Department of Pathology, Faculty of Medicine, The National University of Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Wilayah Persekutuan, 56000, Malaysia.
Department of Medical Diagnostic, Faculty of Health Sciences, Universiti Selangor, Jalan Zirkon A7/7, Seksyen 7, Shah Alam, Selangor, 40000, Malaysia.
BMC Cancer. 2024 Sep 30;24(1):1202. doi: 10.1186/s12885-024-12962-8.
Tumour microenvironment (TME) of breast cancer mainly comprises malignant, stromal, immune, and tumour infiltrating lymphocyte (TILs). Assessment of TILs is crucial for determining the disease's prognosis. Manual TIL assessments are hampered by multiple limitations, including low precision, poor inter-observer reproducibility, and time consumption. In response to these challenges, automated scoring emerges as a promising approach. The aim of this systematic review is to assess the evidence on the approaches and performance of automated scoring methods for TILs assessment in breast cancer. This review presents a comprehensive compilation of studies related to automated scoring of TILs, sourced from four databases (Web of Science, Scopus, Science Direct, and PubMed), employing three primary keywords (artificial intelligence, breast cancer, and tumor-infiltrating lymphocytes). The PICOS framework was employed for study eligibility, and reporting adhered to the PRISMA guidelines. The initial search yielded a total of 1910 articles. Following screening and examination, 27 studies met the inclusion criteria and data were extracted for the review. The findings indicate a concentration of studies on automated TILs assessment in developed countries, specifically the United States and the United Kingdom. From the analysis, a combination of sematic segmentation and object detection (n = 10, 37%) and convolutional neural network (CNN) (n = 11, 41%), become the most frequent automated task and ML approaches applied for model development respectively. All models developed their own ground truth datasets for training and validation, and 59% of the studies assessed the prognostic value of TILs. In conclusion, this analysis contends that automated scoring methods for TILs assessment of breast cancer show significant promise for commodification and application within clinical settings.
乳腺癌的肿瘤微环境（TME）主要包括恶性、基质、免疫和肿瘤浸润淋巴细胞（TILs）。评估 TILs 对于确定疾病的预后至关重要。手动 TIL 评估受到多种限制，包括精度低、观察者间重现性差和耗时。为了应对这些挑战，自动评分方法应运而生，成为一种很有前途的方法。本系统综述旨在评估自动评分方法在乳腺癌 TIL 评估中的方法和性能的证据。
本综述全面收集了与 TIL 自动评分相关的研究，来自四个数据库（Web of Science、Scopus、Science Direct 和 PubMed），使用三个主要关键词（人工智能、乳腺癌和肿瘤浸润淋巴细胞）。采用 PICOS 框架评估研究的纳入标准，并按照 PRISMA 指南进行报告。最初的搜索共产生了 1910 篇文章。经过筛选和检查，有 27 项研究符合纳入标准，并提取数据进行综述。
研究结果表明，大多数关于自动 TILs 评估的研究集中在发达国家，特别是美国和英国。从分析中可以看出，语义分割和目标检测的组合（n=10，37%）和卷积神经网络（CNN）（n=11，41%）分别成为最常见的自动任务和用于模型开发的 ML 方法。所有开发的模型都为训练和验证建立了自己的真实数据集，59%的研究评估了 TILs 的预后价值。总之，本分析认为，乳腺癌 TILs 评估的自动评分方法在商品化和临床应用方面具有很大的潜力。
