Dou Zheng, Lin Jiaxi, Lu Chenghao, Ma Xiaoting, Zhang Ruoxu, Zhu Jinzhou, Qin Songbing, Xu Chao, Li Jinli
, Department of Oncology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, Wuxi 214002, China.
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.
BMC Gastroenterol. 2025 Aug 6;25(1):557. doi: 10.1186/s12876-025-04119-z.
To develop and validate a hybrid radiomics model to predict the overall survival in pancreatic cancer patients and identify risk factors that affect patient prognosis.
We conducted a retrospective analysis of 272 pancreatic cancer patients diagnosed at the First Affiliated Hospital of Soochow University from January 2013 to December 2023, and divided them into a training set and a test set at a ratio of 7:3. Pre-treatment contrast-enhanced computed tomography (CT), magnetic resonance imaging (MRI) images, and clinical features were collected. Dimensionality reduction was performed on the radiomics features using principal component analysis (PCA), and important features with non-zero coefficients were selected using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. In the training set, we built clinical prediction models using both random survival forests (RSF) and traditional Cox regression analysis. These models included a radiomics model based on contrast-enhanced CT, a radiomics model based on MRI, a clinical model, 3 bimodal models combining two types of features, and a multimodal model combining radiomics features with clinical features. Model performance evaluation in the test set was based on two dimensions: discrimination and calibration. In addition, risk stratification was performed in the test set based on predicted risk scores to evaluate the model's prognostic utility.
The RSF-based hybrid model performed best with a C-index of 0.807 and a Brier score of 0.101, outperforming the COX hybrid model (C-index of 0.726 and a Brier score of 0.145) and other unimodal and bimodal models. The SurvSHAP(t) plot highlighted CA125 as the most important variable. In the test set, patients were stratified into high- and low-risk groups based on the predicted risk scores, and Kaplan-Meier analysis demonstrated a significant survival difference between the two groups (p < 0.0001).
A multi-modal model using radiomics based on clinical tabular data and contrast-enhanced CT and MRI was developed by RSF, presenting strengths in predicting prognosis in pancreatic cancer patients.
开发并验证一种混合放射组学模型，以预测胰腺癌患者的总生存期，并识别影响患者预后的风险因素。
我们对2013年1月至2023年12月在苏州大学附属第一医院确诊的272例胰腺癌患者进行了回顾性分析，并以7:3的比例将他们分为训练集和测试集。收集治疗前的对比增强计算机断层扫描（CT）、磁共振成像（MRI）图像和临床特征。使用主成分分析（PCA）对放射组学特征进行降维，并使用具有10倍交叉验证的最小绝对收缩和选择算子（LASSO）选择非零系数的重要特征。在训练集中，我们使用随机生存森林（RSF）和传统的Cox回归分析建立临床预测模型。这些模型包括基于对比增强CT的放射组学模型、基于MRI的放射组学模型、临床模型、3种结合两种类型特征的双峰模型以及结合放射组学特征与临床特征的多模态模型。测试集中的模型性能评估基于两个维度：区分度和校准度。此外，基于预测风险评分在测试集中进行风险分层，以评估模型的预后效用。
基于RSF的混合模型表现最佳，C指数为0.
