Department of Pathology, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea.
lnstitute of Medical Sciences, Gyeongsang National University, Jinju, Republic of Korea.
In Vivo. 2024 Jan-Feb;38(1):246-252. doi: 10.21873/invivo.13431.
BACKGROUND/AIM: Immune checkpoint inhibitors (ICI) and tumor-infiltrating lymphocytes (TILs) for cancer treatment in clinical oncology have revolutionized patient care. However, no gold standard exists for the criteria of analytical validity of TILs of different types of cancer.
Clinicopathological data from 60 patients with endometrioid carcinoma (EC) who had undergone surgical treatment at the Gyeongsang National University Hospital between January 2002 and December 2009, were investigated. The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PDL1) expression levels were characterized by immunohistochemical staining patterns, and the interpretations derived from machine learning morphometric analysis (Genie) and the pathologists' assessments were compared. In solid tumors, pathologists assessed the proportion of positive cells in each core of the tissue microarray. For Genie, the proportion of positive cells in the entire core and the number of positive cells per 1 mm were used.
Both the pathologists and Genie identified the same trend in association with tumor size, with significant differences (p=0.026, p=0.033). Genie expression showed a significant association with PD1 expression, and pathologists identified a significant association with PDL1 expression in immune cells.
The PD1 expression levels identified in immune cells of EC specimens were similar between the pathologists and Genie, suggesting that there is little resistance to the introduction of morphometric analysis. To our knowledge, this is the first study to introduce and validate machine learning as an integrated method for predicting prognosis and treatment based on PD1 expression in EC tumor microenvironments.
背景/目的：免疫检查点抑制剂（ICI）和肿瘤浸润淋巴细胞（TIL）在临床肿瘤学中的癌症治疗已经彻底改变了患者的护理方式。然而，对于不同类型癌症的 TIL 的分析有效性标准，目前还没有金标准。
对 2002 年 1 月至 2009 年 12 月在全南国立大学医院接受手术治疗的 60 例子宫内膜样癌（EC）患者的临床病理数据进行了调查。通过免疫组织化学染色模式对程序性死亡蛋白 1（PD-1）/程序性死亡配体 1（PDL1）表达水平进行了特征描述，并比较了来自机器学习形态计量分析（Genie）和病理学家评估的解释。在实体瘤中，病理学家评估了组织微阵列中每个核心的阳性细胞比例。对于 Genie，使用整个核心中的阳性细胞比例和每 1mm 的阳性细胞数。
病理学家和 Genie 都识别出与肿瘤大小相关的相同趋势，差异具有统计学意义（p=0.026，p=0.033）。Genie 表达与 PD1 表达呈显著相关，病理学家鉴定与免疫细胞中 PDL1 表达呈显著相关。
EC 标本免疫细胞中鉴定的 PD1 表达水平在病理学家和 Genie 之间相似，表明对形态计量分析的引入几乎没有抵抗力。据我们所知，这是第一项介绍和验证机器学习作为基于 EC 肿瘤微环境中 PD1 表达预测预后和治疗的综合方法的研究。
