Jin Yu, Arimura Hidetaka, Cui YunHao, Kodama Takumi, Mizuno Shinichi, Ansai Satoshi
Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan.
Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan.
Cancers (Basel). 2023 Apr 10;15(8):2220. doi: 10.3390/cancers15082220.
This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training ( = 92) and testing ( = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves ( = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.
本研究旨在阐明一种基于计算机断层扫描（CT）图像的活检方法，并结合放射基因组特征来预测非小细胞肺癌（NSCLC）患者的同源结构域蛋白同源盒（HOPX）基因表达状态和预后。根据HOPX表达情况将患者标记为HOPX阴性或阳性，并分为训练数据集（n = 92）和测试数据集（n = 24）。在对116例患者通过Pyradiomics提取的基因与图像特征之间的相关性分析中，从1218个图像特征中选择了8个与HOPX表达相关的显著特征作为放射基因组特征候选者。使用最小绝对收缩和选择算子从8个候选者中构建最终特征。通过堆叠集成学习模型建立了具有放射基因组特征的影像活检模型，以预测HOPX表达状态和预后。在测试数据集中，该模型对HOPX表达具有预测能力，受试者工作特征曲线下面积为0.873，在Kaplan-Meier曲线中具有预后能力（P = 0.0066）。本研究结果表明，基于CT图像的活检结合放射基因组特征可帮助医生预测NSCLC患者的HOPX表达状态和预后。
