Performance of deep learning model and radiomics model for preoperative prediction of spread through air spaces in the surgically resected lung adenocarcinoma: a two-center comparative study

作者：Xiang Wang, Chao Ma, Qinling Jiang, Xuebin Zheng, Jun Xie, Chuan He, Pengfei Gu, Yanyan Wu, Yi Xiao, Shiyuan Liu · 发表于：Translational Lung Cancer Research · 年份：2024 · DOI：10.21037/tlcr-24-646 · 被引用次数：4 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Advanced X-ray and CT Imaging

Background: Spread through air spaces (STAS) in lung adenocarcinoma (LUAD) is a distinct pattern of intrapulmonary metastasis where tumor cells disseminate within the pulmonary parenchyma beyond the primary tumor margins. This phenomenon was officially included in the World Health Organization (WHO)'s classification of lung tumors in 2015. STAS is characterized by the spread of tumor cells in three forms: single cells, micropapillary clusters, and solid nests. Clinical studies have linked STAS to a poorer prognosis, higher recurrence risk, and more advanced clinicopathological staging in LUAD patients. In this study, we constructed radiomics models and deep learning models based on computed tomography (CT) for predicting preoperative STAS status in LUAD. Methods: A total of 395 (57.19±11.40 years old) patients with pathologically confirmed LUAD from two centers were enrolled in this retrospective study, in which STAS was detected in 146 patients (36.96%). The general clinical data, preoperative CT images, and the results of pathology reports of all patients were collected. Two experienced radiologists independently segmented the lesions by medical imaging interaction toolkit (MITK) software. The CT-based models only, the clinical-based models only, and the fusion model based on the two were constructed using radiomics and deep learning methods, respectively. The diagnostic performance of the different models was evaluated by comparing the area under the curve (AUC) of the sub...