Literature DB >> 33609208

Refining breast cancer prognosis by incorporating age at diagnosis into clinical prognostic staging: introduction of a novel online calculator.

Helen M Johnson1, William Irish1, Nasreen A Vohra1, Jan H Wong2.   

Abstract

PURPOSE: American Joint Committee on Cancer (AJCC) clinical staging is used to estimate breast cancer prognosis, but individual patient survival within each stage varies considerably by age at diagnosis. We hypothesized that the addition of age at diagnosis to the staging schema will enable more refined risk stratification.
METHODS: We performed a retrospective population analysis of adult women diagnosed with invasive breast cancer between 2010 and 2015 registered in SEER. Multivariable Cox hazards models were used to evaluate the association of AJCC 8th edition clinical prognostic stage (CPS) and age with risk of overall mortality. Separate hierarchical models were fit to the data: Model 1: CPS alone; Model 2: CPS + age + age2; and Model 3: CPS + age + age2 + CPS x age + CPS x age2. Models were compared by the Akaike information criterion (AIC), the c-statistic for time-dependent receiver operator characteristic curves, and category-free net reclassification improvement (NRI). Internal validation was performed using bootstrapping samples.
RESULTS: Among 86,637 women, the median follow-up was 36 months and 3-year overall survival was 91.9% ± 0.1%. Age significantly modified the effect of CPS on survival (p < 0.0001). Model 3 was the most precise, with the lowest AIC (126,619.63), the highest c-statistic (0.8212, standard error 0.0187), and superior NRI indices.
CONCLUSION: Age at diagnosis is a highly prognostic variable that warrants consideration for inclusion in future editions of the AJCC Breast Cancer Staging Manual.

Entities:  

Keywords:  Age; Breast cancer; Prognosis; Stage; Survival

Year:  2021        PMID: 33609208     DOI: 10.1007/s10549-021-06113-x

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  30 in total

1.  Age effects on survival from early breast cancer in clinical settings in Australia.

Authors:  David M Roder; Primali de Silva; Helen M Zorbas; James Kollias; Peter L Malycha; Chris M Pyke; Ian D Campbell
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2.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

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Review 3.  Breast cancer in elderly women and altered clinico-pathological characteristics: a systematic review.

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Journal:  Breast Cancer Res Treat       Date:  2017-08-12       Impact factor: 4.872

4.  Quantifying the relationship between age at diagnosis and breast cancer-specific mortality.

Authors:  Helen M Johnson; William Irish; Mahvish Muzaffar; Nasreen A Vohra; Jan H Wong
Journal:  Breast Cancer Res Treat       Date:  2019-07-11       Impact factor: 4.872

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Journal:  Breast Cancer Res Treat       Date:  2017-11-15       Impact factor: 4.872

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Review 8.  Incorporating Biologic Factors into the American Joint Committee on Cancer Breast Cancer Staging System: Review of the Supporting Evidence.

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Authors:  Anna Weiss; Mariana Chavez-MacGregor; Daphne Y Lichtensztajn; Min Yi; Audree Tadros; Gabriel N Hortobagyi; Sharon H Giordano; Kelly K Hunt; Elizabeth A Mittendorf
Journal:  JAMA Oncol       Date:  2018-02-01       Impact factor: 31.777

10.  Prognostic Validation of the American Joint Committee on Cancer 8th Staging System in 24,014 Korean Patients with Breast Cancer.

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