Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Asia Pac J Clin Oncol. 2024 Dec;20(6):771-778. doi: 10.1111/ajco.14099. Epub 2024 Jul 1.
This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.
We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).
The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).
The prediction model based on radiomics and genomics features has a good prediction performance for STAS.
本研究旨在基于影像学和基因组特征建立早期非小细胞肺癌气腔内播散（STAS）的预测模型。
我们回顾性收集了 204 例（47 例 STAS+和 157 例 STAS-）在南京金陵医院接受手术治疗的非小细胞肺癌患者。收集了他们的术前 CT 图像、基因检测数据（包括其他医院的下一代测序数据）和临床数据。患者被随机分为训练和测试队列（7:3）。
本研究共纳入 204 例符合条件的患者。47 例（23.0%）患者存在 STAS，157 例（77.0%）患者不存在 STAS。受试者工作特征曲线显示，基于放射组学和基因组学特征的模型、临床基因组学模型和混合模型具有良好的预测性能（曲线下面积 [AUC] = 0.85；AUC = 0.70；AUC = 0.85）。
基于放射组学和基因组学特征的预测模型对 STAS 具有良好的预测性能。
