RESUMEN
Purpose: High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data. Methods: Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not. Results: A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%). Conclusion: Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.
RESUMEN
Completion axillary lymph node dissection (cALND) is the golden standard if breast cancer involves the sentinel lymph node (SLN). However, most non-sentinel lymph nodes (NSLN) are not involved, cALND has a considerable complication rate and does not improve outcome. We here present and validate our predictive model for positive NSLNs in the cALND if the SLN is positive. Consecutive early breast cancer patients from one center undergoing cALND for a positive SLN were included. We assessed demographic and clinicopathological variables for NSLN involvement. Uni- and multivariate analysis was performed. A predictive model was built and validated in two external centers. 21.9% of 470 patients had at least one involved NSLN. In univariate analysis, seven variables were significantly correlated with NSLN involvement: tumor size, grade, lymphovascular invasion (LVI), number of positive and negative SLNs, size of SLN metastasis and intraoperative positive SLN. In multivariate analysis, LVI, number of negative SLNs, size of SLN metastasis and intraoperative positive pathological evaluation were independent predictors for NSLN involvement. The calculated risk resulted in an AUC of 0.76. Applied to the external data, the model was accurate and discriminating for one (AUC = 0.75) and less for the other center (AUC = 0.58). A discriminative predictive model was constructed to calculate the risk of NSLN involvement in case of a positive SLN. External validation of our model reveals differences in performance when applied to data from other institutions concluding that such a predictive model requires validation prior to use.
