Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data

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 regist...

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Bibliographic Details
Published in:Clinical epidemiology Vol. 15; pp. 559 - 568
Main Authors: Izci, Hava, Macq, Gilles, Tambuyzer, Tim, De Schutter, Harlinde, Wildiers, Hans, Duhoux, Francois P, de Azambuja, Evandro, Taylor, Donatienne, Staelens, Gracienne, Orye, Guy, Hlavata, Zuzana, Hellemans, Helga, De Rop, Carine, Neven, Patrick, Verdoodt, Freija
Format: Journal Article
Language:English
Published: New Zealand Dove Medical Press Limited 01-01-2023
Dove
Dove Medical Press
Subjects:
administrative data
algorithm
Algorithms
Breast cancer
Cancer
Data mining
Development and progression
Diagnosis
distant metastases
Machine learning
Metastasis
Original Research
recurrences
Relapse
Belgium
recurrences
breast cancer
distant metastases
administrative data
machine learning
algorithm
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Summary: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. 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. 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%). 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.
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ISSN:1179-1349
1179-1349
DOI:10.2147/CLEP.S400071