Abstract P4-15-01: Integration of clinical and pathological data with the DCIS score to predict the risk of local recurrence
Authors: Paszat L , Sutradhar R , Zhou L , Lalani N , Nofech-Mozes S , Rakovitch E 
Published: February 2018

Abstract
Background: Prediction of local recurrence (LR) risk after breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) is needed to guide decisions regarding risks and benefits of adjuvant radiotherapy (RT). We aim to determine the optimal combination of clinical and pathological characteristics with the Oncotype DCIS Score (DS) to predict individualized 10 year risks of local recurrence (LR) after BCS (with or without RT) for DCIS and develop a web-based nomogram / risk calculator.
Methods: DS (continuous, categorical risk groups low/intermediate/high) and complete clinico-pathological data (age, tumor size, nuclear grade, comedonecrosis, multifocality, margin width and receipt of breast RT) are available for 1102 cases from the Ontario population cohort of pure DCIS treated by BCS (981 cases with negative margins, 121 cases with positive margins). We examined various categorizations of discrete variables, and transformations of continuous variables, and used model selection procedures to determine the best fitting Cox proportional hazards regression model of LR according to the c-statistic, Akaike Information Criterion, and log-likelihood. We tested all two-way interactions and interactions with time. The 10-year probability of LR was calculated for each woman using the estimate of the baseline survival function and the estimate of the linear predictor, which is a function of the regression parameter estimates and specific covariate values. Model calibration will be explored by comparing observed versus predicted risk of LR, and the model's discriminative ability will be assessed by the concordance index. Model validation will be conducted via bootstrapping approaches.
Results: In the best fitting main effects full model, the adjusted hazard ratios (HR) (95% confidence intervals (CI)) for LR included: intermediate/ high risk DS vs. low risk (HR =1.96 (1.39, 2.74)), age &lt; 50 years at diagnosis vs. age&gt;= 50 (HR = 1.62 (1.16, 2.25)), square root of tumor size (HR/mm = 1.24 (1.11, 1.38)), comedonecrosis &gt; 30% vs. &lt;=30% (HR=1.53 (1.08, 2.16)), multifocality ( HR=2.01 (1.45, 2.77)), and receipt of RT ( HR=0.50 (0.37, 0.68)). There was a significant interaction between tumor size and DS but not between DS and RT. Among women with a low risk DS and age &gt;= 50, tumor size &lt;= 10 mm, &lt;= 30% comedo necrosis, no multifocality, low or moderate nuclear grade and negative margins, the average predicted 10 year LR risk = 6.8% (range 6.4% - 7.6%) after treatment by BCS without RT, and 3.6% (range 3.4% - 3.8%) after BCS+RT (an absolute benefit of 3.2% from RT). Among women with intermediate/high risk DS and the same low risk clinical-pathological features, the average predicted 10 year LR risk = 19.0% (range 18.3% - 20.0%) without RT, and 9.5% (range 9.1% - 10.3%) with RT (an absolute benefit of 9.5% from RT).
Conclusion: This prediction model combines clinical and pathological features with the DS to improve estimates of local recurrence risk after BCS alone and the absolute benefit with RT, which can improve decision making in DCIS. After calibration and validation, it will be the basis of a web-based nomogram / risk calculator. It also demonstrates the importance of molecular testing for studies of the de-escalation of therapy for DCIS.
Citation Format: Paszat L, Sutradhar R, Zhou L, Lalani N, Nofech-Mozes S, Rakovitch E. Integration of clinical and pathological data with the DCIS score to predict the risk of local recurrence [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-15-01.