486 Towards personalized breast cancer follow‐up: Prediction model for recurrence and allocation of visits during 10 years of follow‐up
Friday, 11 March 2016 Poster Abstracts
A. Witteveen J.W. Ruiter, R. Bretveld, C.G.M. Groothuis-Oudshoorn, I.M.H. Vliegen, M.J. IJzerman, S. Siesling University of Twente, Health Technology and Services Research HTSR- MIRA institute for Biomedical Technology and Technical Medicine, Enschede, Netherlands; Mayo Clinic, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, USA; Netherlands
Comprehensive Cancer Organisation IKNL, Research, Utrecht, Netherlands; University of Twente, Industrial Engineering and Business Information Systems IEBIS- Centre for Healthcare Operations Improvement & Research, Enschede, Netherlands
The aim of this nationwide population based study was to analyze recurrence patterns and define predictive factors for locoregional recurrence (LRR) and subsequent recurrences up to ten years after the primary tumor. In the Netherlands, follow-up takes place for five years after primary treatment with a yearly mammography. Using risk thresholds, follow-up visits can be reallocated based on risk over the ten years following primary treatment.
Women diagnosed with primary invasive breast cancer in 2003 with no distant metastasis, previous or synchronous tumors and curatively treated with surgery were selected from the Netherlands Cancer Registry (N = 8,035). Survival analysis was performed to identify predictive factors for LRR. Predictors for the second and third recurrence after a LRR were assessed as well. Based on the current follow-up, the lower risk boundary and quantiles for intervals were determined. With these thresholds
redistribution of the visits was established for a low, middle and high risk group, over ten years of follow-up.
During ten years of follow-up 509 (6.3%) of the 8,035 patients developed a LRR as a first event. Predictive factors for first LRR can be found in Table 1. The chances of developing a second and third event after a LRR were 41% and 48% respectively. Most important predictors for all types (LRR/second primary/metastasis) of second recurrence were tumor size, surgery type and radiotherapy, with the effect of the last two reversed compared to the first recurrence (Table 1). The predictors for second LRR and third recurrence were all non-significant.
The cumulative hazard of the population after five year was 0.0448, resulting in a hazard interval of 0.00896 per visit. The hazard after five years was 0.0068, which constituted the lower boundary for follow-up. Given the used thresholds, the medium (<50, hormone therapy) risk group should receive two follow-up visits and the high group (>50, no hormone therapy) seven during the follow-up period of ten years. The low risk group (>50, hormone therapy) remained below the threshold for all the ten years.
Table 1. Hazard rates from the Cox regression analyses a
Surgery type (breast conserving / mastectomy)
First recurrence (LRR) Second recurrence (all types)
First recurrence (LRR) Second recurrence (all types)
Age (⩽50 / >50) − / 0.70 − / 0.78
Size (⩽2 cm / >2 cm) − / 1.47 − / 1.45
Grade (I / II / III) − / 1.12 / 1.77 − / 0.74 / 0.79
Lymph nodes (0 / 1–3 / >3) − / 1.65 / 3.29 − / 0.78 / 0.65 Hormone status (non-negative /
negative)
− / 1.16 − / 1.21
mastectomy)
Radiotherapy (yes / no) − / 1.92 − / 0.51
Hormone therapy (yes / no) − / 2.07 − / 1.01
Chemotherapy (yes / no) − / 1.69 − / 1.00
a Values in Bold indicate a significant difference.
This study developed a prediction model for LRR risk for up to ten years of follow-up. The model can be used to identify patients with a low or high risk to personalize follow-up after breast cancer, develop a decision support tool and allocate resources efficiently.
No conflicts of interest
