An estimation model for Oncotype DX recurrence score using routine histopathologic variables.

Umbricht, Christopher B.; Illei, Peter B.; Magalhães, Maria Cristina Figueroa; Pesce, Catherine; Gage, Michele M.; Mylander, Charles; Rosman, Martin; Tafra, Lorraine; Visvanathan, Kala; Cope, Leslie; Wolff, Antonio C.

doi:10.1200/jco.2014.32.15_suppl.559

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“…Others have attempted to develop an accurate model for OncotypeDX using routine histopathologic variables [10, 11, 13-15]. The Magee equations use Nottingham score, ER H-score, PR H-score, tumor size, and Ki-67 percentage to estimate OncotypeDX score and are available for free online [14].…”

Section: Discussionmentioning

confidence: 99%

Estimating the OncotypeDX score: validation of an inexpensive estimation tool

Eaton

Pesce

Murphy

et al. 2016

Breast Cancer Res Treat

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Background OncotypeDX, a multi-gene expression assay, has been incorporated into clinical practice as a prognostic and predictive tool. However, its use in resource-constrained international healthcare systems is limited. Here we develop and validate a simplified model using clinicopathologic criteria to predict OncotypeDX score. Methods Patients with estrogen receptor (ER) and/or progesterone receptor (PR) positive and HER2 negative invasive ductal carcinoma for whom the OncotypeDX test was successfully performed between 09/2008–12/2011 were retrospectively identified. Tumor size, nuclear and histologic grade, lymphovascular invasion, and ER and PR status were extracted from pathology reports. Data were split into a training dataset comprising women tested 09/2008–04/2011, and a validation dataset comprising women tested 04/2011–12/2011. Using the training dataset, linear regression analysis was used to identify factors associated with OncotypeDX score, and to create a simplified risk score and identify risk cutoffs. Results Estrogen and progesterone receptors, tumor size, nuclear and histologic grades, and lymphovascular involvement were independently associated with OncotypeDX. The full model explained 39% of the variation in the test data, and the simplified risk score and cutoffs assigned 57% of patients in the test data to the correct risk category (OncotypeDX score <18, 18–30, >30). 41% of patients were predicted to have OncotypeDX score<18; of these, 83%, 16%, and 2% had true scores of <18, 18–30, and >30, respectively. Conclusions Awaiting an inexpensive test that is prognostic and predictive, our simplified tool allows clinicians to identify a fairly large group of patients (41%) with very low chance of having high-risk disease (2%).

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