{
  "abstract": "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.",
  "authors": [
    {
      "affiliations": [
        "Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China"
      ],
      "name": "Jia Fu"
    },
    {
      "affiliations": [
        "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China",
        "School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing, China"
      ],
      "name": "Xiaoying Huang"
    },
    {
      "affiliations": [
        "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China"
      ],
      "name": "Mengjie Fang"
    },
    {
      "affiliations": [
        "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China",
        "School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing, China"
      ],
      "name": "Xin Feng"
    },
    {
      "affiliations": [
        "State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"
      ],
      "name": "Xu-Yao Zhang"
    },
    {
      "affiliations": [
        "Kiang Wu Hospital, Macau, China"
      ],
      "name": "Xuebin Xie"
    },
    {
      "affiliations": [
        "Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China"
      ],
      "name": "Zhuozhao Zheng"
    },
    {
      "affiliations": [
        "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China",
        "School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing, China",
        "National Key Laboratory of Kidney Diseases, Beijing, China"
      ],
      "name": "Di Dong"
    }
  ],
  "title": "Artificial intelligence in medical imaging empowers precision neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma",
  "uid": "273dd3ed-4ee4-50e3-811e-6acb4ff59038"
}