CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：Machine Learning Quantification of Intraepithelial Tumor-Infiltrating Lymphocytes as a Significant Prognostic Factor in High-Grade Serous Ovarian Carcinomas.
Machine Learning Quantification of Intraepithelial Tumor-Infiltrating Lymphocytes as a Significant Prognostic Factor in High-Grade Serous Ovarian Carcinomas.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
多种肿瘤中已证实TIL（肿瘤浸润淋巴细胞）具有预后和预测作用。然而，少数探讨高级别浆液性卵巢癌（HGSOC）的研究大多采用半定量方式评估TIL。
因此，需要基于更准确的TIL定量开展临床相关性研究。我们基于76例分子和临床特征明确的晚期HGSOC病例，使用苏木精-伊红（H&E）染色切片建立机器学习系统。该系统可对免疫细胞分类，并进一步分析这些免疫参数与总生存期（OS）及无进展间期（PFI）的关系。TIL密集浸润肿瘤细胞索与更佳预后相关。
此外，多变量分析显示，瘤内上皮（ie）TIL浓度是OS（p=0.02）和PFI（p=0.001）的独立有利预后因素。完全手术减瘤与ieTIL水平高之间还存在协同作用，无论OS（p=0.0005）还是PFI（p=0.0008）均可见此效应。
我们认为，机器学习数字分析提高了HGSOC中TIL定量的准确性。本研究证明，基于H&E染色切片的ieTIL定量是一项独立预后指标。瘤内上皮TIL定量可能有助于识别适合接受免疫治疗的候选患者。
The prognostic and predictive role of tumor-infiltrating lymphocytes (TILs) has been demonstrated in various neoplasms. The few publications that have addressed this topic in high-grade serous ovarian carcinoma (HGSOC) have approached TIL quantification from a semiquantitative standpoint. Clinical correlation studies, therefore, need to be conducted based on more accurate TIL quantification.
We created a machine learning system based on H&E-stained sections using 76 molecularly and clinically well-characterized advanced HGSOC. This system enabled immune cell classification. These immune parameters were subsequently correlated with overall survival (OS) and progression-free survival (PFI). An intense colonization of the tumor cords by TILs was associated with a better prognosis.
Moreover, the multivariate analysis showed that the intraephitelial (ie) TILs concentration was an independent and favorable prognostic factor both for OS ( p = 0. 02) and PFI ( p = 0. 001). A synergistic effect between complete surgical cytoreduction and high levels of ieTILs was evidenced, both in terms of OS ( p = 0. 0005) and PFI ( p = 0. 0008).
We consider that digital analysis with machine learning provided a more accurate TIL quantification in HGSOC. It has been demonstrated that ieTILs quantification in H&E-stained slides is an independent prognostic parameter. It is possible that intraepithelial TIL quantification could help identify candidate patients for immunotherapy.
MEMBER ACCOUNT
登录成功会直接打开下一页。
