Predicting outcomes after bladder cancer surgery using deep learning
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
First Affiliated Hospital of Chongqing Medical University · NCT06092450
This study is testing a new way to use CT scan images to see if it can help predict how well patients with muscle invasive bladder cancer will do after surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 500 (estimated) |
| Sex | All |
| Sponsor | First Affiliated Hospital of Chongqing Medical University (other) |
| Locations | 1 site (Chongqing, Chongqing Municipality) |
| Trial ID | NCT06092450 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop and validate a deep learning radiomics model that utilizes preoperative enhanced CT images to predict postoperative survival outcomes in patients with muscle invasive bladder cancer (MIBC) following radical cystectomy. By analyzing radiomic features from CT scans, the study seeks to improve treatment decision-making and patient prognosis. Eligible participants include those with confirmed MIBC who have undergone a recent contrast-CT scan and have complete clinical data. The study excludes patients who have received neoadjuvant therapy or have poor-quality imaging.
Who should consider this trial
Good fit: Ideal candidates for this study are patients with pathologically confirmed muscle invasive bladder cancer who are scheduled for radical cystectomy.
Not a fit: Patients who have received neoadjuvant therapy or have incomplete clinical data may not benefit from this study.
Why it matters
Potential benefit: If successful, this model could significantly enhance the ability to predict survival outcomes for bladder cancer patients, leading to more personalized treatment strategies.
How similar studies have performed: While the use of deep learning in radiomics is an emerging field, similar studies have shown promise in predicting outcomes in various cancers, suggesting potential for success in this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * patients with pathologically confirmed MIBC after radical cystectomy; * contrast-CT scan less than two weeks before surgery; * complete CT image data and clinical data. Exclusion Criteria: * patients who received neoadjuvant therapy; * non-urothelial carcinoma; * poor quality of CT images; * incomplete clinical and follow-up data.
Where this trial is running
Chongqing, Chongqing Municipality
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University — Chongqing, Chongqing Municipality, China (RECRUITING)
Study contacts
- Study coordinator: Zongjie Wei
- Email: wzj9846@163.com
- Phone: 023-89012557
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Bladder Cancer, Tomography, X-ray computed, Muscle-invasive bladder cancer, Radiomics, Deep Learning