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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

256 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-04-01

Study Completion Date

2025-01-20

Brief Summary

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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).

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Detailed Description

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Conditions

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

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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TCGA

Intervention Type OTHER

SYSMH

Intervention Type OTHER

Interventions

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Intervention Type OTHER

Eligibility Criteria

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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

Eligible Sex

ALL

Accepts Healthy Volunteers

No