CT-Based Radiogenomic Prediction of ICAM1 and RAET1E as Biomarkers of NK Cytotoxicity in Clear Cell Renal Cell Carcinoma - Scorecard - MDSpire

CT-Based Radiogenomic Prediction of ICAM1 and RAET1E as Biomarkers of NK Cytotoxicity in Clear Cell Renal Cell Carcinoma

  • By

  • Ma, Xinwei

  • Yang, Jiao

  • Qian, Xusheng

  • Dou, Xin

  • Ji, Shiliang

  • Dai, Yakang

  • Yang, Yi

  • Wang, Yi

  • Zhu, Jianbing

  • May 20, 2026

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Clinical Scorecard: Radiogenomic Assessment Using CT Imaging to Identify ICAM1 and RAET1E as Indicators of NK Cell Cytotoxicity in Clear Cell Renal Cell Carcinoma

At a Glance

CategoryDetail
ConditionClear Cell Renal Cell Carcinoma (ccRCC)
Key MechanismsNK cell-mediated cytotoxicity pathways linked to imaging features
Target PopulationPatients with histologically confirmed ccRCC
Care SettingPreoperative assessment using CT imaging

Key Highlights

  • Development of a noninvasive CT-based radiogenomic framework
  • Identification of ICAM1 and RAET1E as biomarkers related to NK cell cytotoxicity
  • Significant association of biomarkers with tumor stage and survival outcomes
  • External validation achieved predictive accuracies of 76.92% for ICAM1 and 73.08% for RAET1E
  • 835 imaging-associated genes enriched in immune-related pathways identified

Guideline-Based Recommendations

Diagnosis

  • Utilize preoperative contrast-enhanced CT images for assessment

Management

  • Consider radiogenomic analysis for noninvasive immune characterization

Monitoring & Follow-up

  • Monitor expression levels of ICAM1 and RAET1E as indicators of tumor progression

Risks

  • Higher ICAM1 and lower RAET1E expression associated with advanced tumor stage and unfavorable survival outcomes

Patient & Prescribing Data

143 patients with ccRCC and 538 TCGA-KIRC tumor samples

Radiogenomic features may inform treatment strategies based on immune microenvironment

Clinical Best Practices

  • Employ L1-penalized support vector machine models for biomarker estimation
  • Validate findings with immunohistochemistry and external datasets
  • Integrate radiomic features into clinical decision-making processes

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