Tong Yahan, Hu Can, Cen Xiaoping, Chen Haiyan, Han Zhe, Xu Zhiyuan, Shi Liang
Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, P.R. China.
Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang, Hangzhou, Zhejiang 310022, P.R. China.
Mol Clin Oncol. 2024 Oct 21;21(6):96. doi: 10.3892/mco.2024.2794. eCollection 2024 Dec.
The objective of the present study was to build and validate a radio-clinical model integrating radiological features and clinical characteristics based on information available before surgery for prediction of microvascular invasion (MI) in gastric cancer. The retrospective study included a cohort of 534 patients (n=374 for the training set and n=160 for the test set) who were diagnosed with gastric cancer. All patients underwent contrast-enhanced computed tomography within one month before surgery. The focal area was mapped by ITK-SNAP. Radiomics features were extracted from portal venous phase CT images. Principal component analysis was used to reduce dimensionality, maximum relevance minimum redundancy, and least absolute shrinkage and selection operator to screen features most associated with MI. The radiomics signature was subsequently computed based on the coefficient weight assigned to it. The independent risk factors for MI of gastric cancer were determined using univariate analysis and multivariate logistic regression analysis. Univariate logistic regression analysis was used to assess the association between clinical characteristics and MI status. A radio-clinical model was constructed by employing multi-variable logistic regression analysis, incorporating radiomic features with clinical characteristics. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curves were employed for the analysis and evaluation of the model's performance. The radiomics signature model had moderate recognition ability, with an area under ROC curve (AUC) of 0.77 for the training set and 0.73 for the test set. The radio-clinical model, consisting of rad-score and clinical features, could well discriminate the training set and test set (AUC=0.88 and 0.80, respectively). The calibration curves and DCA further validated the favorable fit and clinical applicability of the radio-clinical model. In conclusion, the radio-clinical model combining the radiomics signature and clinical characteristics may be used to individually predict MI in gastric cancer to aid in the development of a clinical treatment strategy.
本研究的目的是基于术前可用信息构建并验证一个整合放射学特征和临床特征的放射临床模型，用于预测胃癌的微血管侵犯（MI）。这项回顾性研究纳入了534例被诊断为胃癌的患者队列（训练集n = 374例，测试集n = 160例）。所有患者在手术前1个月内接受了对比增强计算机断层扫描。通过ITK-SNAP对病灶区域进行标记。从门静脉期CT图像中提取放射组学特征。采用主成分分析进行降维，最大相关最小冗余法以及最小绝对收缩和选择算子法来筛选与MI最相关的特征。随后根据赋予的系数权重计算放射组学特征标签。使用单因素分析和多因素逻辑回归分析确定胃癌MI的独立危险因素。单因素逻辑回归分析用于评估临床特征与MI状态之间的关联。通过多变量逻辑回归分析构建放射临床模型，将放射组学特征与临床特征相结合。采用受试者工作特征（ROC）曲线分析、决策曲线分析（DCA）和校准曲线对模型性能进行分析和评估。放射组学特征标签模型具有中等识别能力，训练集的ROC曲线下面积（AUC）为0.77，测试集为0.73。由放射学评分和临床特征组成的放射临床模型能够很好地区分训练集和测试集（AUC分别为0.88和0.80）。校准曲线和DCA进一步验证了放射临床模型的良好拟合度和临床适用性。总之，结合放射组学特征标签和临床特征的放射临床模型可用于个体预测胃癌的MI，以辅助制定临床治疗策略。
