Literature DB >> 25862439

Dynamic prediction in breast cancer: proving feasibility in clinical practice using the TEAM trial.

D B Y Fontein1, M Klinten Grand2, J W R Nortier3, C Seynaeve4, E Meershoek-Klein Kranenbarg1, L Y Dirix5, C J H van de Velde1, H Putter6.   

Abstract

BACKGROUND: Predictive models are an integral part of current clinical practice and help determine optimal treatment strategies for individual patients. A drawback is that covariates are assumed to have constant effects on overall survival (OS), when in fact, these effects may change during follow-up (FU). Furthermore, breast cancer (BC) patients may experience events that alter their prognosis from that time onwards. We investigated the 'dynamic' effects of different covariates on OS and developed a nomogram to calculate 5-year dynamic OS (DOS) probability at different prediction timepoints (tP) during FU.
METHODS: Dutch and Belgian postmenopausal, endocrine-sensitive, early BC patients enrolled in the TEAM trial were included. We assessed time-varying effects of specific covariates and obtained 5-year DOS predictions using a proportional baselines landmark supermodel. Covariates included age, histological grade, hormone receptor and HER2 status, T- and N-stage, locoregional recurrence (LRR), distant recurrence, and treatment compliance. A nomogram was designed to calculate 5-year DOS based on individual characteristics.
RESULTS: A total of 2602 patients were included (mean FU 6.2 years). N-stage, LRR, and HER2 status demonstrated time-varying effects on 5-year DOS. Hazard ratio (HR) functions for LRR, high-risk N-stage (N2/3), and HER2 positivity were HR = (8.427 × 0.583[Formula: see text], HR = (3.621 × 0.816[Formula: see text], and HR = (1.235 × 0.851[Formula: see text], respectively. Treatment discontinuation was associated with a higher mortality risk, but without a time-varying effect [HR 1.263 (0.867-1.841)]. All other covariates were time-constant. DISCUSSION: The current nomogram accounts for elapsed time since starting adjuvant endocrine treatment and optimizes prediction of individual 5-year DOS during FU for postmenopausal, endocrine-sensitive BC patients. The nomogram can facilitate in determining whether further therapy will benefit an individual patient, although validation in an independent dataset is still needed.
© The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  breast cancer; dynamic prediction; landmark analysis; personalized therapy; survival probability

Mesh:

Substances:

Year:  2015        PMID: 25862439     DOI: 10.1093/annonc/mdv146

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  8 in total

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2.  Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data.

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Journal:  Haematologica       Date:  2019-02-28       Impact factor: 9.941

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5.  A comparison of the beta-geometric model with landmarking for dynamic prediction of time to pregnancy.

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Journal:  Ann Transl Med       Date:  2021-10

7.  Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study.

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Journal:  BMJ Open       Date:  2020-07-19       Impact factor: 2.692

8.  Development and external validation of a dynamic prognostic nomogram for primary extremity soft tissue sarcoma survivors.

Authors:  Dario Callegaro; Rosalba Miceli; Sylvie Bonvalot; Peter C Ferguson; Dirk C Strauss; Veroniek V M van Praag; Antonin Levy; Anthony M Griffin; Andrew J Hayes; Silvia Stacchiotti; Cecile Le Pèchoux; Myles J Smith; Marco Fiore; Angelo Paolo Dei Tos; Henry G Smith; Charles Catton; Joanna Szkandera; Andreas Leithner; Michiel A J van de Sande; Paolo G Casali; Jay S Wunder; Alessandro Gronchi
Journal:  EClinicalMedicine       Date:  2019-11-22
  8 in total

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