Sunday, October 6, 2019

Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information. - PubMed - NCBI

Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information. - PubMed - NCBI



 2019 Aug 22. pii: S1526-8209(19)30650-0. doi: 10.1016/j.clbc.2019.07.006. [Epub ahead of print]

Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information.

Author information


1
Eastern Ontario Regional Laboratory, Department of Pathology and Laboratory Medicine, The Ottawa Hospital and the University of Ottawa, Ottawa, Ontario, Canada.
2
Department of Oncology, McMaster University, Hamilton, Ontario, Canada.
3
Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Division of Medical Oncology, Department of Medicine, The Ottawa Hospital Cancer Centre and the University of Ottawa, Ottawa, Ontario, Canada.
4
Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
5
Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Division of Surgical Oncology, Department of Surgery, The Ottawa Hospital and the University of Ottawa, Ottawa, Ontario, Canada.
6
Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Division of Medical Oncology, Department of Medicine, The Ottawa Hospital Cancer Centre and the University of Ottawa, Ottawa, Ontario, Canada; Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. Electronic address: mclemons@toh.ca.

Abstract

INTRODUCTION:

Indiscriminate ordering of Oncotype DX (ODX) is expensive and of poor value to patients, physicians, and health care providers. The 3 Magee equations, Gage Algorithm, and University of Tennessee predictive algorithm all use standard clinicopathologic data to provide surrogate ODX scores. In this hypothesis-generating study, we evaluated whether these prognostic scores could be used to identify patients unlikely to benefit from additional ODX testing.

PATIENTS AND METHODS:

Retrospective data was collected from 302 patients with invasive ductal breast cancer and available ODX scores. Additional data was available for: Magee equations 1 (212 patients), 2 (299 patients), 3 (212 patients), Gage Algorithm (299 patients), and University of Tennessee predictive algorithm (286 patients). ODX scores were banded according to the TAILORx results.

RESULTS:

Correlation with ODX scores was between 0.7 and 0.8 (Gage), 0.8 and 0.9 (Magee 2, University of Tennessee predictive algorithm), and > 0.9 (Magee 1 and 3). Magee 3 was the most robust and is proposed as a screening tool: for patients aged ≤ 50 years, ODX testing would be not required if the Magee 3 score was < 14 or ≥ 20; for those aged > 50 years, ODX would not be required if the Magee 3 score was < 18 or ≥ 26. Using these cut-offs, 110 (51.9%) of 212 patients would be deemed as not requiring ODX testing, and 109 (99.1%) of110 patients would be appropriately managed.

CONCLUSIONS:

Use of all formulae, and the Magee 3 equation in particular, are proposed as possible screening tools for ODX testing, resulting in significantly reduced frequency of ODX testing. This requires validation in other populations.

KEYWORDS:

Adjuvant; Algorithms; Breast cancer; Pathology; Recurrence score

PMID:
 
31551182
 
DOI:
 
10.1016/j.clbc.2019.07.006

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