TY  - JOUR
AU  - Giardiello, Daniele
AU  - Hooning, Maartje J
AU  - Hauptmann, Michael
AU  - Keeman, Renske
AU  - Heemskerk-Gerritsen, B. A. M.
AU  - Becher, Heiko
AU  - Blomqvist, Carl
AU  - Bojesen, Stig E
AU  - Bolla, Manjeet K
AU  - Camp, Nicola J
AU  - Czene, Kamila
AU  - Devilee, Peter
AU  - Eccles, Diana M
AU  - Fasching, Peter A
AU  - Figueroa, Jonine D
AU  - Flyger, Henrik
AU  - García-Closas, Montserrat
AU  - Haiman, Christopher A
AU  - Hamann, Ute
AU  - Hopper, John L
AU  - Jakubowska, Anna
AU  - Leeuwen, Floor E
AU  - Lindblom, Annika
AU  - Lubiński, Jan
AU  - Margolin, Sara
AU  - Martinez, Maria Elena
AU  - Nevanlinna, Heli
AU  - Nevelsteen, Ines
AU  - Pelders, Saskia
AU  - Pharoah, Paul D P
AU  - Siesling, Sabine
AU  - Southey, Melissa C
AU  - van der Hout, Annemieke H
AU  - van Hest, Liselotte P
AU  - Chang-Claude, Jenny
AU  - Hall, Per
AU  - Easton, Douglas F
AU  - Steyerberg, Ewout W
AU  - Schmidt, Marjanka K
TI  - PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in   200,000 patients.
JO  - Breast cancer research
VL  - 24
IS  - 1
SN  - 1465-5411
CY  - London
PB  - BioMed Central
M1  - DKFZ-2022-02502
SP  - 69
PY  - 2022
AB  - Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95
KW  - BCAC (Other)
KW  - BRCA1/2 germline mutation (Other)
KW  - Breast Cancer Association Consortium (Other)
KW  - Breast cancer genetic predisposition (Other)
KW  - Clinical decision-making (Other)
KW  - Contralateral breast cancer (Other)
KW  - Contralateral preventive mastectomy (Other)
KW  - Polygenic risk score (Other)
KW  - Prediction performance (Other)
KW  - Risk prediction (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:36271417
DO  - DOI:10.1186/s13058-022-01567-3
UR  - https://inrepo02.dkfz.de/record/182208
ER  -