Abstract
BackgroundPrognostic factors in breast cancer are often measured on a continuous scale, but categorized for clinical decision-making. The primary aim of this study was to evaluate if accounting for continuous non-linear effects of the three factors age at diagnosis, tumor size, and number of positive lymph nodes improves prognostication. These factors will most likely be included in the management of breast cancer patients also in the future, after an expected implementation of gene expression profiling for adjuvant treatment decision-making.MethodsFour thousand four hundred forty seven and 1132 women with primary breast cancer constituted the derivation and validation set, respectively. Potential non-linear effects on the log hazard of distant recurrences of the three factors were evaluated during 10 years of follow-up. Cox-models of successively increasing complexity: dichotomized predictors, predictors categorized into three or four groups, and predictors transformed using fractional polynomials (FPs) or restricted cubic splines (RCS), were used. Predictive performance was evaluated by Harrell’s C-index.ResultsUsing FP-transformations, non-linear effects were detected for tumor size and number of positive lymph nodes in univariable analyses. For age, non-linear transformations did, however, not improve the model fit significantly compared to the linear identity transformation. As expected, the C-index increased with increasing model complexity for multivariable models including the three factors. By allowing more than one cut-point per factor, the C-index increased from 0.628 to 0.674. The additional gain, as measured by the C-index, when using FP- or RCS-transformations was modest (0.695 and 0.696, respectively). The corresponding C-indices for these four models in the validation set, based on the same transformations and parameter estimates from the derivation set, were 0.675, 0.700, 0.706, and 0.701.ConclusionsCategorization of each factor into three to four groups was found to improve prognostication compared to dichotomization. The additional gain by allowing continuous non-linear effects modeled by FPs or RCS was modest. However, the continuous nature of these transformations has the advantage of making it possible to form risk groups of any size.
Highlights
Prognostic factors in breast cancer are often measured on a continuous scale, but categorized for clinical decision-making
The use of multiple cut-points per factor, like e.g. T0 to Tumor size > mm (T3) for tumor size in the Tumor node metastasis (TNM) system is a step in the right direction, but how should cut-points be chosen for new prognostic factors? Optimal cut-offs, maximizing the prognostic value of a new factor in a specific dataset, will in general lead to biased effect estimates, even though methods have been designed to deal with this problem [10]
In the final fractional polynomial (FP)-analyses, non-linear effects were detected for tumor size and number of positive lymph nodes, but not for age, see Fig. 3a-c
Summary
Prognostic factors in breast cancer are often measured on a continuous scale, but categorized for clinical decision-making. The primary aim of this study was to evaluate if accounting for continuous non-linear effects of the three factors age at diagnosis, tumor size, and number of positive lymph nodes improves prognostication These factors will most likely be included in the management of breast cancer patients in the future, after an expected implementation of gene expression profiling for adjuvant treatment decision-making. Prognostic and treatment predictive factors in breast cancer (e.g. number of positive lymph nodes, age at diagnosis, tumor size, estrogen receptor (ER) and progesterone receptor (PgR), histological grade, and human epidermal growth factor receptor type 2 (HER2)) can predict clinical outcome and facilitate treatment choice [1, 2]. Pre-defined percentile-based cut-offs can be used, but different percentiles might be prognostically useful for different factors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
