Base de dados : MEDLINE
Pesquisa : G17.035.250.500 [Categoria DeCS]
Referências encontradas : 2349 [refinar]
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[PMID]:28461067
[Au] Autor:Dunn AG; Surian D; Leask J; Dey A; Mandl KD; Coiera E
[Ad] Endereço:Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia. Electronic address: adam.dunn@mq.edu.au.
[Ti] Título:Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States.
[So] Source:Vaccine;35(23):3033-3040, 2017 05 25.
[Is] ISSN:1873-2518
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States. METHODS: We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models. RESULTS: Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68% of the variance in one dose 2015 HPV vaccine coverage in females (males: 63%). In comparison, models based on socioeconomic factors explained 42% of the variance in females (males: 40%). CONCLUSIONS: Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance.
[Mh] Termos MeSH primário: Infecções por Papillomavirus/prevenção & controle
Vacinas contra Papillomavirus/administração & dosagem
Mídias Sociais
Cobertura Vacinal
[Mh] Termos MeSH secundário: Feminino
Seres Humanos
Aprendizado de Máquina
Masculino
Aceitação pelo Paciente de Cuidados de Saúde
Fatores Socioeconômicos
Estados Unidos
Recusa de Vacinação
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Papillomavirus Vaccines)
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170503
[St] Status:MEDLINE


  2 / 2349 MEDLINE  
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[PMID]:28451691
[Au] Autor:Zvára K; Tomecková M; Peleska J; Svátek V; Zvárová J
[Ti] Título:Tool-supported Interactive Correction and Semantic Annotation of Narrative Clinical Reports.
[So] Source:Methods Inf Med;56(3):217-229, 2017 May 18.
[Is] ISSN:2511-705X
[Cp] País de publicação:Germany
[La] Idioma:eng
[Ab] Resumo:OBJECTIVES: Our main objective is to design a method of, and supporting software for, interactive correction and semantic annotation of narrative clinical reports, which would allow for their easier and less erroneous processing outside their original context: first, by physicians unfamiliar with the original language (and possibly also the source specialty), and second, by tools requiring structured information, such as decision-support systems. Our additional goal is to gain insights into the process of narrative report creation, including the errors and ambiguities arising therein, and also into the process of report annotation by clinical terms. Finally, we also aim to provide a dataset of ground-truth transformations (specific for Czech as the source language), set up by expert physicians, which can be reused in the future for subsequent analytical studies and for training automated transformation procedures. METHODS: A three-phase preprocessing method has been developed to support secondary use of narrative clinical reports in electronic health record. Narrative clinical reports are narrative texts of healthcare documentation often stored in electronic health records. In the first phase a narrative clinical report is tokenized. In the second phase the tokenized clinical report is normalized. The normalized clinical report is easily readable for health professionals with the knowledge of the language used in the narrative clinical report. In the third phase the normalized clinical report is enriched with extracted structured information. The final result of the third phase is a semi-structured normalized clinical report where the extracted clinical terms are matched to codebook terms. Software tools for interactive correction, expansion and semantic annotation of narrative clinical reports has been developed and the three-phase preprocessing method validated in the cardiology area. RESULTS: The three-phase preprocessing method was validated on 49 anonymous Czech narrative clinical reports in the field of cardiology. Descriptive statistics from the database of accomplished transformations has been calculated. Two cardiologists participated in the annotation phase. The first cardiologist annotated 1500 clinical terms found in 49 narrative clinical reports to codebook terms using the classification systems ICD 10, SNOMED CT, LOINC and LEKY. The second cardiologist validated annotations of the first cardiologist. The correct clinical terms and the codebook terms have been stored in a database. CONCLUSIONS: We extracted structured information from Czech narrative clinical reports by the proposed three-phase preprocessing method and linked it to electronic health records. The software tool, although generic, is tailored for Czech as the specific language of electronic health record pool under study. This will provide a potential etalon for porting this approach to dozens of other less-spoken languages. Structured information can support medical decision making, quality assurance tasks and further medical research.
[Mh] Termos MeSH primário: Registros Eletrônicos de Saúde/normas
Aprendizado de Máquina
Processamento de Linguagem Natural
Semântica
Vocabulário Controlado
Processamento de Texto/normas
Redação/normas
[Mh] Termos MeSH secundário: Acurácia dos Dados
Guias como Assunto
Classificação Internacional de Doenças
Uso Significativo/normas
Software
Interface Usuário-Computador
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170429
[St] Status:MEDLINE
[do] DOI:10.3414/ME16-01-0083


