Department of Computer Science, University of Warwick, Coventry, UK.
Department of Pathology, Shaukat Khanum Memorial Cancer Hospital Research Centre, Lahore, Pakistan.
J Pathol. 2022 Feb;256(2):174-185. doi: 10.1002/path.5819. Epub 2021 Nov 24.
The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim was to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI-based automated method. A deep learning-based automated method was employed to segment tumour, tumour-associated stroma, and lymphocytes in digitally scanned whole slide images of HNSCC tissue slides. The spatial patterns of lymphocytes and tumour-associated stroma were digitally quantified to compute the tumour-associated stroma infiltrating lymphocytes score (TASIL-score). Finally, the prognostic significance of the TASIL-score for disease-specific and disease-free survival was investigated using the Cox proportional hazard analysis. Three different cohorts of haematoxylin and eosin (H&E)-stained tissue slides of HNSCC cases (n = 537 in total) were studied, including publicly available TCGA head and neck cancer cases. The TASIL-score carries prognostic significance (p = 0.002) for disease-specific survival of HNSCC patients. The TASIL-score also shows a better separation between low- and high-risk patients compared with the manual tumour-infiltrating lymphocytes (TILs) scoring by pathologists for both disease-specific and disease-free survival. A positive correlation of TASIL-score with molecular estimates of CD8 T cells was also found, which is in line with existing findings. To the best of our knowledge, this is the first study to automate the quantification of TASILs from routine H&E slides of head and neck cancer. Our TASIL-score-based findings are aligned with the clinical knowledge, with the added advantages of objectivity, reproducibility, and strong prognostic value. Although we validated our method on three different cohorts (n = 537 cases in total), a comprehensive evaluation on large multicentric cohorts is required before the proposed digital score can be adopted in clinical practice. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
肿瘤间质中 T 淋巴细胞的浸润是针对肿瘤的有效免疫反应的标志,从而导致更好的生存。在这项研究中,我们的目的是通过基于人工智能的自动方法探索头颈部鳞状细胞癌(HNSCC)中肿瘤相关基质浸润淋巴细胞(TASIL)的预后意义。采用基于深度学习的自动方法对头颈部鳞状细胞癌组织切片的数字扫描全玻片图像进行肿瘤、肿瘤相关基质和淋巴细胞的分割。对淋巴细胞和肿瘤相关基质的空间模式进行数字化量化,以计算肿瘤相关基质浸润淋巴细胞评分(TASIL 评分)。最后,使用 Cox 比例风险分析研究 TASIL 评分对头颈部鳞状细胞癌患者疾病特异性和无病生存率的预后意义。研究了三种不同的 H&E 染色头颈部鳞状细胞癌组织切片队列(总共 537 例),包括公开的 TCGA 头颈部癌症病例。TASIL 评分对头颈部鳞状细胞癌患者的疾病特异性生存率具有预后意义(p=0.002)。与病理学家对手动肿瘤浸润淋巴细胞(TIL)评分相比,TASIL 评分在疾病特异性和无病生存率方面也能更好地区分低风险和高风险患者。还发现 TASIL 评分与 CD8 T 细胞的分子估计值呈正相关,这与现有发现一致。据我们所知,这是第一项从头颈部癌症的常规 H&E 幻灯片中自动量化 TASIL 的研究。我们基于 TASIL 评分的研究结果与临床知识一致,具有客观性、可重复性和较强预后价值的优势。尽管我们在三个不同的队列(总共 537 例病例)上验证了我们的方法,但在提出的数字评分可以在临床实践中采用之前,还需要对大型多中心队列进行全面评估。©2021 作者。John Wiley & Sons Ltd 代表英国和爱尔兰的病理学会出版了《病理学杂志》。