Number of images in each dataset.

Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. However, traditional cytological diagnosis requires an experienced pathologist to evaluate all sections individually under a microscope, which is a time-consuming process with low interobserver consistency. With the development of deep neural networks, the You Only Look Once (YOLO) object-detection model has been recognized for its impressive speed and accuracy. Thus, in this study, we developed a model for intraoperative cytological segmentation of pulmonary lesions based on the YOLOv8 algorithm, which labels each instance by segmenting the image at the pixel level. The model achieved a mean pixel accuracy and mean intersection over union of 0.80 and 0.70, respectively, on the test set. At the image level, the accuracy and area under the receiver operating characteristic curve values for malignant and benign (or normal) lesions were 91.0% and 0.90, respectively. In addition, the model was deemed suitable for diagnosing pleural fluid cytology and bronchoalveolar lavage fluid cytology images. The model predictions were strongly correlated with pathologist diagnoses and the gold standard, indicating the model’s ability to make clinical-level decisions during initial diagnosis. Thus, the proposed method is useful for rapidly localizing lung cancer cells based on microscopic images and outputting image interpretation results.

与肺癌组织学检查相比，细胞学检查（cytology）的侵入性更低，且可更完整地保留病变的整体形态与细节信息。然而传统细胞学诊断需由经验丰富的病理学家在显微镜下逐一评估所有切片，该过程耗时较长且观察者间一致性（interobserver consistency）较低。随着深度神经网络（deep neural networks）的发展，你只需要看一次（You Only Look Once，简称YOLO）目标检测模型因其优异的速度与精度得到广泛认可。因此，本研究基于YOLOv8算法构建了一种肺部病变术中细胞学分割模型，该模型通过像素级图像分割对每个目标实例进行标注。该模型在测试集上的平均像素准确率（mean pixel accuracy）与平均交并比（mean intersection over union）分别为0.80与0.70。在图像层面，针对恶性与良性（或正常）病变的准确率与受试者工作特征曲线（receiver operating characteristic curve，简称ROC曲线）下面积分别为91.0%与0.90。此外，该模型被证实可适用于胸腔积液细胞学与支气管肺泡灌洗液细胞学图像的诊断任务。模型预测结果与病理学家诊断及金标准（gold standard）具有高度相关性，表明该模型具备在初始诊断阶段做出临床级决策的能力。因此，本研究提出的方法可基于显微图像快速定位肺癌细胞并输出图像判读结果。