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[PMID]:29244011
[Au] Autor:Yu N; Yu Z; Pan Y
[Ad] Endereço:Department of Computing Sciences, The College at Brockport, State University of New York, 350 New Campus Drive, Brockport, 14420, NY, USA. nyu@brockport.edu.
[Ti] Título:A deep learning method for lincRNA detection using auto-encoder algorithm.
[So] Source:BMC Bioinformatics;18(Suppl 15):511, 2017 Dec 06.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. RESULTS: The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. CONCLUSIONS: The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.
[Mh] Termos MeSH primário: Algoritmos
Biologia Computacional/métodos
Aprendizado de Máquina
RNA Longo não Codificante/genética
Análise de Sequência de RNA/métodos
[Mh] Termos MeSH secundário: Seres Humanos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (RNA, Long Noncoding)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180307
[Lr] Data última revisão:
180307
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171216
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-017-1922-3


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[PMID]:28465242
[Au] Autor:Lee JH; Lee SH; Baek C; Chun H; Ryu JH; Kim JW; Deaton R; Zhang BT
[Ad] Endereço:Graduate Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
[Ti] Título:In vitro molecular machine learning algorithm via symmetric internal loops of DNA.
[So] Source:Biosystems;158:1-9, 2017 Aug.
[Is] ISSN:1872-8324
[Cp] País de publicação:Ireland
[La] Idioma:eng
[Ab] Resumo:Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules.
[Mh] Termos MeSH primário: Algoritmos
DNA
Simulação de Dinâmica Molecular
[Mh] Termos MeSH secundário: Animais
Seres Humanos
Lógica
Aprendizado de Máquina
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
9007-49-2 (DNA)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180307
[Lr] Data última revisão:
180307
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170504
[St] Status:MEDLINE


  5 / 2349 MEDLINE  
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[PMID]:29295994
[Au] Autor:Mardt A; Pasquali L; Wu H; Noé F
[Ad] Endereço:Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
[Ti] Título:VAMPnets for deep learning of molecular kinetics.
[So] Source:Nat Commun;9(1):5, 2018 01 02.
[Is] ISSN:2041-1723
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.
[Mh] Termos MeSH primário: Algoritmos
Aprendizado de Máquina
Cadeias de Markov
Simulação de Dinâmica Molecular
Redes Neurais (Computação)
[Mh] Termos MeSH secundário: Cinética
Ligação Proteica
Dobramento de Proteína
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180306
[Lr] Data última revisão:
180306
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180104
[St] Status:MEDLINE
[do] DOI:10.1038/s41467-017-02388-1


  6 / 2349 MEDLINE  
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[PMID]:29191179
[Au] Autor:Fierst JL; Murdock DA
[Ad] Endereço:Department of Biological Sciences, University of Alabama, Tuscaloosa, 35487, AL, USA. jlfierst@ua.edu.
[Ti] Título:Decontaminating eukaryotic genome assemblies with machine learning.
[So] Source:BMC Bioinformatics;18(1):533, 2017 Dec 01.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism. RESULTS: We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols. CONCLUSIONS: A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.
[Mh] Termos MeSH primário: Caenorhabditis/genética
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Animais
Composição de Bases
DNA de Helmintos/química
DNA de Helmintos/isolamento & purificação
DNA de Helmintos/metabolismo
Bases de Dados Genéticas
Genoma
Sequenciamento de Nucleotídeos em Larga Escala
Análise de Sequência de DNA
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (DNA, Helminth)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180306
[Lr] Data última revisão:
180306
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171202
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-017-1941-0


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[PMID]:29335532
[Au] Autor:Brasko C; Smith K; Molnar C; Farago N; Hegedus L; Balind A; Balassa T; Szkalisity A; Sukosd F; Kocsis K; Balint B; Paavolainen L; Enyedi MZ; Nagy I; Puskas LG; Haracska L; Tamas G; Horvath P
[Ad] Endereço:University of Szeged, Szeged, Hungary Közép fasor 52, 6726, Szeged, Hungary.
[Ti] Título:Intelligent image-based in situ single-cell isolation.
[So] Source:Nat Commun;9(1):226, 2018 01 15.
[Is] ISSN:2041-1723
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.
[Mh] Termos MeSH primário: Separação Celular/métodos
Processamento de Imagem Assistida por Computador/métodos
Microscopia Confocal/métodos
Análise de Célula Única/métodos
[Mh] Termos MeSH secundário: Animais
Células Cultivadas
Perfilação da Expressão Gênica
Seres Humanos
Aprendizado de Máquina
Células Piramidais/citologia
Células Piramidais/metabolismo
Reprodutibilidade dos Testes
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180305
[Lr] Data última revisão:
180305
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180117
[St] Status:MEDLINE
[do] DOI:10.1038/s41467-017-02628-4


