A prognostic survival model for women diagnosed with invasive breast cancer in Queensland, Australia.

Baade, PD; Fowler, H; Kou, K; Dunn, J; Chambers, SK; Pyke, C; Aitken, JF

A prognostic survival model for women diagnosed with invasive breast cancer in Queensland, Australia.

Baade, PD Fowler, H Kou, K Dunn, J Chambers, SK Pyke, C Aitken, JF

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Breast Cancer Research and Treatment, 2022, 195, (2), pp. 191-200
Issue Date:: 2022-09

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Field	Value	Language
dc.contributor.author	Baade, PD
dc.contributor.author	Fowler, H
dc.contributor.author	Kou, K
dc.contributor.author	Dunn, J
dc.contributor.author	Chambers, SK
dc.contributor.author	Pyke, C
dc.contributor.author	Aitken, JF
dc.date.accessioned	2022-09-26T03:40:55Z
dc.date.available	2022-07-06
dc.date.available	2022-09-26T03:40:55Z
dc.date.issued	2022-09
dc.identifier.citation	Breast Cancer Research and Treatment, 2022, 195, (2), pp. 191-200
dc.identifier.issn	0167-6806
dc.identifier.issn	1573-7217
dc.identifier.uri	http://hdl.handle.net/10453/162065
dc.description.abstract	PURPOSE: Prognostic models can help inform patients on the future course of their cancer and assist the decision making of clinicians and patients in respect to management and treatment of the cancer. In contrast to previous studies considering survival following treatment, this study aimed to develop a prognostic model to quantify breast cancer-specific survival at the time of diagnosis. METHODS: A large (n = 3323), population-based prospective cohort of women were diagnosed with invasive breast cancer in Queensland, Australia between 2010 and 2013, and followed up to December 2018. Data were collected through a validated semi-structured telephone interview and a self-administered questionnaire, along with data linkage to the Queensland Cancer Register and additional extraction from medical records. Flexible parametric survival models, with multiple imputation to deal with missing data, were used. RESULTS: Key factors identified as being predictive of poorer survival included more advanced stage at diagnosis, higher tumour grade, "triple negative" breast cancers, and being symptom-detected rather than screen detected. The Harrell's C-statistic for the final predictive model was 0.84 (95% CI 0.82, 0.87), while the area under the ROC curve for 5-year mortality was 0.87. The final model explained about 36% of the variation in survival, with stage at diagnosis alone explaining 26% of the variation. CONCLUSIONS: In addition to confirming the prognostic importance of stage, grade and clinical subtype, these results highlighted the independent survival benefit of breast cancers diagnosed through screening, although lead and length time bias should be considered. Understanding what additional factors contribute to the substantial unexplained variation in survival outcomes remains an important objective.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Springer
dc.relation.ispartof	Breast Cancer Research and Treatment
dc.relation.isbasedon	10.1007/s10549-022-06682-5
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1103 Clinical Sciences, 1112 Oncology and Carcinogenesis
dc.subject.classification	Oncology & Carcinogenesis
dc.subject.mesh	Australia
dc.subject.mesh	Breast Neoplasms
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Prognosis
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Queensland
dc.subject.mesh	Australia
dc.subject.mesh	Breast Neoplasms
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Prognosis
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Queensland
dc.subject.mesh	Humans
dc.subject.mesh	Breast Neoplasms
dc.subject.mesh	Prognosis
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Australia
dc.subject.mesh	Queensland
dc.subject.mesh	Female
dc.title	A prognostic survival model for women diagnosed with invasive breast cancer in Queensland, Australia.
dc.type	Journal Article
utslib.citation.volume	195
utslib.location.activity	Netherlands
utslib.for	1103 Clinical Sciences
utslib.for	1112 Oncology and Carcinogenesis
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-26T03:40:50Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	195
utslib.citation.issue	2

Abstract:

PURPOSE: Prognostic models can help inform patients on the future course of their cancer and assist the decision making of clinicians and patients in respect to management and treatment of the cancer. In contrast to previous studies considering survival following treatment, this study aimed to develop a prognostic model to quantify breast cancer-specific survival at the time of diagnosis. METHODS: A large (n = 3323), population-based prospective cohort of women were diagnosed with invasive breast cancer in Queensland, Australia between 2010 and 2013, and followed up to December 2018. Data were collected through a validated semi-structured telephone interview and a self-administered questionnaire, along with data linkage to the Queensland Cancer Register and additional extraction from medical records. Flexible parametric survival models, with multiple imputation to deal with missing data, were used. RESULTS: Key factors identified as being predictive of poorer survival included more advanced stage at diagnosis, higher tumour grade, "triple negative" breast cancers, and being symptom-detected rather than screen detected. The Harrell's C-statistic for the final predictive model was 0.84 (95% CI 0.82, 0.87), while the area under the ROC curve for 5-year mortality was 0.87. The final model explained about 36% of the variation in survival, with stage at diagnosis alone explaining 26% of the variation. CONCLUSIONS: In addition to confirming the prognostic importance of stage, grade and clinical subtype, these results highlighted the independent survival benefit of breast cancers diagnosed through screening, although lead and length time bias should be considered. Understanding what additional factors contribute to the substantial unexplained variation in survival outcomes remains an important objective.

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http://hdl.handle.net/10453/162065