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Automated Molecular Subtyping of Breast Cancer Through Immunohistochemistry Image Analysis

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

Molecular subtyping has a significant role in cancer prognosis and targeted therapy. However, the prevalent manual procedure for this has disadvantages, such as deficit of medical experts, inter-observer variability, and high time consumption. This paper suggests a novel approach to automate molecular subtyping of breast cancer using an end-to-end deep learning model. Immunohistochemistry (IHC) images of the tumor tissues are analyzed using a three-stage system to determine the subtype. A modified Res-UNet CNN architecture is used in the first stage to segregate the biomarker responses. This is followed by using a CNN classifier to determine the status of the four biomarkers. Finally, the biomarker statuses are combined to determine the specific subtype of breast cancer. For each IHC biomarker, the performance of segmentation models is analyzed qualitatively and quantitatively. In addition, the patient-level biomarker prediction results are also assessed. The findings of the suggested technique demonstrate the potential of computer-aided techniques to diagnose the subtypes of breast cancer. The proposed automated molecular subtyping approach can accelerate pathology procedures, considerably reduce pathologists’ workload, and minimize the overall cost and time required for diagnosis and treatment planning.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India

    S. Niyas, Shraddha Priya, Reena Oswal, Tojo Mathew & Jeny Rajan

  2. Department of Computer Science and Engineering, The National Institute of Engineering, Mysuru, India

    Tojo Mathew

  3. Department of Pathology, Kasturba Medical College, Mangalore, India

    Jyoti R. Kini

  4. Manipal Academy of Higher Education, Manipal, Karnataka, India

    Jyoti R. Kini

Authors
  1. S. Niyas
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  2. Shraddha Priya
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  3. Reena Oswal
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  4. Tojo Mathew
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  5. Jyoti R. Kini
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  6. Jeny Rajan
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Corresponding author

Correspondence to S. Niyas .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Niyas, S., Priya, S., Oswal, R., Mathew, T., Kini, J.R., Rajan, J. (2023). Automated Molecular Subtyping of Breast Cancer Through Immunohistochemistry Image Analysis. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_3

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