Ling Tong, Zuo Zhichao, Wu Liucheng, Ma Jie, Wang Tingan, Huang Mingwei
Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Guangxi, China.
School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, China.
Digit Health. 2025 May 8;11:20552076251341740. doi: 10.1177/20552076251341740. eCollection 2025 Jan-Dec.
Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aiming to reduce tumor burden, enhance resection rates, and improve clinical outcomes. Due to variability in patient responses, the objective of this study was to enhance the prediction of NAC tumor regression grade (TRG) in patients with LAGC by integrating radiomic features with clinical biomarkers through machine learning (ML) approaches.
We analyzed a cohort of 255 patients with LAGC who underwent NAC prior to surgical resection at the Affiliated Cancer Hospital of Guangxi Medical University. Among these patients, 57 (22.4%) were classified as responders (TRG 0-1), and 198 (77.6%) were identified as non-responders (TRG 2-3). The cohort was divided into a training set (n = 178) and a validation set (n = 77) in a 7:3 ratio. Pre-treatment portal venous-phase computed tomography scans were used to extract 1130 radiomic features via the OnekeyAI platform software. Through feature engineering, we generated a radiomics score (rad score) by linearly combining these features. A variety of ML algorithms were applied to integrate the rad score with clinical biomarkers, resulting in the construction of a hybrid model. The model's diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC).
Among the ML models tested, the random forest (RF) model performed best when both the rad score and clinical biomarkers were used as input features, leading to our hybrid model development. This hybrid model (AUC = 0.814) outperformed the radiomics (AUC = 0.755) and clinical (AUC = 0.682) models.
A RF-based hybrid model was developed by integrating radiomics and clinical biomarkers to predict NAC response in patients with LAGC undergoing surgical resection, providing personalized treatment insights.
新辅助化疗（NAC）是治疗局部晚期胃癌（LAGC）的一种有前景的治疗策略，旨在减轻肿瘤负荷、提高切除率并改善临床结局。由于患者反应存在差异，本研究的目的是通过机器学习（ML）方法将放射组学特征与临床生物标志物相结合，提高对LAGC患者NAC肿瘤退缩分级（TRG）的预测。
我们分析了广西医科大学附属肿瘤医院255例接受手术切除前NAC的LAGC患者队列。在这些患者中，57例（22.4%）被分类为反应者（TRG 0 - 1），198例（77.6%）被确定为无反应者（TRG 2 - 3）。该队列以7:3的比例分为训练集（n = 178）和验证集（n = 77）。使用治疗前门静脉期计算机断层扫描通过OnekeyAI平台软件提取1130个放射组学特征。通过特征工程，我们通过线性组合这些特征生成了一个放射组学评分（rad评分）。应用多种ML算法将rad评分与临床生物标志物相结合，构建了一个混合模型。使用受试者工作特征曲线和曲线下面积（AUC）评估模型的诊断性能。
在测试的ML模型中，当rad评分和临床生物标志物都用作输入特征时，随机森林（RF）模型表现最佳，从而开发了我们的混合模型。该混合模型（AUC = 0.814）优于放射组学模型（AUC = 0.755）和临床模型（AUC = 0.682）。
通过整合放射组学和临床生物标志物开发了一种基于RF的混合模型，以预测接受手术切除的LAGC患者的NAC反应，提供个性化治疗见解。
