The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.
早期肺腺癌中气腔播散的存在是与疾病复发及不良预后相关的重要预测因素。尽管当前STAS检测方法依赖于病理学检查，但人工智能的发展为自动化组织病理学图像分析提供了可能。本研究开发了一种用于STAS预测的深度学习模型，并探讨了预测结果与患者预后之间的相关性。为构建基于深度学习的STAS预测模型，研究采用竞赛数据集的1053张数字病理全切片图像作为训练集，并纳入国立台湾大学医院的227张全切片图像进行外部验证。研究提出基于YOLOv5的预测框架，包含预处理、候选区域检测、假阳性排除和基于患者的预测四个步骤。该模型预测STAS存在的曲线下面积为0.83，准确率72%，敏感性81%，特异性63%。此外，与病理学评估相比，深度学习模型在无病生存期预测方面展现出预后价值。这些发现表明，基于深度学习的STAS预测可作为辅助筛查工具，有助于早期肺腺癌患者的临床决策。
肿瘤(癌症)患者之家