Wang Ching-Wei, Chu Kai-Lin, Muzakky Hikam, Lin Yi-Jia, Chao Tai-Kuang
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Department of Pathology, Tri-Service General Hospital, Taipei 11490, Taiwan.
Cancers (Basel). 2023 Aug 6;15(15):3991. doi: 10.3390/cancers15153991.
Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.
乳腺癌是全球女性癌症相关死亡的主要原因，早期检测和治疗已被证明可显著降低重症死亡率。此外，通过荧光原位杂交（FISH）和双重原位杂交（DISH）检测人类表皮生长因子受体2（HER2）基因扩增，对于选择合适的乳腺癌患者进行HER2靶向治疗至关重要。然而，由于病理学家和细胞病理学家之间观察者间差异较大，显微镜检查的目视观察耗时、主观且重复性差。在识别癌样细胞核方面缺乏一致性，导致敏感性和特异性计算存在差异。本文介绍了一种计算成本低的高效深度学习方法。实验结果表明，所提出的框架在三个重要临床应用中实现了高精度和召回率，包括乳腺癌诊断以及在FISH和DISH载玻片上进行人类表皮受体因子2（HER2）扩增检测以用于HER2靶向治疗。此外，在所提出的方法在三个重要临床应用的交并比（IoU）方面显著优于大多数基准方法（p<0.001）。重要的是，运行时间分析表明，所提出的方法在人工智能（AI）训练时间（减少16.93%）、AI推理时间（减少17.25%）和内存使用（减少18.52%）方面显著减少的情况下，仍能获得出色的分割结果，使得所提出的框架在实际临床应用中可行。
