Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer

Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy. Postoperative survival stratification based on radiomics and deep learning may be useful for treatment decisions to improve prognosis. This study was aimed to develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC.

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Key information

Conditions

Bladder Cancer Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Male Urogenital Diseases Neoplasms Neoplasms by Site Urinary Bladder Diseases Urinary Bladder Neoplasms Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Urology, The First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, 400016, China

Location status: Recruiting

Location contact

Zongjie Wei

CONTACT

[email protected]

023-89012557

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

develop and validate a deep learning radiomics model based on preoperative enhanced CT image

Other

develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC

Primary outcomes

Overall survival（OS）

Time frame: up to 10 years

the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off.
Recurrence free survival（RFS)

Time frame: up to 10 years

the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored.

Study contacts

Contact information is provided by the study sponsor or research team.

Zongjie Wei

CONTACT

[email protected]

023-89012557

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital of Chongqing Medical University

Other

Registry information

Official study title

Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer

Important dates

Study start: 2023
Primary completion: 2025
Study completion: 2025
First posted: Oct 23, 2023
Registry last updated: May 31, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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