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Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training.

  • Author(s): Bartlett, John MS
  • Bayani, Jane
  • Kornaga, Elizabeth N
  • Danaher, Patrick
  • Crozier, Cheryl
  • Piper, Tammy
  • Yao, Cindy Q
  • Dunn, Janet A
  • Boutros, Paul C
  • Stein, Robert C
  • OPTIMA Trial Management Group
  • et al.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470374/
No data is associated with this publication.
Abstract

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX® (Genomic Health.), Prosigna™ (NanoString Technologies, Inc.), MammaPrint® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational "modelling" of test results using published algorithms and secondly a "training" approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a "modelling" approach without reference to real world test results. Simultaneously we show that a "training" approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results.

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