DataSheet_1_A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma.pdf

ObjectiveTo explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data.
MethodsA retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted. Patients were randomly divided into training (n = 73) and validation (n = 30) groups. A total of 1,217 radiomics features were extracted from regions of interest on CT and MR images of each patient. Univariate Cox regression, Spearman’s correlation analysis, Pearson’s correlation analysis, and least absolute shrinkage and selection operator Cox analysis were used for feature selection in the training set, multivariate Cox proportional risk models were established to predict disease-free survival (DFS) and overall survival (OS), and the models were validated using validation cohort data. Multimodal radiomics scores, integrating CT and MRI data, were applied, together with clinical risk factors, to construct nomograms for individualized survival assessment, and calibration curves were used to evaluate model consistency. Harrell’s concordance index (C-index) values were calculated to evaluate the prediction performance of the models.
ResultsThe radiomics score established using CT and MR data was an independent predictor of prognosis (DFS and OS) in patients with HCC (p < 0.05). Prediction models illustrated by nomograms for predicting prognosis in liver cancer were established. Integrated CT and MRI and clinical multimodal data had the best predictive performance in the training and validation cohorts for both DFS [(C-index (95% CI): 0.858 (0.811–0.905) and 0.704 (0.563–0.845), respectively)] and OS [C-index (95% CI): 0.893 (0.846–0.940) and 0.738 (0.575–0.901), respectively]. The calibration curve showed that the multimodal radiomics model provides greater clinical benefits.
ConclusionMultimodal (MRI/CT) radiomics models can serve as effective visual tools for predicting prognosis in patients with liver cancer. This approach has great potential to improve treatment decisions when applied for preoperative prediction in patients with HCC.

研究目的：探索基于磁共振成像（MRI）与计算机断层扫描（CT）影像数据预测肝癌预后的新型模型。
研究方法：本研究为回顾性研究，纳入103例经组织学证实的肝细胞癌（hepatocellular carcinoma, HCC）患者。将患者随机分为训练集（n = 73）与验证集（n = 30）。从每位患者的CT与MR图像感兴趣区中，共提取1217个放射组学特征（radiomics features）。在训练集中，采用单因素Cox回归、斯皮尔曼相关分析、皮尔逊相关分析及最小绝对收缩和选择算子Cox分析进行特征筛选，构建多变量Cox比例风险模型以预测无病生存期（disease-free survival, DFS）与总生存期（overall survival, OS），并使用验证队列数据对模型进行验证。整合CT与MRI数据的多模态放射组学评分，联合临床危险因素构建用于个体化生存评估的列线图（nomograms），并采用校准曲线评估模型一致性。计算Harrell一致性指数（Harrell’s concordance index, C-index）以评价模型的预测性能。
研究结果：基于CT与MR数据构建的放射组学评分可作为HCC患者预后（DFS与OS）的独立预测因子（p < 0.05）。本研究构建了用于肝癌预后预测的列线图可视化模型。整合CT、MRI及临床多模态数据的模型，在训练集与验证队列中对DFS[一致性指数（95%置信区间：0.858（0.811–0.905）与0.704（0.563–0.845）]及OS[一致性指数（95%置信区间：0.893（0.846–0.940）与0.738（0.575–0.901）]均展现出最优的预测性能。校准曲线结果显示，多模态放射组学模型可带来更显著的临床获益。
研究结论：多模态（MRI/CT）放射组学模型可作为预测肝癌患者预后的有效可视化工具。该方法应用于肝细胞癌患者术前预测时，在优化治疗决策方面具有巨大潜力。