000147182 001__ 147182
000147182 005__ 20240229112642.0
000147182 0247_ $$2doi$$a10.3390/cancers11101435
000147182 0247_ $$2pmid$$apmid:31561507
000147182 037__ $$aDKFZ-2019-02318
000147182 041__ $$aeng
000147182 082__ $$a610
000147182 1001_ $$0P:(DE-He78)657300dfd28903ec8149ca9bf5e7968d$$aBoakye, Daniel$$b0$$eFirst author$$udkfz
000147182 245__ $$aPersonalizing the Prediction of Colorectal Cancer Prognosis by Incorporating Comorbidities and Functional Status into Prognostic Nomograms.
000147182 260__ $$aBasel$$bMDPI$$c2019
000147182 3367_ $$2DRIVER$$aarticle
000147182 3367_ $$2DataCite$$aOutput Types/Journal article
000147182 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1577104247_15304
000147182 3367_ $$2BibTeX$$aARTICLE
000147182 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000147182 3367_ $$00$$2EndNote$$aJournal Article
000147182 520__ $$aDespite consistent evidence that comorbidities and functional status (FS) are strong prognostic factors for colorectal cancer (CRC) patients, these important characteristics are not considered in prognostic nomograms. We assessed to what extent incorporating these characteristics into prognostic models enhances prediction of CRC prognosis. CRC patients diagnosed in 2003-2014 who were recruited into a population-based study in Germany and followed over a median time of 4.7 years were randomized into training (n = 1608) and validation sets (n = 1071). In the training set, Cox models with predefined variables (age, sex, stage, tumor location, comorbidity scores, and FS) were used to construct nomograms for relevant survival outcomes. The performance of the nomograms, compared to models without comorbidity and FS, was evaluated in the validation set using concordance index (C-index). The C-indexes of the nomograms for overall and disease-free survival in the validation set were 0.768 and 0.737, which were substantially higher than those of models including tumor stage only (0.707 and 0.701) or models including stage, age, sex, and tumor location (0.749 and 0.718). The nomograms enabled significant risk stratification within all stages including stage IV. Our study suggests that incorporating comorbidities and FS into prognostic nomograms could substantially enhance prediction of CRC prognosis.
000147182 536__ $$0G:(DE-HGF)POF3-313$$a313 - Cancer risk factors and prevention (POF3-313)$$cPOF3-313$$fPOF III$$x0
000147182 588__ $$aDataset connected to CrossRef, PubMed,
000147182 7001_ $$0P:(DE-He78)bbfe0ebad1e3b608bca2b49d4f86bd09$$aJansen, Lina$$b1$$udkfz
000147182 7001_ $$aSchneider, Martin$$b2
000147182 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b3$$udkfz
000147182 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b4$$udkfz
000147182 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b5$$eLast author$$udkfz
000147182 773__ $$0PERI:(DE-600)2527080-1$$a10.3390/cancers11101435$$gVol. 11, no. 10, p. 1435 -$$n10$$p1435$$tCancers$$v11$$x2072-6694$$y2019
000147182 909CO $$ooai:inrepo02.dkfz.de:147182$$pVDB
000147182 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)657300dfd28903ec8149ca9bf5e7968d$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000147182 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)bbfe0ebad1e3b608bca2b49d4f86bd09$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000147182 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000147182 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000147182 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000147182 9131_ $$0G:(DE-HGF)POF3-313$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vCancer risk factors and prevention$$x0
000147182 9141_ $$y2019
000147182 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCANCERS : 2017
000147182 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000147182 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000147182 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000147182 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central
000147182 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000147182 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000147182 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review
000147182 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ
000147182 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000147182 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000147182 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000147182 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000147182 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000147182 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000147182 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bCANCERS : 2017
000147182 9201_ $$0I:(DE-He78)C070-20160331$$kC070$$lKlinische Epidemiologie und Alternsforschung$$x0
000147182 9201_ $$0I:(DE-He78)C120-20160331$$kC120$$lPräventive Onkologie$$x1
000147182 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lEpidemiologie von Krebserkrankungen$$x2
000147182 9201_ $$0I:(DE-He78)L101-20160331$$kL101$$lDKTK Heidelberg$$x3
000147182 980__ $$ajournal
000147182 980__ $$aVDB
000147182 980__ $$aI:(DE-He78)C070-20160331
000147182 980__ $$aI:(DE-He78)C120-20160331
000147182 980__ $$aI:(DE-He78)C020-20160331
000147182 980__ $$aI:(DE-He78)L101-20160331
000147182 980__ $$aUNRESTRICTED