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

Recruiting
years of age
Both
Phase N/A

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Overview

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).

Eligibility

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

Study details

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

NCT06241092

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

14 February 2024

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