National University of Sciences and Technology (NUST), Islamabad, Pakistan.
J Imaging Inform Med. 2024 Aug;37(4):1728-1751. doi: 10.1007/s10278-024-01049-2. Epub 2024 Mar 1.
Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.
生存分析是医学统计学的一个组成部分，广泛用于建立死亡率或疾病复发的预后指标，评估治疗效果，并制定有效的治疗计划。识别能够预测患者生存的预后生物标志物是癌症研究领域的主要目标。随着数字组织学图像最近整合到常规临床实践中，学术文献中出现了大量基于人工智能（AI）的数字病理学方法，有助于预测患者的生存情况。这些方法在分析和解释全切片图像方面表现出了很高的准确性，其结果可与专家病理学家相媲美。AI 驱动技术的复杂性因数字组织学图像的独特特征而加剧，包括其千兆像素的大小和不同的组织外观。因此，采用先进的基于补丁的方法来有效地提取与患者生存相关的特征。这些计算方法极大地提高了生存预测的准确性，并增强了癌症患者的预后能力。这篇综述讨论了文献中使用的方法、它们的性能指标、当前的挑战以及未来发展的潜在解决方案。本文介绍了用于分析癌症患者的生存分析和特征提取方法。它还汇编了与癌症精准医学相关的重要缩写词。此外，值得注意的是，这是该领域的第一篇综述论文。这篇跨学科综述的目标读者包括 AI 从业者、医学统计学家和积极将 AI 驱动的解决方案转化为临床实践的进步肿瘤学家。我们希望这篇全面的综述文章能够指导癌症研究领域的未来研究方向。
