东华大学学报（英文版）2024，Vol.41Issue(3)：221-230,10.DOI:10.19884/j.1672-5220.202307005
多目标系统化学习的PD-L1切片分析方法
Systematic PD-L1 Slide Analysis Based on Multi-Objective Learning
摘要
Abstract
In treatment of cancers,especially non-small-cell lung cancers such as lung squamous cell carcinoma(LUSC),tumor proportion score(TPS)of a programmed death-ligand 1(PD-L1)slide is essential for selecting tumor therapies.Many parameters of tumor cells(TCs)are vital to cancer diagnosis.Although the indexes can be estimated via the computational analysis,there is seldom a unified system that could acquire different nucleus information simultaneously.To address the issues,multi-objective learning pipeline(MOLP)is proposed to predict TPS,cell counts,nucleus contours and categories altogether from PD-L1 slides of LUSC.The main network comprises two branches,one estimating TPS via the cell analysis and the other directly regressing TPS.It minimizes the difference between these two approximated values of TPS to gain robustness.The cell-analysis branch increases confidence of the estimated TPS by nucleus segmentation,classification and counting.It also enables the system to estimate appearance parameters of TCs for LUSC diagnosis.Experiments on a large image set show that MOLP is feasible and effective.The TPS predicted by MOLP exhibits statistically significant correlation with pathologists'scores,with a mean absolute error(MAE)of 4.97(95％confidence interval(CI):-0.56-10.49)and a Pearson correlation coefficient(PCC)of 0.97(p＜0.001).关键词
程序性死亡受体-配体1(PD-L1)切片/阳性肿瘤细胞比例评分(TPS)/多目标学习/分类/分割/计数Key words
programmed death-ligand 1(PD-L1)slide/tumor proportion score(TPS)/multi-objective learning/classification/segmentation/counting分类
信息技术与安全科学引用本文复制引用
陈昭,郭丹琦,王倩,沈熠婷,王庆国..多目标系统化学习的PD-L1切片分析方法[J].东华大学学报（英文版）,2024,41(3):221-230,10.基金项目
National Natural Science Foundation of China(Nos.61702094 and 62301142) （Nos.61702094 and 62301142）
"Chenguang Program"Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.18CG38) （No.18CG38）
