Table_3_Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.xlsx

BackgroundPreoperative 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.
MethodsChest 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.
ResultsThe 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.
ConclusionsDL-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.

背景
针对依据2021版世界卫生组织肺肿瘤分类及2020年新版分级系统定义的IA期肺腺癌（LUAD），其组织学亚型与分级的术前预测模型目前仍有待探索。本研究旨在基于CT影像组学方法，构建IA期肺腺癌患者的无创病理分型及分级评估方案，并评估其在临床实践中的应用价值。

方法
回顾性收集两家医院内经病理确诊为IA期肺腺癌且接受根治性切除术患者的胸部CT扫描数据。首先采用深度学习分割算法辅助病灶勾画，后续进一步应用边界框标注等数据扩增策略。随后提取并筛选影像组学特征，基于四种经典机器学习算法构建影像组学模型，用于组织学亚型分类与分级分层。以受试者工作特征曲线下面积（AUC）评估模型性能。

结果
本研究分别纳入两家医院的294例和145例IA期肺腺癌患者用于影像组学分析。针对四种组织学亚型分类任务，多层感知机（MLP）算法对标注策略无明显偏好，在1像素扩增标注设置下，其在内部测试集、独立测试集与外部测试集上的平均AUC分别为0.855、0.922与0.720。采用边界框标注策略时，MLP在各测试集上也可获得可接受且稳定的分类精度。同时，本研究选择逻辑回归模型用于分级分层任务，在最优标注策略设置下，其在内部、独立与外部测试集上的平均AUC分别为0.928、0.837与0.748。

结论
基于深度学习增强的影像组学模型在CT影像基础上预测早期肺腺癌的精细组织学亚型与分级方面具有巨大潜力，有望成为早期肺腺癌诊断与临床管理的无创评估手段。