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Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis

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Abstract

Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathologists in grading and diagnosing these tumors based on histopathological images. The appropriate treatment plan for the patient is determined based on the tumor grade. An artificial intelligence-based method is proposed to aid pathologists in the automated calculation and grading of the Ki-67 proliferation index. The proposed system first performs preprocessing to enhance image quality. Then, segmentation process is performed using the U-Net architecture, which is a deep learning algorithm, to separate the nuclei from the background. The identified nuclei are then evaluated as Ki-67 positive or negative based on basic color space information and other features. The Ki-67 proliferation index is then calculated, and the neuroendocrine tumor is graded accordingly. The proposed system’s performance was evaluated on a dataset obtained from the Department of Pathology at Meram Faculty of Medicine Hospital, Necmettin Erbakan University. The results of the pathologist and the proposed system were compared, and the proposed system was found to have an accuracy of 95% in tumor grading when compared to the pathologist’s report.

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Acknowledgements

This study was conducted as a result of Zehra Yücel’s doctoral research. The authors would like to express their gratitude to the Pathology Department of Necmettin Erbakan University Meram Faculty of Medicine Hospital for their kind cooperation and providing all the histopathological images used in this study.

Funding

This study was not supported by a foundation.

Author information

Authors and Affiliations

  1. Necmettin Erbakan University, Department of Computer Technologies, Konya, Turkey

    Zehra Yücel

  2. Hacettepe University, Graduate School of Science and Engineering, Ankara, Turkey

    Zehra Yücel

  3. Hacettepe University, Faculty of Engineering, Department of Computer Engineering, Ankara, Turkey

    Fuat Akal

  4. Necmettin Erbakan University, Faculty of Medicine, Department of Pathology, Konya, Turkey

    Pembe Oltulu

Authors
  1. Zehra Yücel
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  2. Fuat Akal
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  3. Pembe Oltulu
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Corresponding author

Correspondence to Zehra Yücel.

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The authors declare that he has no conflict of interest.

Ethical approval

This study was in accordance with the ethical standards of the institutional and/or national research committee (The Ethics Approval Certificate of Hacettepe University Non-Interventional Clinical Research Ethics Commission numbered 16969557–1579) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Yücel, Z., Akal, F. & Oltulu, P. Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis. Med Biol Eng Comput 62, 1899–1909 (2024). https://doi.org/10.1007/s11517-024-03045-8

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  • DOI: https://doi.org/10.1007/s11517-024-03045-8

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