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

Yücel Z., AKAL F., Oltulu P.

Medical and Biological Engineering and Computing, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s11517-024-03045-8
  • Dergi Adı: Medical and Biological Engineering and Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CINAHL, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: Deep learning, Histopathological images, Ki-67, Neuroendocrine tumor, Proliferation index
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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. Graphical abstract: (Figure presented.)