AI in Computational Pathology of Cancer: Improving DiagnosticWorkflows and Clinical Outcomes?

被引：13

作者：

Cifci, Didem ^{[1
]}

Veldhuizen, Gregory P. ^{[2
]}

Foersch, Sebastian ^{[3
]}

Kather, Jakob Nikolas ^{[1
,2
,4
,5
]}

机构：

[1] Rhein Wesrfal TH, Univ Hosp RWTH, Dept Med 3, Aachen, Germany

[2] Tech Univ Dresden, Univ Hosp Carl Gustav Carus Dresden, Else Kroener Fresenius Ctr Digital Hlth, Dresden, Germany

[3] Univ Med Ctr Mainz, Dept Pathol, Mainz, Germany

[4] Univ Hosp Heidelberg, Natl Ctr Tumor Dis, Dept Med Oncol, Heidelberg, Germany

[5] Univ Leeds, Leeds Inst Med Res St Jamess, Div Pathol & Data Analyt, Leeds, W Yorkshire, England

来源：

ANNUAL REVIEW OF CANCER BIOLOGY | 2023年 / 7卷

关键词：

machine learning; deep learning; oncology; tumor; biomarkers; histopathology; ARTIFICIAL-INTELLIGENCE; HISTOPATHOLOGICAL IMAGES; SURVIVAL PREDICTION; PROSTATE-CANCER; DEEP; DIAGNOSIS; BIOMARKERS; SIGNATURES; FUSION;

D O I：

10.1146/annurev-cancerbio-061521-092038

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and has become a cornerstone of modern precision oncology. Histopathological evaluation is typically performed manually by expert pathologists due to the complexity of visual data. However, in the last ten years, new artificial intelligence (AI) methods have made it possible to train computers to perform visual tasks with high performance, reaching similar levels as experts in some applications. In cancer histopathology, these AI tools could help automate repetitive tasks, making more efficient use of pathologists' time. In research studies, AI methods have been shown to have an astounding ability to predict genetic alterations and identify prognostic and predictive biomarkers directly from routine tissue slides. Here, we give an overview of these recent applications of AI in computational pathology, focusing on new tools for cancer research that could be pivotal in identifying clinical biomarkers for better treatment decisions.

引用

页码：57 / 71

页数：15

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