Literature DB >> 24344212

Predicting and communicating the risk of recurrence and death in women with early-stage breast cancer: a systematic review of risk prediction models.

Ellen G Engelhardt1, Mirjam M Garvelink, J Hanneke C J M de Haes, Jacobus J M van der Hoeven, Ellen M A Smets, Arwen H Pieterse, Anne M Stiggelbout.   

Abstract

BACKGROUND: It is a challenge for oncologists to distinguish patients with breast cancer who can forego adjuvant systemic treatment without negatively affecting survival from those who cannot. Risk prediction models (RPMs) have been developed for this purpose. Oncologists seem to have embraced RPMs (particularly Adjuvant!) in clinical practice and often use them to communicate prognosis to patients. We performed a systematic review of published RPMs and provide an overview of the prognosticators incorporated and reported clinical validity. Subsequently, we selected the RPMs that are currently used in the clinic for a more in-depth assessment of clinical validity. Finally, we assessed lay comprehensibility of the reports generated by RPMs.
METHODS: Pubmed, EMBASE, and Web of Science were searched. Two reviewers independently selected relevant articles and extracted data. Agreement on article selection and data extraction was achieved in consensus meetings.
RESULTS: We identified RPMs based on clinical prognosticators (N = 6) and biomolecular features (N = 14). Generally predictions from RPMs seem to be accurate, except for patients ≤ 50 years or ≥ 75 years at diagnosis, in addition to Asian populations. RPM reports contain much medical jargon or technical details, which are seldom explained in lay terms.
CONCLUSION: The accuracy of RPMs' prognostic estimates is suboptimal in some patient subgroups. This urgently needs to be addressed. In their current format, RPM reports are not conducive to patient comprehension. Communicating survival probabilities using RPM might seem straightforward, but it is fraught with difficulties. If not done properly, it can backfire and confuse patients. Evidence to guide best communication practice is needed.

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Year:  2013        PMID: 24344212     DOI: 10.1200/JCO.2013.50.3417

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  35 in total

1.  G15 sensitizes epithelial breast cancer cells to doxorubicin by preventing epithelial-mesenchymal transition through inhibition of GPR30.

Authors:  Yu Liu; Fei-Ya Du; Wei Chen; Pei-Fen Fu; Min-Ya Yao; Shu-Sen Zheng
Journal:  Am J Transl Res       Date:  2015-05-15       Impact factor: 4.060

2.  Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women.

Authors:  Yuan Wang; Ying Gao; Munkhzul Battsend; Kexin Chen; Wenli Lu; Yaogang Wang
Journal:  Tumour Biol       Date:  2014-08-02

3.  Curcumin inhibits LPA-induced invasion by attenuating RhoA/ROCK/MMPs pathway in MCF7 breast cancer cells.

Authors:  Kai Sun; Xiaoyi Duan; Hui Cai; Xiaohong Liu; Ya Yang; Min Li; Xiaoyun Zhang; Jiansheng Wang
Journal:  Clin Exp Med       Date:  2015-01-18       Impact factor: 3.984

Review 4.  Managing psychosocial issues faced by young women with breast cancer at the time of diagnosis and during active treatment.

Authors:  Sara Fernandes-Taylor; Taiwo Adesoye; Joan R Bloom
Journal:  Curr Opin Support Palliat Care       Date:  2015-09       Impact factor: 2.302

5.  Relapse profile of early breast cancer according to immunohistochemical subtypes: guidance for patient's follow up?

Authors:  Nesrine Mejri; H Boussen; S Labidi; F Benna; M Afrit; K Rahal
Journal:  Ther Adv Med Oncol       Date:  2015-05       Impact factor: 8.168

6.  The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

Authors:  Hoong-Seam Wong; Shridevi Subramaniam; Zarifah Alias; Nur Aishah Taib; Gwo-Fuang Ho; Char-Hong Ng; Cheng-Har Yip; Helena M Verkooijen; Mikael Hartman; Nirmala Bhoo-Pathy
Journal:  Medicine (Baltimore)       Date:  2015-02       Impact factor: 1.889

7.  Are we able to predict survival in ER-positive HER2-negative breast cancer? A comparison of web-based models.

Authors:  E Laas; P Mallon; M Delomenie; V Gardeux; J-Y Pierga; P Cottu; F Lerebours; D Stevens; R Rouzier; F Reyal
Journal:  Br J Cancer       Date:  2015-01-15       Impact factor: 7.640

8.  An evaluation of the prognostic model PREDICT using the POSH cohort of women aged ⩽40 years at breast cancer diagnosis.

Authors:  T Maishman; E Copson; L Stanton; S Gerty; E Dicks; L Durcan; G C Wishart; P Pharoah; D Eccles
Journal:  Br J Cancer       Date:  2015-03-17       Impact factor: 7.640

9.  Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath.

Authors:  Solon Karapanagiotis; Paul D P Pharoah; Christopher H Jackson; Paul J Newcombe
Journal:  Clin Cancer Res       Date:  2018-02-14       Impact factor: 12.531

10.  Breast cancer specialists' views on and use of risk prediction models in clinical practice: a mixed methods approach.

Authors:  Ellen G Engelhardt; Arwen H Pieterse; Nanny van Duijn-Bakker; Judith R Kroep; Hanneke C J M de Haes; Ellen M A Smets; Anne M Stiggelbout
Journal:  Acta Oncol       Date:  2014-10-13       Impact factor: 4.089

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