and (3) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs...
March 8, 2018: Bioinformatics
Sherri Rose
OBJECTIVE: To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. DATA SOURCES: 2011-2012 Truven MarketScan database. STUDY DESIGN: I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning...
March 11, 2018: Health Services Research
Wenjuan Ma, Yumei Zhao, Yu Ji, Xinpeng Guo, Xiqi Jian, Peifang Liu, Shandong Wu
RATIONALE AND OBJECTIVES: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions...
March 8, 2018: Academic Radiology
Tetsushi Nakajima, Kenji Katsumata, Hiroshi Kuwabara, Ryoko Soya, Masanobu Enomoto, Tetsuo Ishizaki, Akihiko Tsuchida, Masayo Mori, Kana Hiwatari, Tomoyoshi Soga, Masaru Tomita, Masahiro Sugimoto
Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to detect CRC, and an accurate pattern recognition to differentiate CRC with early stage cases from healthy controls are needed. Here, we utilized liquid chromatography triple quadrupole mass spectrometry to profile seven kinds of polyamines, such as spermine and spermidine with their acetylated forms...
March 7, 2018: International Journal of Molecular Sciences
Shane O'Sullivan, Andreas Holzinger, Dominic Wichmann, Paulo Hilario Nascimento Saldiva, Mohammed Imran Sajid, Kurt Zatloukal
No abstract text is available yet for this article.
January 2018: Autopsy & Case Reports
Enkelejda Miho, Alexander Yermanos, Cédric R Weber, Christoph T Berger, Sai T Reddy, Victor Greiff
The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape...
2018: Frontiers in Immunology
Nagesh Shukla, Markus Hagenbuchner, Khin Than Win, Jack Yang
BACKGROUND: Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. OBJECTIVE: The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties...
March 2018: Computer Methods and Programs in Biomedicine
Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang
Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data...
December 2017: Stroke and Vascular Neurology
Zinaida Good, Jolanda Sarno, Astraea Jager, Nikolay Samusik, Nima Aghaeepour, Erin F Simonds, Leah White, Norman J Lacayo, Wendy J Fantl, Grazia Fazio, Giuseppe Gaipa, Andrea Biondi, Robert Tibshirani, Sean C Bendall, Garry P Nolan, Kara L Davis
Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level...
March 5, 2018: Nature Medicine
Ryo Ogawa, Jun Nishikawa, Eizaburo Hideura, Atsushi Goto, Yurika Koto, Shunsuke Ito, Madoka Unno, Yuko Yamaoka, Ryo Kawasato, Shinichi Hashimoto, Takeshi Okamoto, Hiroyuki Ogihara, Yoshihiko Hamamoto, Isao Sakaida
PURPOSE: The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences. METHODS: Eighteen histopathologically confirmed early GC lesions were examined. We prepared images from white light endoscopy (WL), indigo carmine (Indigo), and acetic acid-indigo carmine chromoendoscopy (AIM). A border between cancerous and non-cancerous areas on endoscopic images was established from post-treatment pathological findings, and 2000 pixels with equivalent luminance values were randomly extracted from each image of cancerous and non-cancerous areas...
March 5, 2018: Journal of Gastrointestinal Cancer
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