结直肠癌
比例危险模型
单变量
肿瘤科
危险系数
内科学
医学
肿瘤浸润淋巴细胞
单变量分析
疾病
癌症
多元统计
多元分析
机器学习
置信区间
免疫疗法
计算机科学
作者
Anran Liu,Xingyu Li,Hongyi Wu,Bangwei Guo,Jitendra Jonnagaddala,Hong Zhang,Steven Xu
摘要
PURPOSE: Tumor-infiltrating lymphocytes (TILs) have a significant prognostic value in cancers. However, very few automated, deep learning-based TIL scoring algorithms have been developed for colorectal cancer (CRC). MATERIALS AND METHODS: ) for disease progression and overall survival (OS) was evaluated using two international data sets, including 554 patients with CRC from The Cancer Genome Atlas (TCGA) and 1,130 patients with CRC from Molecular and Cellular Oncology (MCO). RESULTS: and the risk of disease progression or death in both TCGA and MCO cohorts. Both univariate and multivariate Cox regression analyses for the TCGA data demonstrated that patients with high TIL abundance had a significant (approximately 75%) reduction in risk for disease progression. In both the MCO and TCGA cohorts, the TIL-high group was significantly associated with improved OS in univariate analysis (30% and 54% reduction in risk, respectively). The favorable effects of high TIL levels were consistently observed in different subgroups (classified according to known risk factors). CONCLUSION: for OS is also evident.
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