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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.
Baharun et al. (Mon,) studied this question.