{
  "abstract": "Background Neoadjuvant immunotherapy significantly improves the pathological complete response (pCR) rate in colorectal cancer (CRC). However, the lack of reliable tools to accurately identify responders remains a key barrier to its widespread clinical adoption. This study aimed to develop an interpretable radiomics model guided by immunophenotypes to predict response to preoperative immunotherapy in CRC, with the goal of enabling more precise and personalized treatment strategies.Methods First, we retrospectively collected 108 patients with CRC from the center who underwent preoperative CT and RNA sequencing. Immunophenotypes were characterized through unsupervised clustering of tumor-infiltrating immune cells at RNA level, with subsequent validation by CD3 and CD8 spatial distributions at immunohistochemical (IHC) level. Furthermore, patients from center II (n=19) and center III (n=22) receiving neoadjuvant immunotherapy were assigned to training and validation cohorts. Through a two-stage selection process, immunophenotype-associated radiomic features were initially identified, followed by immunotherapy response-related radiomics features that were further identified. Eventually, an interpretable immunotherapy response prediction model was developed by integrating a decision tree algorithm with SHapley Additive exPlanations (SHAP) analysis.Results Two immunophenotypes (immune-hot and immune-cold) were identified, compared with the immune-cold, the former exhibited more abundant RNA-based immune cell infiltration and higher densities of CD3 and CD8 T cells in both the core tumor and invasive margin areas. A decision tree model integrating three key radiomic features achieved an area under the curve of 0.904 (95% CI: 0.679 to 1.000) in an independent validation cohort. SHAP analysis identified higher large dependence emphasis and lower variance as potential predictors of pCR, which quantified the homogeneity of the tumors.Conclusion Two immunophenotypes were constructed and identified at both bulk RNA level and IHC level. An interpretable and accurate radiomics model was constructed to guide personalized immunotherapy strategies in clinical practice.",
  "authors": [
    {
      "affiliations": [
        "Department of Radiology, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China",
        "Faculty of Life science and Technology, Kunming University of Science and Technology, Kunming, Yunnan, China",
        "The Afiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China"
      ],
      "name": "Yiwen Zhang"
    },
    {
      "affiliations": [
        "Department of Colorectal surgery, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China"
      ],
      "name": "Xuan Zhang"
    },
    {
      "affiliations": [
        "Department of Medical Imaging, Guangzhou Medical University Affiliated Cancer Hospital, Guangzhou, Guangdong, China"
      ],
      "name": "Xi Zhong"
    },
    {
      "affiliations": [
        "Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong, China"
      ],
      "name": "Liebin Huang"
    },
    {
      "affiliations": [
        "Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Guangzhou, Guangdong, China",
        "Guangdong Provincial Key Laboratory of Artificial Inteligence in Medical lmage Analysis and Application, Guangzhou, Guangdong, China"
      ],
      "name": "Wenjing Jiang"
    },
    {
      "affiliations": [
        "Department of Radiology, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China"
      ],
      "name": "Caixia Zhang"
    },
    {
      "affiliations": [
        "Department of Radiology, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China"
      ],
      "name": "Lizhu Liu"
    },
    {
      "affiliations": [
        "Department of Radiology, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China"
      ],
      "name": "Ruimin You"
    },
    {
      "affiliations": [
        "Department of Radiology, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China"
      ],
      "name": "Yanli Li"
    },
    {
      "affiliations": [
        "Department of Radiology, Central South University, Changsha, Hunan, China"
      ],
      "name": "Xiaoping Yi"
    },
    {
      "affiliations": [
        "Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Guangzhou, Guangdong, China",
        "Guangdong Provincial Key Laboratory of Artificial Inteligence in Medical lmage Analysis and Application, Guangzhou, Guangdong, China"
      ],
      "name": "Zaiyi Liu"
    },
    {
      "affiliations": [
        "Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Guangzhou, Guangdong, China",
        "Guangdong Provincial Key Laboratory of Artificial Inteligence in Medical lmage Analysis and Application, Guangzhou, Guangdong, China",
        "Guangdong Cardiovascular institute, Guangdong Provincial People's Hospital, Guangdong Academy of MedicalSciences, Guangzhou, Guangdong, China"
      ],
      "name": "Xiaobo Chen"
    },
    {
      "affiliations": [
        "Department of Radiology, The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital，Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China"
      ],
      "name": "Zhenhui Li"
    }
  ],
  "title": "Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III–IV d-MMR/MSI-H colorectal cancer",
  "uid": "31db889a-03a7-5aff-bead-8b8250d24e66"
}