Rehman Zaka Ur, Ahmad Fauzi Mohammad Faizal, Wan Ahmad Wan Siti Halimatul Munirah, Abas Fazly Salleh, Cheah Phaik Leng, Chiew Seow Fan, Looi Lai-Meng
Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.
Institute for Research, Development and Innovation (IRDI), IMU University, Bukit Jalil, Kuala Lumpur 57000, Malaysia.
Diagnostics (Basel). 2024 Sep 21;14(18):2089. doi: 10.3390/diagnostics14182089.
Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques.
医学成像的最新进展极大地推动了计算技术在数字病理学中的应用，特别是在利用原位杂交（ISH）成像对乳腺癌进行分类方面。HER2扩增是20%-25%乳腺癌中的关键预后标志物，可通过基因拷贝数或蛋白质表达的改变来评估。然而，由于核区域的异质性和癌症生物标志物检测的复杂性，挑战依然存在。本综述探讨了用于分析ISH图像的半自动和全自动计算方法，重点是基因扩增。分析了1997年至2023年的文献，强调了银增强原位杂交（SISH）及其与图像处理和机器学习技术的整合。比较了传统机器学习方法和深度学习的最新进展。该综述表明，结合明场显微镜的自动化ISH分析为常规病理学提供了一种经济高效且可扩展的解决方案。深度学习技术的整合显示出比传统方法提高准确性的潜力，尽管存在与数据可变性和计算需求相关的局限性。自动化ISH分析可以减少人工劳动并提高诊断准确性。未来的研究应专注于改进这些计算方法，特别是在处理HER2状态评估的复杂性质方面，并整合最佳实践以进一步促进这些技术在临床上的应用。
