NCT06410677

Changhai Multimodal Esophageal Cancer Cohort

Prediction of Immune Infiltration Level and Immunotherapy Efficacy of Esophageal Squamous Cell Carcinoma Based on Multimodal Deep Learning

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 110 (estimated)
• Sponsor: Wangluowei · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

The burden of esophageal squamous cell carcinoma (ESCC) in China is substantial, with 85% of the cancers being in the progressive stage. The treatment for advanced ESCC are extremely limited, and immunotherapy, represented by PD-1 inhibitors, has demonstrated a promising application potential. However, the effectiveness of PD-1 inhibitors varies significantly among patients with different types of ESCC, and currently, there is no effective method to predict the response to PD-1 inhibitors. In this study, investigators aim to construct a multimodal deep learning-based model to predict the level of immune infiltration and the efficacy of immunotherapy for ESCC, integrating both pathological image features and clinical information of patients with ESCC, thereby enhancing the level of individualized and precise treatment for ESCC.

Conditions

• Esophageal Squamous Cell Carcinoma

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	DNA Sequencing, RNA Sequencing	High-coverage Whole-Exome Sequencing sequencing of DNA samples from ESCC was performed. RNA expression was analyzed using the NanoString PanCancer Immuno-Oncology 360TM Panel that includes a set of more than 700 genes involved in the main biological pathways of human immunity. These experiments were performed by the Genomics platform of Institut Curie. Total RNAs were used as templates.

Timeline

• Start date: 2018-06-13
• Primary completion: 2022-10-21
• Completion: 2024-10-01
• First posted: 2024-05-13
• Last updated: 2024-05-13

Locations

1 site across 1 country: China