التفاصيل البيبلوغرافية
| العنوان: |
Systematic PD-L1 Slide Analysis Based on Multi-Objective Learning. |
| Alternate Title: |
多目标系统化学习的 PD-L1 切片分析方法. (Chinese) |
| المؤلفون: |
CHEN Zhao, GUO Danqi, WANG Qian, SHEN Yiting, WANG Qingguo |
| المصدر: |
Journal of Donghua University (English Edition); Jun2024, Vol. 41 Issue 3, p221-230, 10p |
| مصطلحات موضوعية: |
NON-small-cell lung carcinoma, CANCER treatment, CANCER diagnosis, CANCER cells, PATHOLOGISTS |
| Abstract (English): |
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). [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
在肿瘤尤其是如肺鳞癌 (lung squamous cell carcinoma, LUSC) 的非小细胞肺癌的治疗中, 基于 程序性死亡受体-配体 1 ( programmed cell death-ligand 1, PD-L1) 染色切片的阳性肿瘤细胞比例评分 (tumor proportion score, TPS) 可为治疗方案的选择提供重要依据。 肿瘤细胞 (tumor cell, TC) 的许多参 数对癌症诊断至关重要。 虽然可以通过计算分析来预测这些参数, 但很少有一个统一的框架可以同时获得 细胞的不同病理信息。 为此, 提出了一种多目标学习框架 (multi-objective learning pipeline, MOLP), 从 LUSC 的 PD-L1 切片中预测 TPS、 细胞数目、 细胞核轮廓和类别。 主干网络包括两个分支: 一个分支通过 细胞分析估算 TPS, 另一个分支直接通过回归分析估算 TPS。 MOLP 通过最小化两个分支的 TPS 预测差值 来提高其鲁棒性。 细胞分析支路可实现细胞核分割、 分类和计数, 不仅增强了 TPS 估计的可信度, 还使得 MOLP 能够估计肿瘤细胞的外观参数以用于 LUSC 诊断。 在大规模图像集上的实验结果证明了 MOLP 的可 行性和有效性。 MOLP 预测的 TPS 与病理医师的评分呈现出统计学上的显著相关性: 平均绝对误差仅为 4. 97 (95%置信区间: -0. 56~ 10. 49), 皮尔逊相关系数为 0. 97 (p<0. 001)。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Supplemental Index |
