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

Sun Yat-sen University

Status: Enrolling

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

Study type: Observational

Funder types: Other

Identifiers

NCT06241092

SYSEC-KY-KS-2021-259

Details and patient eligibility

About

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).

Enrollment: 256 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

≥ 18 years of age Diagnosis of Papillary thyroid carcinoma at least one months before trial Willing to return for required follow-up (posttest) visits

Exclusion criteria

The patient requires valve or other likely surgery The patient is unable to carry out any physical activity without discomfort The patient had thyroid ache within three months prior to enrollment The patient refuses to give informed consent The patient is a candidate for coronary bypass surgery or something similar

Trial design

256 participants in 2 patient groups

TCGA

SYSMH