Predicting disruptive instabilities in controlled fusion plasmas through deep learning.
Julian Kates-Harbeck, Alexey Svyatkovskiy, William Tang
https://pubmed.ncbi.nlm.nih.gov/30996348/Actively Recruiting
Led by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Updated on 2024-02-29
4000
Participants Needed
4
Research Sites
N/A
Total Duration
Breast phyllodes tumor PT is a rare type of breast tumor classified into benign, borderline, and malignant categories based on specific tissue features. Malignant PTs have a high chance of coming back locally and spreading to other parts of the body. Early and accurate diagnosis along with identifying treatment targets is important to improve patient outcomes. This research focuses on using artificial intelligence AI to combine clinical, imaging, and genetic data to help diagnose and predict the prognosis of breast PT. The study collects high-quality data from nearly a thousand patients with breast PT, including various medical images such as ultrasound, mammography, CT, and MRI, along with tissue gene sequencing. Researchers aim to build a detailed multi-omics database and develop an AI-based system that can support early diagnosis and predict how the tumor may progress. This system is designed to assist personalized treatment decisions and address care differences across regions. Participants diagnosed with breast PT will contribute their imaging and tissue data. Researchers will assess outcomes like diagnostic sensitivity, false-negative and false-positive rates, and accuracy over five years using statistical measures such as the receiver operating characteristic curve. The study involves no treatment interventions but focuses on observation and data analysis to improve diagnostic tools. The research is sponsored by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University and started in March 2023, with an expected completion by the end of 2027.
CONDITIONS
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors
Y
Yan Nie, Prof.Dr.
Explore thousands of other clinical trials that might be a better match.
Sign up to get personalized trial recommendations delivered to your inbox.
Already have an account? Log in here
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead Sponsor
Sun Yat-sen University
Collaborating Sponsor
You may qualify if you...
You will not qualify if you...
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Duration - Up to 5 years
Participants undergo medical imaging procedures including ultrasound, mammography, CT, and MRI to assist in diagnosis and prognosis of breast phyllodes tumors.
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
Have more questions? Get in touch with our team for quick support
Julian Kates-Harbeck, Alexey Svyatkovskiy, William Tang
https://pubmed.ncbi.nlm.nih.gov/30996348/Y Wang, B Acs, S Robertson...
https://pubmed.ncbi.nlm.nih.gov/34756513/Yazid Belkacémi, Guilhem Bousquet, Hugo Marsiglia...
https://pubmed.ncbi.nlm.nih.gov/17931796/Zi Long Chow, Aye Aye Thike, Hui Hua Li...
https://pubmed.ncbi.nlm.nih.gov/32150458/Shashi Prakash Mishra, Satyendra Kumar Tiwary, Manjaree Mishra...
https://pubmed.ncbi.nlm.nih.gov/23577269/Yan Nie, Jianing Chen, Di Huang...
https://pubmed.ncbi.nlm.nih.gov/28512246/Chee Leong Cheng, Nur Diyana Md Nasir, Gary Jian Zhe Ng...
https://pubmed.ncbi.nlm.nih.gov/34819630/Chang Gong, Yan Nie, Shaohua Qu...
https://pubmed.ncbi.nlm.nih.gov/24980553/Jeroen van der Laak, Geert Litjens, Francesco Ciompi
https://pubmed.ncbi.nlm.nih.gov/33990804/Kaustav Bera, Kurt A Schalper, David L Rimm...
https://pubmed.ncbi.nlm.nih.gov/31399699/Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead Sponsor
Sun Yat-sen University
Collaborating Sponsor