Ontology type: schema:ScholarlyArticle Open Access: True
2016-11
AUTHORSLobna Ouldamer, Sofiane Bendifallah, Gilles Body, Cyril Touboul, Olivier Graesslin, Emilie Raimond, Pierre Collinet, Charles Coutant, Vincent Lavoué, Jean Lévêque, Emile Daraï, Marcos Ballester
ABSTRACTBACKGROUND: The purpose of this study was to develop a nomogram to predict 'poor prognosis recurrence' (PPR) in women treated for endometrial cancer (EC). METHODS: The data of 861 women who received primary surgical treatment between January 2001 and December 2013 were abstracted from a prospective multicenter database. Data were randomly split into two sets: training and validation with a predefined 2/3 ratio. A Cox proportional hazards multivariate model of selected prognostic features was performed in the training cohort (n=574) to develop a nomogram predicting PPRs. The nomogram was validated in the validation cohort of 287 patients. RESULTS: In the training cohort, 82 (14.3%) developed subsequent PPR. Age, histologic type and grade, lymphovascular space invasion status, FIGO stage, and nodal staging (SLN±pelvic and/or para-aortic lymphadenectomy) were independently associated with subsequent PPR. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.82 (95% confidence interval (CI), 0.73-0.89) in the training set. The validation set showed a good discrimination with an AUC of 0.75 (95% CI, 0.65-0.83). CONCLUSIONS: We have developed a robust tool that is able to predict subsequent PPRs in women with FIGO I-III EC. More... »
PAGES1296
http://scigraph.springernature.com/pub.10.1038/bjc.2016.337
DOIhttp://dx.doi.org/10.1038/bjc.2016.337
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/27824810
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"sdDataset": "articles",
"sdDatePublished": "2019-04-10T14:16",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8660_00000549.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/bjc2016337"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/bjc.2016.337'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/bjc.2016.337'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/bjc.2016.337'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/bjc.2016.337'
This table displays all metadata directly associated to this object as RDF triples.
303 TRIPLES
21 PREDICATES
71 URIs
29 LITERALS
17 BLANK NODES