Fu Jia, Huang Xiaoying, Fang Mengjie, Feng Xin, Zhang Xu-Yao, Xie Xuebin, Zheng Zhuozhao, Dong Di
Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
J Immunother Cancer. 2025 Sep 9;13(9):e012468. doi: 10.1136/jitc-2025-012468.
Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility. In recent years, the application of artificial intelligence (AI) in medical imaging has expanded rapidly. By incorporating voxel-level feature maps, the combination of radiomics and deep learning enables the extraction of rich textural, morphological, and microstructural features, while autonomously learning high-level abstract representations from clinical CT images, thereby revealing biological heterogeneity that is often imperceptible to conventional assessments. Leveraging these high-dimensional representations, AI models can provide more accurate predictions of nICT response. Future advancements in foundation models, multimodal integration, and dynamic temporal modeling are expected to further enhance the generalizability and clinical applicability of AI. AI-powered medical imaging is poised to support all stages of perioperative management in ESCC, playing a pivotal role in high-risk patient identification, dynamic monitoring of therapeutic response, and individualized treatment adjustment, thereby comprehensively advancing precision nICT.
新辅助免疫化疗（nICT）已显示出在提高局部晚期食管鳞状细胞癌（ESCC）患者的病理缓解率和生存结果方面具有巨大潜力。然而，治疗结果存在显著的个体间差异，这凸显了迫切需要更精确的预测工具来指导临床决策。传统生物标志物在预测性能和临床可行性方面仍然有限。近年来，人工智能（AI）在医学成像中的应用迅速扩展。通过纳入体素级特征图，放射组学和深度学习的结合能够提取丰富的纹理、形态和微观结构特征，同时从临床CT图像中自主学习高级抽象表示，从而揭示传统评估通常难以察觉的生物学异质性。利用这些高维表示，AI模型可以对nICT反应提供更准确的预测。基础模型、多模态整合和动态时间建模的未来进展有望进一步提高AI的通用性和临床适用性。人工智能驱动的医学成像有望支持ESCC围手术期管理的各个阶段，在高危患者识别、治疗反应的动态监测和个体化治疗调整中发挥关键作用，从而全面推进精准nICT。
