Faa Gavino, Coghe Ferdinando, Pretta Andrea, Castagnola Massimo, Van Eyken Peter, Saba Luca, Scartozzi Mario, Fraschini Matteo
Dipartimento di Scienze Mediche e Sanità Pubblica, University of Cagliari, 09123 Cagliari, Italy.
UOC Laboratorio Analisi, AOU of Cagliari, 09123 Cagliari, Italy.
Diagnostics (Basel). 2024 Jul 25;14(15):1605. doi: 10.3390/diagnostics14151605.
With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.
随着全切片成像（WSI）技术的出现，这项能够对全切片进行高分辨率数字扫描的技术，病理学正在经历一场数字革命。检测结直肠癌中的微卫星不稳定性（MSI）对于正确治疗至关重要，因为它能识别出适合免疫治疗的患者。尽管推荐进行MSI的普遍检测，特别是在受结直肠癌（CRC）影响的患者中，但许多患者仍未接受检测，且他们主要分布在低收入国家。迫切需要可及的、低成本的工具来进行MSI预筛查。在此，讨论了最相关的人工智能驱动模型在仅从组织学直接预测微卫星不稳定性方面的潜在预测作用，重点是结直肠癌。本文在最相关的研究中分析了深度学习（DL）模型在识别MSI状态方面的作用，这些研究报告了为此目的训练的算法的开发情况。讨论了每种人工智能方法的最重要性能和最相关不足。报告了为多个研究和临床中心之间的算法共享而提出的模型，包括联邦学习（FL）和群体学习（SL）。根据本文报道的所有研究，人工智能模型是仅在结直肠癌中通过WSI预测MSI状态的有价值工具。使用数字化苏木精和伊红染色切片以及经过训练的算法，可以在短时间内以低成本提取相关分子信息，如MSI状态。本文强调了在常规手术病理学中引入DL方法可能带来的优势，并建议加速病理科和服务的数字化转型。
