A comprehensive review of tubule formation in histopathology images: advancement in tubule and tumor detection techniques

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Joseph Jiun Wen Siet, Xiao Jian Tan, Wai Loon Cheor, Khairul Shakir Ab Rahman, Ee Meng Cheng, Wan Zuki Azman Wan Muhamad, Sook Yee Yip
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引用次数: 0

Abstract

Breast cancer, the earliest documented cancer in history, stands as a foremost cause of mortality, accounting for 684,996 deaths globally in 2020 (15.5% of all female cancer cases). Irrespective of socioeconomic factors, geographic locations, race, or ethnicity, breast cancer ranks as the most frequently diagnosed cancer in women. The standard grading for breast cancer utilizes the Nottingham Histopathology Grading (NHG) system, which considers three crucial features: mitotic counts, nuclear pleomorphism, and tubule formation. Comprehensive reviews on features, for example, mitotic count and nuclear pleomorphism have been available thus far. Nevertheless, a thorough investigation specifically focusing on tubule formation aligned with the NHG system is currently lacking. Motivated by this gap, the present study aims to unravel tubule formation in histopathology images via a comprehensive review of detection approaches involving tubule and tumor features. Without temporal constraints, a structured methodology is established in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in 12 articles for tubule detection and 67 included articles for tumor detection. Despite the primary focus on breast cancer, the structured search string extends beyond this domain to encompass any cancer type utilizing histopathology images as input, focusing on tubule and tumor detection. This broadened scope is essential. Insights from approaches in tubule and tumor detection for various cancers can be assimilated, integrated, and contributed to an enhanced understanding of tubule formation in breast histopathology images. This study compiles evidence-based analyses into a cohesive document, offering comprehensive information to a diverse audience, including newcomers, experienced researchers, and stakeholders interested in the subject matter.

Abstract Image

组织病理学图像中小管形成的全面回顾:小管和肿瘤检测技术的进步
乳腺癌是历史上记载最早的癌症,也是导致死亡的首要原因,2020 年全球有 684 996 人死于乳腺癌(占所有女性癌症病例的 15.5%)。无论社会经济因素、地理位置、种族或民族如何,乳腺癌都是女性最常诊断出的癌症。乳腺癌的标准分级采用诺丁汉组织病理学分级(NHG)系统,该系统考虑了三个关键特征:有丝分裂计数、核多形性和小管形成。迄今为止,已有关于有丝分裂计数和核多形等特征的全面综述。然而,目前还缺乏专门针对与 NHG 系统一致的小管形成的深入研究。基于这一空白,本研究旨在通过全面回顾涉及小管和肿瘤特征的检测方法,揭示组织病理学图像中的小管形成。在没有时间限制的情况下,根据系统综述和荟萃分析首选报告项目(PRISMA)指南建立了结构化的方法,最终有 12 篇文章涉及小管检测,67 篇文章涉及肿瘤检测。尽管主要关注的是乳腺癌,但结构化搜索字符串的范围超出了这一领域,涵盖了使用组织病理学图像作为输入的任何癌症类型,重点关注小管和肿瘤检测。扩大搜索范围至关重要。从各种癌症的小管和肿瘤检测方法中获得的启示可以被吸收、整合,并有助于加深对乳腺组织病理学图像中小管形成的理解。本研究将以证据为基础的分析汇编成一份有凝聚力的文件,为不同受众(包括新手、有经验的研究人员以及对该主题感兴趣的相关人士)提供全面的信息。
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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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