Gu Jingxiao, Bao Shanlei, Han Lu, Yu Xiao, Jia Zhongzheng, Huang Chen
Research Centre of Molecular Medicine, Nantong Health College of Jiangsu Province, Nantong, 226001, The People's Republic of China.
Department of Vascular Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, The People's Republic of China.
J Imaging Inform Med. 2025 May 12. doi: 10.1007/s10278-024-01381-7.
This study aims to assess the value of clinical and MRI radiomics features in noninvasively predicting programmed cell death protein 1 (PD-1) expression level and the response to anti-PD-1 immunotherapy combined with transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC). A total of 107 HCC patients (85 in training set and 22 in validation set) with PD-1 positive (n = 41) and negative (n = 66) were enrolled. Radiomics features were extracted from T2-weighted, fat suppression T2-weighted, contrast-enhanced, and diffusion-weighted images. A clinical model was constructed based on independent clinical risk factors (p < 0.05), while a radiomics model was developed utilizing the optimal radiomics signature. A radiomics nomogram model for predicting PD-1 integrated the significant clinical and radiomics features. The performance of nomogram was evaluated with respect to its discrimination and clinical utility and compared with that of clinical and radiomics prediction models. The radiomics nomogram, combining the clinical factors with the Rad-score, had best prediction performance (area under the curve [AUC]: 0.95 in the training set; AUC: 0.86 in the validation set). Decision curve analysis (DCA) showed that the nomogram exhibited superior accuracy in clinical assessment compared to the other models. The predicted high-risk group of PD-1 had longer overall survival (OS) than the predicted low-risk group after receiving anti-PD-1 therapy combined with TACE. The MRI-based radiomics nomogram performed well for identifying high-risk PD-1 group for combination therapy and may inform personalized treatment decision-making strategies.
本研究旨在评估临床和MRI影像组学特征在无创预测肝细胞癌（HCC）中程序性细胞死亡蛋白1（PD-1）表达水平以及抗PD-1免疫治疗联合经动脉化疗栓塞术（TACE）疗效方面的价值。共纳入107例PD-1阳性（n = 41）和阴性（n = 66）的HCC患者（训练集85例，验证集22例）。从T2加权、脂肪抑制T2加权、对比增强和扩散加权图像中提取影像组学特征。基于独立临床危险因素（p < 0.05）构建临床模型，同时利用最优影像组学特征构建影像组学模型。用于预测PD-1的影像组学列线图模型整合了显著的临床和影像组学特征。通过区分度和临床实用性评估列线图的性能，并与临床和影像组学预测模型进行比较。结合临床因素和Rad评分的影像组学列线图具有最佳预测性能（训练集中曲线下面积[AUC]：0.95；验证集中AUC：0.86）。决策曲线分析（DCA）表明，与其他模型相比，列线图在临床评估中表现出更高的准确性。接受抗PD-1治疗联合TACE后，预测的PD-1高风险组总生存期（OS）长于预测的低风险组。基于MRI的影像组学列线图在识别联合治疗的高风险PD-1组方面表现良好，可能为个性化治疗决策策略提供参考。
