Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Department of Human Oncology and UW Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Oral Oncol. 2024 May;152:106750. doi: 10.1016/j.oraloncology.2024.106750. Epub 2024 Mar 27.
The prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts.
A network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and "other" cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation.
We found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival).
Neural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.
多年来，多项研究已经证明了病理学家解读的肿瘤浸润淋巴细胞（TILs）对头颈部癌症的预后和预测意义。TILs 尚未广泛应用于临床，这可能是由于观察者间存在很大的差异。在这项研究中，我们开发了一种基于机器的头颈部癌症 TIL 评估算法，并在独立队列中验证了其预后价值。
一种名为 NN3-17 的网络分类器被训练用于识别和计算苏木精-伊红染色切片上的肿瘤细胞、淋巴细胞、成纤维细胞和“其他”细胞，并使用 QuPath 软件进行计算。这些测量值用于构建三个预先定义的 TIL 变量。我们回顾性地收集了 154 例头颈部鳞状细胞癌病例作为发现集，以确定 TIL 变量与生存的最佳关联。另外两个独立的 234 例病例队列用于验证。
我们发现电子 TIL 变量与 HPV 阳性和阴性病例的预后良好相关。在调整了临床病理因素后，Cox 回归分析表明，HPV 阳性病例中电子总 TILs%（p=0.025）和 HPV 阴性病例中电子基质 TILs%（p<0.001）是疾病特异性结局（无病生存）的独立标志物。
神经网络 TIL 变量在 HPV 阳性和 HPV 阴性头颈部癌症的验证队列中显示出独立的预后价值。这些客观变量可以通过开源软件计算，并可以考虑在前瞻性研究中进行测试，以评估其潜在的临床意义。