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[PMID]:29331743
[Au] Autor:Beccaria M; Mellors TR; Petion JS; Rees CA; Nasir M; Systrom HK; Sairistil JW; Jean-Juste MA; Rivera V; Lavoile K; Severe P; Pape JW; Wright PF; Hill JE
[Ad] Endereço:Thayer School of Engineering, Dartmouth College, Hanover, NH, USA.
[Ti] Título:Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning.
[So] Source:J Chromatogr B Analyt Technol Biomed Life Sci;1074-1075:46-50, 2018 Feb 01.
[Is] ISSN:1873-376X
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection. Human breath has been evaluated in the setting of active tuberculosis using thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology. From the entire spectrum of volatile metabolites in breath, three random forest machine learning models were applied leading to the generation of a panel of 46 breath features. The twenty-two common features within each random forest model used were selected as a set that could distinguish subjects with confirmed pulmonary M. tuberculosis infection and people with other pathologies than TB.
[Mh] Termos MeSH primário: Testes Respiratórios/métodos
Cromatografia Gasosa-Espectrometria de Massas/métodos
Aprendizado de Máquina
Tuberculose/diagnóstico
Compostos Orgânicos Voláteis/análise
[Mh] Termos MeSH secundário: Adolescente
Adulto
Feminino
Seres Humanos
Masculino
Meia-Idade
Compostos Orgânicos Voláteis/química
Adulto Jovem
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Volatile Organic Compounds)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180305
[Lr] Data última revisão:
180305
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180115
[St] Status:MEDLINE


  9 / 2349 MEDLINE  
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[PMID]:29298978
[Au] Autor:Lee SI; Celik S; Logsdon BA; Lundberg SM; Martins TJ; Oehler VG; Estey EH; Miller CP; Chien S; Dai J; Saxena A; Blau CA; Becker PS
[Ad] Endereço:Paul G. Allen School of Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA, 98195, USA. suinlee@cs.washington.edu.
[Ti] Título:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
[So] Source:Nat Commun;9(1):42, 2018 01 03.
[Is] ISSN:2041-1723
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
[Mh] Termos MeSH primário: DNA Helicases/genética
Resistência a Medicamentos Antineoplásicos/genética
Leucemia Mieloide Aguda/genética
Aprendizado de Máquina
Proteínas Nucleares/genética
Medicina de Precisão/métodos
Fatores de Transcrição/genética
[Mh] Termos MeSH secundário: Algoritmos
Antineoplásicos/farmacologia
Antineoplásicos/uso terapêutico
Biomarcadores Tumorais/metabolismo
Linhagem Celular
Conjuntos de Dados como Assunto
Etoposídeo/farmacologia
Etoposídeo/uso terapêutico
Seres Humanos
Leucemia Mieloide Aguda/tratamento farmacológico
Inibidores da Topoisomerase II/farmacologia
Inibidores da Topoisomerase II/uso terapêutico
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, N.I.H., EXTRAMURAL; RESEARCH SUPPORT, NON-U.S. GOV'T; RESEARCH SUPPORT, U.S. GOV'T, NON-P.H.S.
[Nm] Nome de substância:
0 (Antineoplastic Agents); 0 (Biomarkers, Tumor); 0 (Nuclear Proteins); 0 (Topoisomerase II Inhibitors); 0 (Transcription Factors); 6PLQ3CP4P3 (Etoposide); EC 3.6.1.- (SMARCA4 protein, human); EC 3.6.4.- (DNA Helicases)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180305
[Lr] Data última revisão:
180305
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180105
[St] Status:MEDLINE
[do] DOI:10.1038/s41467-017-02465-5


  10 / 2349 MEDLINE  
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[PMID]:29234806
[Au] Autor:Ehteshami Bejnordi B; Veta M; Johannes van Diest P; van Ginneken B; Karssemeijer N; Litjens G; van der Laak JAWM; Hermsen M; Manson QF; Balkenhol M; Geessink O; Stathonikos N; van Dijk MC; Bult P; Beca F; Beck AH; Wang D; Khosla A; Gargeya R; Irshad H; Zhong A; Dou Q; Li Q; Chen H; Lin HJ; Heng PA; Haß C; Bruni E; Wong Q; Halici U; Öner MÜ; Cetin-Atalay R; Berseth M; Khvatkov V; Vylegzhanin A; Kraus O; Shaban M; Rajpoot N; Awan R; Sirinukunwattana K; Qaiser T; Tsang YW; Tellez D; Annuscheit J; Hufnagl P; Valkonen M; Kartasalo K; Latonen L; Ruusuvuori P; Liimatainen K; the CAMELYON16 Consortium
[Ad] Endereço:Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.
[Ti] Título:Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
[So] Source:JAMA;318(22):2199-2210, 2017 12 12.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
[Mh] Termos MeSH primário: Neoplasias da Mama/patologia
Metástase Linfática/diagnóstico
Aprendizado de Máquina
Patologistas
[Mh] Termos MeSH secundário: Algoritmos
Feminino
Seres Humanos
Metástase Linfática/patologia
Patologia Clínica
Curva ROC
[Pt] Tipo de publicação:COMPARATIVE STUDY; JOURNAL ARTICLE
[Em] Mês de entrada:1712
[Cu] Atualização por classe:180228
[Lr] Data última revisão:
180228
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171214
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.14585



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