数字组织病理学中的人工智能用于预测乳腺癌患者的预后和治疗效果。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
William M. Gallagher, Christine McCaffrey, C. Jahangir, Clodagh Murphy, Caoimbhe Burke, Arman Rahman
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引用次数: 0

摘要

导言组织学图像包含可预测患者预后的表型信息。由于病理学家的工作量繁重、定量评估组织学特征耗时以及人眼识别空间模式的局限性,在常规病理工作流程中手动提取预后信息仍具有挑战性。数字病理学利用全切片图像(WSI)扫描仪和人工智能(AI)算法促进了对这些特征的挖掘和量化。从肿瘤微环境(TME)中识别基于图像的生物标记物的人工智能算法有可能彻底改变肿瘤学领域,减少诊断与预后判断之间的延迟,对患者进行快速分层并制定最佳治疗方案,从而改善患者的预后。在这篇综述中,作者讨论了人工智能算法和数字病理学如何利用基于图像的生物标志物来预测乳腺癌患者的预后和治疗效果,以及在临床环境中采用这项技术所面临的挑战。人工智能在临床上的广泛应用面临着伦理、监管和技术方面的挑战,不过前瞻性试验可以提供保证并促进其应用,最终通过减少诊断到预后的延迟来改善患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence in Digital Histopathology for predicting patient prognosis and treatment efficacy in breast cancer.
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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