A5023559474- Thyroid Cancer Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- Bioinformatics and Genomic Networks
- Global Cancer Incidence and Screening
- Biomedical Text Mining and Ontologies
- Histiocytic Disorders and Treatments
- Parvovirus B19 Infection Studies
- Artificial Intelligence in Healthcare and Education
Peking University
2024
Peking University Cancer Hospital
2024
Kunming Medical University
2024
Peking Union Medical College Hospital
2022
Chinese Academy of Medical Sciences & Peking Union Medical College
2022
The survival rate of patients with distant metastasis (DM) papillary thyroid carcinoma (PTC) is significantly reduced. It great significance to find an effective method for early prediction the risk DM formulating individualized diagnosis and treatment plans improving prognosis. Previous studies have significant limitations, it still necessary develop new models predicting PTC. We aimed validate interpretable machine learning (ML) in PTC using a multicenter cohort. collected data on who were...
The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis DTC (LMDTC). This population poses challenge clinical practice, necessitating the urgent development effective risk stratification methods and predictive tools for metastasis.
<div>AbstractPurpose:<p>The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis DTC (LMDTC). This population poses challenge clinical practice, necessitating the urgent development effective risk stratification methods and predictive tools for metastasis.</p>Experimental Design:<p>Through proteomic analysis large samples primary lesion dual validation employing parallel...
<div>AbstractPurpose:<p>The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis DTC (LMDTC). This population poses challenge clinical practice, necessitating the urgent development effective risk stratification methods and predictive tools for metastasis.</p>Experimental Design:<p>Through proteomic analysis large samples primary lesion dual validation employing parallel...
<p>Figure S2. Principal component analysis of patients in LMDTC (M) group and NMDTC (NM) group.</p>
<p>Figure S4. WGCNA analysis of the proteomics data.</p>
<p>Figure S4. WGCNA analysis of the proteomics data.</p>
<p>Figure S3. Unsupervised clustering analysis of all samples using the expression profiles differentially expressed proteins.</p>
<p>Figure S3. Unsupervised clustering analysis of all samples using the expression profiles differentially expressed proteins.</p>
<p>Figure S5. Protein expression categorized into 4 levels.</p>
<p>Figure S1. Statistical chart of protein identification results.</p>
<p>Figure S6. Specific performance of the 3 risk prediction models for lung metastasis in DTC.</p>
<p>Figure S2. Principal component analysis of patients in LMDTC (M) group and NMDTC (NM) group.</p>
<p>KEGG Analysis Table of Differential Proteins</p>
<p>Figure S1. Statistical chart of protein identification results.</p>