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Radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13346))

Abstract

Radiomics is a systematic approach to characterize objects in terms of their radiological appearance. We used radiomic features of 5027 objects of 6 classes and trained a binary classifier with 79% accuracy. Features were obtained by using our novel preprocessing pipeline for object segmentation from the lung tissue in a low-dose Computed Tomography (LDCT) imaging technique. Our results show that radiomic features prove effective in distinguishing between suspicious and benign objects located in the lung tissue. Our data shows that there is vast space for improvement from both model- as well as a data-centric approach to developing Computer-aided detection (CAD) systems based on radiomics for early lung cancer detection. We show our results in the paper.

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Acknowledgment

WP benefits from the European Union scholarship through the European Social Fund (grant POWR.03.05.00-00-Z305) and from OPUS grant no. 2017/27/B/NZ7/01833. JP was financed by 02/070/BK_22/0033 project and by OPUS grant no. 2017/27/B/NZ7/01833. Calculations were carried out using GeCONiI infrastructure funded by NCBiR project no. POIG.02.03.01-24-099/13.

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Authors and Affiliations

  1. Department of Data Science and Engineering, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland

    Wojciech Prazuch & Joanna Polanska

  2. 2nd Department of Radiology, Medical University of Gdansk, ul. Smoluchowskiego 17, 80-001, Gdansk, Poland

    Malgorzata Jelitto-Gorska, Agata Durawa & Katarzyna Dziadziuszko

Authors
  1. Wojciech Prazuch
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  2. Malgorzata Jelitto-Gorska
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  3. Agata Durawa
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  4. Katarzyna Dziadziuszko
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  5. Joanna Polanska
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Corresponding author

Correspondence to Wojciech Prazuch .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Prazuch, W., Jelitto-Gorska, M., Durawa, A., Dziadziuszko, K., Polanska, J. (2022). Radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13346. Springer, Cham. https://doi.org/10.1007/978-3-031-07704-3_29

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  • DOI: https://doi.org/10.1007/978-3-031-07704-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07703-6

  • Online ISBN: 978-3-031-07704-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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