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- Valachis, Antonis, 1984-, et al.
(författare)
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Overall survival of patients with metastatic breast cancer in Sweden : a nationwide study
- 2022
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Ingår i: British Journal of Cancer. - : Nature Publishing Group. - 0007-0920 .- 1532-1827. ; 127:4, s. 720-725
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Tidskriftsartikel (refereegranskat)abstract
- Background: Breast cancer is the most common cancer among women in Sweden. Whereas survival for the overall breast cancer population is well-documented, survival of patients with metastatic breast cancer (MBC) is harder to quantify due to the lack of reliable data on disease recurrence in national cancer registers.Methods: This study used machine learning to classify the total MBC population in Sweden diagnosed between 2009 and 2016 using national registers, with the aim to estimate overall survival (OS).Results: The total population consisted of 13,832 patients-2528 (18.3%) had de novo MBC whereas 11,304 (81.7%) were classed as having a recurrent MBC. Median OS for patients with MBC was found to be 29.8 months 95% confidence interval (CI) [28.9, 30.6]. Hormone-receptor (HR)-positive MBC had a median OS of 37.0 months 95% CI [35.9, 38.3] compared to 9.9 months 95% CI [9.1, 11.0] for patients with HR-negative MBC.Conclusion: This study covered the entire MBC population in Sweden during the study time and may serve as a baseline for assessing the effect of new treatment strategies in MBC introduced after the study period.
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| 2. |
- Valachis, Antonis, 1984-, et al.
(författare)
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Use of classifiers to optimise the identification and characterisation of metastatic breast cancer in a nationwide administrative registry
- 2021
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Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 60:12, s. 1604-1610
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Tidskriftsartikel (refereegranskat)abstract
- Bakground: The prognosis for patients with metastatic breast cancer (MBC) is substantially worse when compared with patients with earlier stage disease. Therefore, understanding the differences in epidemiology between these two patient groups is important. Studies using population-based cancer registries to identify MBC are hampered by the quality of reporting. Patients are registered once (at time of initial diagnosis); hence only data for patients with de novo MBC are identifiable, whereas data for patients with recurrent MBC are not. This makes accurate estimation of the epidemiology and healthcare utilisation of MBC challenging. This study aimed to investigate whether machine-learning could improve identification of MBC in national health registries.Material and methods: Data for patients with confirmed MBC from a regional breast cancer registry were used to train machine-learning algorithms (or 'classifiers'). The best performing classifier (accuracy 97.3%, positive predictive value 85.1%) was applied to Swedish national registries for 2008 to 2016.Results: Mean yearly MBC incidence was estimated at 14 per 100,000 person-years (with 18% diagnosed de novo and 76% of the total with HR-positive MBC).Conclusion: To our knowledge, this is the first study to use machine learning to identify MBC regardless of stage at diagnosis in health registries covering the entire population of Sweden.
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