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Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer

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Abstract

Oncotype DX (ODX) is a multi-gene expression signature designed for estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients to predict the recurrence score (RS) and chemotherapy (CT) benefit. The aim of our study is to develop a prediction tool for the three RS’s categories based on deep multi-layer perceptrons (DMLP) and using only the morphoimmunohistological variables. We performed a retrospective cohort of 320 patients who underwent ODX testing from three French hospitals. Clinico-pathological characteristics were recorded. We built a supervised machine learning classification model using Matlab software with 152 cases for the training and 168 cases for the testing. Three classifiers were used to learn the three risk categories of the ODX, namely the low, intermediate, and high risk. Experimental results provide the area under the curve (AUC), respectively, for the three risk categories: 0.63 [95% confidence interval: (0.5446, 0.7154), p < 0.001], 0.59 [95% confidence interval: (0.5031, 0.6769), p < 0.001], 0.75 [95% confidence interval: (0.6184, 0.8816), p < 0.001]. Concordance rate between actual RS and predicted RS ranged from 53 to 56% for each class between DMLP and ODX. The concordance rate of low and intermediate combined risk group was 85%.

We developed a predictive machine learning model that could help to define patient’s RS. Moreover, we integrated histopathological data and DMLP results to select tumor for ODX testing. Thus, this process allows more relevant use of histopathological data, and optimizes and enhances this information.

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Funding

The authors received no specific funding for this work.

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Authors and Affiliations

  1. Department of Pathology, Nord Franche Comte Hospital, 100 route de Moval, CS 10499 Trevenans, 90015, Belfort, France

    Aline Baltres & Christine Devalland

  2. FEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, ENSMM, Besançon, France

    Zeina Al Masry & Noureddine Zerhouni

  3. CEDRIC Laboratory of the Conservatoire National Des Arts Et Métiers (CNAM), HESAM Université, Paris, France

    Ryad Zemouri

  4. Department of Pathology, University hospital, Grenoble, France

    Severine Valmary-Degano

  5. Department of Pathology, Centre Georges-François Leclerc, Dijon, France

    Laurent Arnould

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  1. Aline Baltres
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  6. Noureddine Zerhouni
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Contributions

AB, ZA, and CD contributed to the study design, data analysis, and drafting of the manuscript. AB collected the clinical, epidemiological and histological data, interpreted the results, and wrote the manuscript. LA contributed to the creation of the database. RZ conducted the model DMLP. ZA conducted the statistical analysis. AB, ZA, NZ and CD contributed to the study design and data analysis. All authors reviewed the manuscript and approved the final version.

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Correspondence to Christine Devalland.

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Baltres, A., Al Masry, Z., Zemouri, R. et al. Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer. Breast Cancer 27, 1007–1016 (2020). https://doi.org/10.1007/s12282-020-01100-4

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  • DOI: https://doi.org/10.1007/s12282-020-01100-4

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