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Classification of Kidney Tumor Grading on Preoperative Computed Tomography Scans

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Pervasive Computing Technologies for Healthcare (PH 2022)

Abstract

Deep learning (DL) has proven itself as a powerful tool to capture patterns that human eyes may not be able to perceive when looking at high-dimensional data such as radiological data (volumetric data). For example, the classification or grading of kidney tumors in computed tomography (CT) volumes based on distinguishable patterns is a challenging task. Kidney tumor classification or grading is clinically useful information for patient management and better informing treatment decisions. In this paper, we propose a novel DL-based framework to automate the classification of kidney tumors based on the International Society of Urological Pathology (ISUP) renal tumor grading system in CT volumes. The framework comprises several pre-processing techniques and a three-dimensional (3D) DL-based classifier model. The classifier model is forced to pay particular attention to the tumor regions in the CT volumes so that it can better interpret the surface patterns of the tumor regions to attain performance improvement. The proposed framework achieves the following results on a public dataset of CT volumes of kidney cancer: sensitivity 85%, precision 84%. Code used in this publication is freely available at: https://github.com/Balasingham-AI-Group/Classification-Kidney-Tumor-ISUP-Grade.

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Notes

  1. 1.

    https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=61081171.

  2. 2.

    http://6.869.csail.mit.edu/fa17/miniplaces.html.

  3. 3.

    http://www.itksnap.org/pmwiki/pmwiki.php.

  4. 4.

    Area under the curve is a performance measurement for the classification problems. It tells how much the model is capable of distinguishing between classes.

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Acknowledgement

This research was funded by the research council Norway and ICT:5G-HEART. We thank our colleagues Davit Aghayan and Egidijus Pelanis from Intervention Center, Oslo University Hospital, who provided insight and expertise in medical images and clinical aspects that greatly assisted the research. We gratitude Piotr Bialecki, Senior Engineering Manager of the PyTorch Team at NVIDIA, for assistance with technical parts of the training model with deep neural networks; and Håvard Kvamme, previous Ph.D. student of the University of Oslo in the faculty of Mathematics, for his comments and showing the actual path of the research.

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Authors and Affiliations

  1. The Intervention Centre, Oslo University Hospital, 0372, Oslo, Norway

    Maryamalsadat Mahootiha, Hemin Ali Qadir, Jacob Bergsland & Ilangko Balasingham

  2. Faculty of Medicine, University of Oslo, 0372, Oslo, Norway

    Maryamalsadat Mahootiha

  3. Department of Electronic Systems, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Ilangko Balasingham

Authors
  1. Maryamalsadat Mahootiha
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  2. Hemin Ali Qadir
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  3. Jacob Bergsland
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  4. Ilangko Balasingham
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Correspondence to Maryamalsadat Mahootiha .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  2. Centre for Research and Technology Hellas, Thessaloniki, Greece

    Andreas Triantafyllidis

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Mahootiha, M., Qadir, H.A., Bergsland, J., Balasingham, I. (2023). Classification of Kidney Tumor Grading on Preoperative Computed Tomography Scans. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-34586-9_6

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  • Publisher Name: Springer, Cham

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