AI-Based Multimodal Multi-tasks Analysis Reveals Tumor Molecular Heterogeneity, Predicts Preoperative Lymph Node Metastasis and Prognosis in Papillary Thyroid Carcinoma: A Retrospective Study
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Enrollment
- 256
- Locations
- 1
- Primary Endpoint
- Lymph node metastasis
Study Overview
Brief 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).
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Cross Sectional
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
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
Outcomes
Primary Outcomes
Lymph node metastasis
Time Frame: 2021.10.1
Lymph node metastasis is frequently influenced by a myriad of factors, and tumor heterogeneity stands out as a significant contributing element.
Secondary Outcomes
- Disease-free survival(2021.10.1)
Investigators
Yunfang Yu
Dr.
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University