PURPOSE: Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis. METHODS: The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively. RESULTS: 3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility. CONCLUSION: Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.
PURPOSE: Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis. METHODS: The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively. RESULTS: 3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility. CONCLUSION: Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.
Authors: Hubert Y Pan; Gary V Walker; Stephen R Grant; Pamela K Allen; Jing Jiang; B Ashleigh Guadagnolo; Benjamin D Smith; Matthew Koshy; Chad G Rusthoven; Usama Mahmood Journal: Cancer Epidemiol Biomarkers Prev Date: 2017-02-09 Impact factor: 4.254
Authors: Katrin M Sjoquist; Lindsay A Renfro; R John Simes; Niall C Tebbutt; Stephen Clarke; Matthew T Seymour; Richard Adams; Timothy S Maughan; Leonard Saltz; Richard M Goldberg; Hans-Joachim Schmoll; Eric Van Cutsem; Jean-Yves Douillard; Paulo M Hoff; Joel Randolph Hecht; Christophe Tournigand; Cornelis J A Punt; Miriam Koopman; Herbert Hurwitz; Volker Heinemann; Alfredo Falcone; Rainer Porschen; Charles Fuchs; Eduardo Diaz-Rubio; Enrique Aranda; Carsten Bokemeyer; Ioannis Souglakos; Fairooz F Kabbinavar; Benoist Chibaudel; Jeffrey P Meyers; Daniel J Sargent; Aimery de Gramont; John R Zalcberg Journal: J Natl Cancer Inst Date: 2018-06-01 Impact factor: 13.506
Authors: Shuzhen Liu; Stephen K Chia; Erika Mehl; Samuel Leung; Ashish Rajput; Maggie C U Cheang; Torsten O Nielsen Journal: Breast Cancer Res Treat Date: 2009-02-10 Impact factor: 4.872
Authors: R Largillier; J-M Ferrero; J Doyen; J Barriere; M Namer; V Mari; A Courdi; J M Hannoun-Levi; F Ettore; I Birtwisle-Peyrottes; C Balu-Maestro; P Y Marcy; I Raoust; M Lallement; E Chamorey Journal: Ann Oncol Date: 2008-07-17 Impact factor: 32.976