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Machine Learning Can Reliably Distinguish Histological Patterns of Micropapillary and Solid Lung Adenocarcinomas

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Book cover Information Technologies in Medicine (ITiB 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 472))

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Abstract

The World Health Organization recommends subclassification of lung cancer according to the percentages of histologic subtypes within a tumor. The manual quantification of lung tumor composition is very time consuming, but it can potentially be aided by a machine learning application. We have updated our previously developed methodology to segment and distinguish solid and micropapillary lung tumor subtypes. Binary tumor masks delineated by machine learning were defined by the mean area of binary objects and by the number of objects found in an image frame. These two features distinguished solid (\(n=31\)) and micropapillary (\(n=61\)) histologic subtypes with excellent performance (\(p<4.04{\textsc {e}}{\text {-}}19\)) for three different frame sizes. Our method to quantify tumor growth patterns applied to histological images of lung adenocarcinoma, demonstrates for the first time that it is feasible to quantify the composition of histological subtypes in individual lung cancers.

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Authors and Affiliations

  1. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Nathan Ing, Zhaoxuan Ma & Beatrice Knudsen

  2. Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Ann Walts, Beatrice Knudsen & Arkadiusz Gertych

  3. Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Nathan Ing, Sadri Salman & Arkadiusz Gertych

Authors
  1. Nathan Ing
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  2. Sadri Salman
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  3. Zhaoxuan Ma
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  4. Ann Walts
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  5. Beatrice Knudsen
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  6. Arkadiusz Gertych
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Corresponding author

Correspondence to Arkadiusz Gertych .

Editor information

Editors and Affiliations

  1. Fac of Biomed Engg, ul. Roosevelta 40, Silesian University of Technology, Gliwice, Poland

    Ewa Piętka

  2. Fac of Biomed Engg, ul. Roosevelta 40, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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© 2016 Springer International Publishing Switzerland

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Ing, N., Salman, S., Ma, Z., Walts, A., Knudsen, B., Gertych, A. (2016). Machine Learning Can Reliably Distinguish Histological Patterns of Micropapillary and Solid Lung Adenocarcinomas. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technologies in Medicine. ITiB 2016. Advances in Intelligent Systems and Computing, vol 472. Springer, Cham. https://doi.org/10.1007/978-3-319-39904-1_17

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  • DOI: https://doi.org/10.1007/978-3-319-39904-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39903-4

  • Online ISBN: 978-3-319-39904-1

  • eBook Packages: EngineeringEngineering (R0)

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