Li Jinze, Dong Pei, Wang Xinran, Zhang Jun, Zhao Meng, Shen Haocheng, Cai Lijing, He Jiankun, Han Mengxue, Miao Jiaxian, Liu Hongbo, Yang Wei, Han Xiao, Liu Yueping
Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
AI Lab, Tencent, Shenzhen, Guangdong, China.
Histopathology. 2024 Sep;85(3):451-467. doi: 10.1111/his.15205. Epub 2024 May 15.
Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable.
We proposed a model in a deep learning-based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole-slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty-one pathologists of different levels from four institutions evaluated the PD-L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)-assisted method.
In the visual assessment, the interpretation results of PD-L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI-assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524-0.719] to 0.931 (95% CI = 0.902-0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886-0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall.
With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice.
评估程序性细胞死亡配体1（PD-L1）联合阳性评分（CPS）对于预测三阴性乳腺癌（TNBC）免疫治疗的疗效至关重要，但病理学家在解读的一致性和准确性方面存在很大差异。建立一种客观、有效且具有高度可重复性的方法非常重要。
我们在基于深度学习的框架中提出了一个模型，该模型在斑块水平纳入细胞分析和组织区域分析，然后在整张切片水平融合斑块结果。进行了三轮环式研究（RSs）。来自四个机构的21名不同水平的病理学家通过视觉评估和我们的人工智能（AI）辅助方法将TNBC标本中的PD-L1 CPS评估为连续分数。
在视觉评估中，不同水平的病理学家对PD-L1（Dako 22C3）CPS的解读结果存在显著差异，一致性较弱。使用AI辅助解读时，所有病理学家之间无显著差异（P = 0.43），组内相关系数（ICC）值从0.618[95%置信区间（CI）= 0.524 - 0.719]提高到0.931（95% CI = 0.902 - 0.955）。解读结果的准确性进一步提高到0.919（95% CI = 0.886 - 0.947）。初级病理学家对AI结果的接受度在所有水平中最高，总体上80%的AI结果被接受。
借助AI辅助诊断方法，不同水平的病理学家在PD-L1（Dako 22C3）CPS的解读中实现了出色的一致性和可重复性。我们的AI辅助诊断方法被证明可加强临床实践中的一致性和可重复性。
