AI-Based Imaging Model for Bladder Cancer Prediction
- Conditions
- Develop a CT-based Tumor Budding Predictive Model for Bladder Cancer Using Deep Learning Algorithms
- Registration Number
- NCT06442839
- Lead Sponsor
- Third Affiliated Hospital, Sun Yat-Sen University
- Brief Summary
Bladder cancer is the ninth most common malignant tumor worldwide, characterized by high malignancy and poor prognosis. We intend to develop a CT-based tumor budding predictive model for bladder cancer using deep learning algorithms. This model will facilitate preoperative assessment of patient conditions, enabling the formulation of more precise and personalized treatment plans.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 2000
- Bladder cancer patients treated from January 1, 2014, to January 1, 2023;
- Hospitalized and underwent transurethral resection of bladder tumor (TURBT) or radical cystectomy;
- Complete clinical, preoperative CT, and pathological data.
- Patients who previously underwent surgical treatment for bladder cancer at other centers, making it difficult to obtain their preoperative data;
- Patients with other concurrent pelvic or urinary system malignancies;
- Patients with poor quality, low resolution, or faded CT or pathological images.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Tumor Budding: An Overview One year after being discharged following surgery Tumor budding is a histopathological phenomenon observed in various types of cancer, including bladder cancer. It refers to the presence of single cells or small clusters of cells (less than five) at the invasive front of tumors. These buds are indicative of an epithelial-mesenchymal transition (EMT), a process where epithelial cells acquire mesenchymal, invasive characteristics, which is crucial for cancer invasion and metastasis.
- Secondary Outcome Measures
Name Time Method
Related Research Topics
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Trial Locations
- Locations (1)
the Third Affiliated Hospital of Sun Yat-sen University
🇨🇳Guangzhou, Guangdong, China
the Third Affiliated Hospital of Sun Yat-sen University🇨🇳Guangzhou, Guangdong, ChinaYun Luo, Dr.Contact13560189936luoyun8@mail.sysu.edu.cn