Kim Dongwoo, Lee Jongwon, Jung Minsoo, Yim Kwangil, Hwang Gisu, Yoon Hongjun, Jeong Daeky, Cho Won June, Alam Mohammad Rizwan, Gong Gyungyub, Cho Nam Hoon, Yoo Chong Woo, Chong Yosep, Seo Kyung Jin
The Catholic University of Korea College of Medicine, Seoul, South Korea.
Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul, South Korea.
Lung Cancer. 2025 Jun;204:108552. doi: 10.1016/j.lungcan.2025.108552. Epub 2025 Apr 21.
Cytological diagnosis of pleural effusion plays an important role in the early detection and diagnosis of lung cancers. Recently, attempts have been made to overcome low diagnostic accuracy and interobserver variability using artificial intelligence-based image analysis. However, such analysis is primarily performed at the image-patch level and not at the whole-slide image (WSI) level. This study aims to develop a WSI-level classification of malignant effusions in metastatic lung cancer based on pleural fluid cytology using a quality-controlled, nationwide dataset.
The dataset was collected by a consortium research group that included three major university hospitals and the Quality Assurance Program Committee of the Korean Society of Cytopathology. It contains 576 normal and 309 cancer WSIs from pleural fluids. A clustering-constrained attention multiple-instance learning (CLAM) model was used for WSI-level classification.
The CLAM model achieved a high accuracy of 97%, with an area under the curve of 0.97, representing a 13% improvement over the image patch classification model-based WSI classification. It also significantly reduced the analysis time and computing resources compared to those required during image patch-level classification and heat map generation on the WSIs.
The CLAM model successfully demonstrated high performance in differentiating malignant effusion at the WSI level using a large, quality-controlled, nationwide dataset. Further external validation is required to ensure generalizability.
胸腔积液的细胞学诊断在肺癌的早期检测和诊断中起着重要作用。最近，人们尝试使用基于人工智能的图像分析来克服低诊断准确性和观察者间变异性的问题。然而，这种分析主要在图像块级别进行，而非在全切片图像（WSI）级别。本研究旨在利用一个质量受控的全国性数据集，开发基于胸水细胞学的转移性肺癌恶性胸腔积液的WSI级别分类方法。
该数据集由一个联合研究小组收集，该小组包括三家主要大学医院和韩国细胞病理学学会质量保证计划委员会。它包含来自胸水的576张正常和309张癌症WSI。使用聚类约束注意力多实例学习（CLAM）模型进行WSI级别分类。
CLAM模型实现了97%的高准确率，曲线下面积为0.97，比基于图像块分类模型的WSI分类提高了13%。与在WSI上进行图像块级别分类和生成热图所需的时间和计算资源相比，它还显著减少了分析时间和计算资源。
CLAM模型使用一个大型、质量受控的全国性数据集，成功地在WSI级别区分恶性胸腔积液方面展示了高性能。需要进一步的外部验证以确保其通用性。
