Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany; German Center for Lung Research (DZL), Partner Site Heidelberg, Heidelberg, Germany.
Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Aignostics GmbH, Schumannstr. 17, Berlin, 10117, Germany.
Semin Cancer Biol. 2022 Sep;84:129-143. doi: 10.1016/j.semcancer.2021.02.011. Epub 2021 Feb 22.
The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.
过去几十年来，诊断（外科）病理学在组织形态和分子分析方面的复杂性大大增加。病理学通过从疾病实体识别到预后评估再到精准治疗预测，不断扩大其在肿瘤诊断中的作用和范围。因此，病理学是医学领域中对人工智能（AI）或机器学习方法应用抱有很高期望的学科之一，因为这些方法能够以定量和标准化的方式分析复杂数据，从而进一步提高诊断的范围和精度。虽然明显的应用是分析组织学图像，但最近用于分析来自不同来源的分子分析数据和临床数据的应用支持了这样一种观点，即人工智能将在未来增强组织病理学和分子病理学。同时，不应误解当前的文献，认为病理学家在可预见的未来可能会被人工智能应用所取代。尽管人工智能将在未来几年改变病理学，但最近报道的用于诊断癌症或预测某些分子特性的人工智能算法所处理的问题相对简单，无法与病理学家在临床常规中面临的诊断复杂性相媲美。在这里，我们回顾了人工智能方法的相关文献及其在病理学中的应用，并根据研究和常规诊断的要求，阐述了当前的成就和未来可以预期的情况。
