Citation

Rehman, Zaka Ur and Ahmad Fauzi, Mohammad Faizal and Wan Ahmad, Wan Siti Halimatul Munirah and Abas, Fazly Salleh and Cheah, Phaik Leng and Chiew, Seow Fan and Looi, Lai Meng (2024) Computational approach for counting of SISH amplification signals for HER2 status assessment. PeerJ Computer Science, 10. e2373. ISSN 2376-5992

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Abstract

The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver in situ hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions. The Stardist deep learning model is fine-tuned on our HER2-SISH data to identify nuclei regions, followed by postprocessing based on identified HER2 and CEP17 signals. Conventional thresholding techniques are used to segment HER2 and CEP17 signals. HER2 amplification status is determined by calculating the HER2-to-CEP17 signal ratio, in accordance with ASCO/CAP 2018 standards. The proposed method significantly reduces the effort and time required for quantification. Experimental results demonstrate a 0.91% correlation coefficient between pathologists manual enumeration and the proposed automatic SISH quantification approach. A one-sided paired t-test confirmed that the differences between the outcomes of the proposed method and the reference standard are statistically insignificant, with p-values exceeding 0.05. This study illustrates how deep learning can effectively automate HER2 status determination, demonstrating improvements over current manual methods and offering a robust, reproducible alternative for clinical practice.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Digital pathology
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Dec 2024 02:11
Last Modified:	03 Dec 2024 02:11
URII:	http://shdl.mmu.edu.my/id/eprint/13163

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