Kabir Saidul, Chowdhury Muhammad E H, Sarmun Rusab, Vranić Semir, Al Saady Rafif Mahmood, Rose Inga, Gatalica Zoran
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh.
Department of Electrical Engineering, Qatar University, Doha, Qatar.
Biomol Biomed. 2025 Mar 3. doi: 10.17305/bb.2025.12056.
A critical predictive marker for anti-PD-1/PD-L1 therapy is programmed death-ligand 1 (PD-L1) expression, assessed by immunohistochemistry (IHC). This paper explores a novel automated framework using deep learning to accurately evaluate PD-L1 expression from whole slide images (WSIs) of non-small cell lung cancer (NSCLC), aiming to improve the precision and consistency of Tumor Proportion Score (TPS) evaluation, which is essential for determining patient eligibility for immunotherapy. Automating TPS evaluation can enhance accuracy and consistency while reducing pathologists' workload. The proposed automated framework encompasses three stages: identifying tumor patches, segmenting tumor areas, and detecting cell nuclei within these areas, followed by estimating the TPS based on the ratio of positively stained to total viable tumor cells. This study utilized a Reference Medicine (Phoenix, Arizona) dataset containing 66 NSCLC tissue samples, adopting a hybrid human-machine approach for annotating extensive WSIs. Patches of size 1000x1000 pixels were generated to train classification models such as EfficientNet, Inception, and Vision Transformer models. Additionally, segmentation performance was evaluated across various UNet and DeepLabV3 architectures, and the pre-trained StarDist model was employed for nuclei detection, replacing traditional watershed techniques. PD-L1 expression was categorized into three levels based on TPS: negative expression (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). The Vision Transformer-based model excelled in classification, achieving an F1-score of 97.54%, while the modified DeepLabV3+ model led in segmentation, attaining a Dice Similarity Coefficient of 83.47%. The TPS predicted by the framework closely correlated with the pathologist's TPS at 0.9635, and the framework's three-level classification F1-score was 93.89%. The proposed deep learning framework for automatically evaluating the TPS of PD-L1 expression in NSCLC demonstrated promising performance. This framework presents a potential tool that could produce clinically significant results more efficiently and cost-effectively.
抗PD-1/PD-L1治疗的一个关键预测标志物是通过免疫组织化学（IHC）评估的程序性死亡配体1（PD-L1）表达。本文探索了一种使用深度学习的新型自动化框架，以从非小细胞肺癌（NSCLC）的全切片图像（WSIs）中准确评估PD-L1表达，旨在提高肿瘤比例评分（TPS）评估的精度和一致性，这对于确定患者是否适合免疫治疗至关重要。自动化TPS评估可以提高准确性和一致性，同时减轻病理学家的工作量。所提出的自动化框架包括三个阶段：识别肿瘤斑块、分割肿瘤区域以及检测这些区域内的细胞核，然后根据阳性染色的存活肿瘤细胞与总存活肿瘤细胞的比例估计TPS。本研究使用了一个包含66个NSCLC组织样本的参考医学（亚利桑那州凤凰城）数据集，采用人机混合方法对大量WSIs进行注释。生成大小为1000x1000像素的斑块来训练分类模型，如EfficientNet、Inception和视觉Transformer模型。此外，还评估了各种UNet和DeepLabV3架构的分割性能，并采用预训练的StarDist模型进行细胞核检测，取代传统的分水岭技术。根据TPS将PD-L1表达分为三个水平：阴性表达（TPS<1%）、低表达（TPS 1-49%）和高表达（TPS≥50%）。基于视觉Transformer的模型在分类方面表现出色，F1分数达到97.54%，而改进的DeepLabV3+模型在分割方面领先，获得了83.47%的骰子相似系数。该框架预测的TPS与病理学家的TPS密切相关，相关系数为0.9635，框架的三级分类F1分数为93.89%。所提出的用于自动评估NSCLC中PD-L1表达TPS的深度学习框架表现出了良好的性能。该框架是一种潜在的工具，可以更高效、更经济地产生具有临床意义的结果。
