RE: Identification of Preoperative Magnetic Resonance Imaging Features Associated with Positive Resection Margins in Breast Cancer?

DOI QR Code

Dongzhi Cen (Department of Nuclear Medicine and Radiation Oncology, The Third Affiliated Hospital of Guangzhou Medical University) ;
Wanyan Hu (The Third Clinical School of Guangzhou Medical University) ;
Xuelin Wang (The Third Clinical School of Guangzhou Medical University) ;
Xiaohuan Wu (The Third Clinical School of Guangzhou Medical University)

This study was supported by Guangdong science and technology project (2016ZC0142).

Kang JH, Youk JH, Kim JA, Gweon HM, Eun NL, Ko KH, et al. Identification of preoperative magnetic resonance imaging features associated with positive resection margins in breast cancer: a retrospective study. Korean J Radiol 2018;19:897-904 https://doi.org/10.3348/kjr.2018.19.5.897
Chi YY, Zhou XH. The need for reorientation toward cost-effective prediction: comments on 'Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond' by Pencina et al., statistics in medicine (DOI: 10.1002/sim.2929). Stat Med 2008;27:182-184
van Smeden M, Moons KG, de Groot JA, Collins GS, Altman DG, Eijkemans MJ, et al. Sample size for binary logistic prediction models: beyond events per variable criteria. Stat Methods Med Res 2018 Jan 1 [Epub ahead of print]. https://doi.org/10.1177/0962280218784726
Wynants L, Bouwmeester W, Moons KG, Moerbeek M, Timmerman D, Van Huffel S, et al. A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data. J Clin Epidemiol 2015;68:1406-1414 https://doi.org/10.1016/j.jclinepi.2015.02.002
Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol 1995;48:1495-1501