000154473 001__ 154473
000154473 005__ 20240229123103.0
000154473 0247_ $$2doi$$a10.1007/s10549-020-05611-8
000154473 0247_ $$2pmid$$apmid:32279280
000154473 0247_ $$2ISSN$$a0167-6806
000154473 0247_ $$2ISSN$$a1573-7217
000154473 0247_ $$2altmetric$$aaltmetric:79669796
000154473 037__ $$aDKFZ-2020-00795
000154473 041__ $$aeng
000154473 082__ $$a610
000154473 1001_ $$aGiardiello, Daniele$$b0
000154473 245__ $$aPrediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts.
000154473 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V.$$c2020
000154473 3367_ $$2DRIVER$$aarticle
000154473 3367_ $$2DataCite$$aOutput Types/Journal article
000154473 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1597931279_10393
000154473 3367_ $$2BibTeX$$aARTICLE
000154473 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000154473 3367_ $$00$$2EndNote$$aJournal Article
000154473 500__ $$a2020 Jun;181(2):423-434
000154473 520__ $$aThree tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula.Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.
000154473 536__ $$0G:(DE-HGF)POF3-319H$$a319H - Addenda (POF3-319H)$$cPOF3-319H$$fPOF III$$x0
000154473 588__ $$aDataset connected to CrossRef, PubMed,
000154473 7001_ $$aHauptmann, Michael$$b1
000154473 7001_ $$aSteyerberg, Ewout W$$b2
000154473 7001_ $$aAdank, Muriel A$$b3
000154473 7001_ $$aAkdeniz, Delal$$b4
000154473 7001_ $$aBlom, Jannet C$$b5
000154473 7001_ $$aBlomqvist, Carl$$b6
000154473 7001_ $$aBojesen, Stig E$$b7
000154473 7001_ $$aBolla, Manjeet K$$b8
000154473 7001_ $$aBrinkhuis, Mariël$$b9
000154473 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b10$$udkfz
000154473 7001_ $$aCzene, Kamila$$b11
000154473 7001_ $$aDevilee, Peter$$b12
000154473 7001_ $$aDunning, Alison M$$b13
000154473 7001_ $$aEaston, Douglas F$$b14
000154473 7001_ $$aEccles, Diana M$$b15
000154473 7001_ $$aFasching, Peter A$$b16
000154473 7001_ $$aFigueroa, Jonine$$b17
000154473 7001_ $$aFlyger, Henrik$$b18
000154473 7001_ $$aGarcía-Closas, Montserrat$$b19
000154473 7001_ $$aHaeberle, Lothar$$b20
000154473 7001_ $$aHaiman, Christopher A$$b21
000154473 7001_ $$aHall, Per$$b22
000154473 7001_ $$0P:(DE-He78)537e07b3e57b16c7b214fc2242e4326b$$aHamann, Ute$$b23$$udkfz
000154473 7001_ $$aHopper, John L$$b24
000154473 7001_ $$aJager, Agnes$$b25
000154473 7001_ $$aJakubowska, Anna$$b26
000154473 7001_ $$0P:(DE-He78)bce1fdec5ce564e2666156d96aeabec9$$aJung, Audrey$$b27$$udkfz
000154473 7001_ $$aKeeman, Renske$$b28
000154473 7001_ $$aKoppert, Linetta B$$b29
000154473 7001_ $$aKramer, Iris$$b30
000154473 7001_ $$aLambrechts, Diether$$b31
000154473 7001_ $$aLe Marchand, Loic$$b32
000154473 7001_ $$aLindblom, Annika$$b33
000154473 7001_ $$aLubiński, Jan$$b34
000154473 7001_ $$0P:(DE-He78)16b8745cffb0db0d366978d3afe17ebc$$aManoochehri, Mehdi$$b35$$udkfz
000154473 7001_ $$aMariani, Luigi$$b36
000154473 7001_ $$aNevanlinna, Heli$$b37
000154473 7001_ $$aOldenburg, Hester S A$$b38
000154473 7001_ $$aPelders, Saskia$$b39
000154473 7001_ $$aPharoah, Paul D P$$b40
000154473 7001_ $$aShah, Mitul$$b41
000154473 7001_ $$aSiesling, Sabine$$b42
000154473 7001_ $$aSmit, Vincent T H B M$$b43
000154473 7001_ $$aSouthey, Melissa C$$b44
000154473 7001_ $$aTapper, William J$$b45
000154473 7001_ $$aTollenaar, Rob A E M$$b46
000154473 7001_ $$avan den Broek, Alexandra J$$b47
000154473 7001_ $$avan Deurzen, Carolien H M$$b48
000154473 7001_ $$avan Leeuwen, Flora E$$b49
000154473 7001_ $$avan Ongeval, Chantal$$b50
000154473 7001_ $$aVan't Veer, Laura J$$b51
000154473 7001_ $$aWang, Qin$$b52
000154473 7001_ $$aWendt, Camilla$$b53
000154473 7001_ $$aWestenend, Pieter J$$b54
000154473 7001_ $$aHooning, Maartje J$$b55
000154473 7001_ $$aSchmidt, Marjanka K$$b56
000154473 773__ $$0PERI:(DE-600)2004077-5$$a10.1007/s10549-020-05611-8$$n2$$p423-434$$tBreast cancer research and treatment$$v181$$x1573-7217$$y2020
000154473 909CO $$ooai:inrepo02.dkfz.de:154473$$pVDB
000154473 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aDeutsches Krebsforschungszentrum$$b10$$kDKFZ
000154473 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)537e07b3e57b16c7b214fc2242e4326b$$aDeutsches Krebsforschungszentrum$$b23$$kDKFZ
000154473 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)bce1fdec5ce564e2666156d96aeabec9$$aDeutsches Krebsforschungszentrum$$b27$$kDKFZ
000154473 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)16b8745cffb0db0d366978d3afe17ebc$$aDeutsches Krebsforschungszentrum$$b35$$kDKFZ
000154473 9131_ $$0G:(DE-HGF)POF3-319H$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vAddenda$$x0
000154473 9141_ $$y2020
000154473 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000154473 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bBREAST CANCER RES TR : 2017
000154473 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000154473 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000154473 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000154473 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000154473 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000154473 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000154473 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000154473 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine
000154473 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000154473 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000154473 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000154473 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lC020 Epidemiologie von Krebs$$x0
000154473 9201_ $$0I:(DE-He78)B072-20160331$$kB072$$lB072 Molekulargenetik des Mammarzinoms$$x1
000154473 980__ $$ajournal
000154473 980__ $$aVDB
000154473 980__ $$aI:(DE-He78)C020-20160331
000154473 980__ $$aI:(DE-He78)B072-20160331
000154473 980__ $$aUNRESTRICTED