Pei Guotian, Wang Dawei, Sun Kunkun, Yang Yingshun, Tang Wen, Sun Yanfeng, Yin Siyuan, Liu Qiang, Wang Shuai, Huang Yuqing
Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China.
Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China.
Front Oncol. 2023 Jul 20;13:1224455. doi: 10.3389/fonc.2023.1224455. eCollection 2023.
Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice.
Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance.
The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies.
DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.
根据2021年世界卫生组织肺肿瘤分类更新和2020年新分级系统，IA期肺腺癌（LUAD）组织学亚型和分级的术前预测模型尚待探索。我们旨在通过基于CT的放射组学方法开发IA期LUAD患者的非侵入性病理和分级评估方法，并评估其在临床实践中的性能。
回顾性收集两家医院诊断为IA期LUAD并接受完整切除的患者的胸部CT扫描。首先应用深度学习分割算法辅助病变勾勒。进一步应用边界框标注等扩展策略。然后提取并选择放射组学特征，接着基于四种经典机器学习算法进行放射组学建模，用于组织学亚型分类和分级分层。采用受试者操作特征曲线下面积（AUC）评估模型性能。
该研究分别纳入了两家医院的294例和145例IA期LUAD患者进行放射组学分析。对于四种组织学亚型的分类，多层感知器（MLP）算法对标注策略无偏好，在采用1像素扩展标注的内部、独立和外部测试集上，平均AUC分别为0.855、0.922和0.720。边界框标注策略也使MLP在测试集中具有可接受且稳定的准确性。同时，选择逻辑回归进行分级分层，在采用最佳标注策略的内部、独立和外部测试集上，平均AUC分别为0.928、0.837和0.748。
基于深度学习增强的放射组学模型有很大潜力基于CT图像预测早期LUAD的精细组织学亚型和分级，这可能是早期LUAD诊断和管理的一种有前景的非侵入性方法。
