NCT06241092

AI-Based Multimodal Multi-tasks Analysis Reveals Tumor Molecular Heterogeneity, Predicts Preoperative Lymph Node Metastasis and Prognosis in Papillary Thyroid Carcinoma

AI-Based Multimodal Multi-tasks Analysis Reveals Tumor Molecular Heterogeneity, Predicts Preoperative Lymph Node Metastasis and Prognosis in Papillary Thyroid Carcinoma: A Retrospective Study

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 256 (estimated)
• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

This study involved a comprehensive analysis of 256 PTC patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH) and 499 patients from The Cancer Genome Atlas. DNA-based next-generation sequencing (NGS) and single-cell RNA sequencing (scRNA-seq) were employed to capture genetic alterations and TME heterogeneity. A deep learning multimodal model was developed by incorporating matched histopathology slide images, genomic, transcriptomic, immune cells data to predict LNM and disease-free survival (DFS).

Conditions

• Papillary Thyroid Carcinoma; Molecular Heterogeneity; Multi-model Analysis; Artificial Intelligence; Lymph Node Metastases; Disease-free Survival

Interventions

Type	Name	Description
OTHER	—

Timeline

• Start date: 2024-04-01
• Primary completion: 2024-09-20
• Completion: 2025-01-20
• First posted: 2024-02-05
• Last updated: 2024-02-05

Locations

1 site across 1 country: China