AI-Based Imaging Model for Bladder Cancer Prediction

Development and Validation of an Image-Based Artificial Intelligence Predictive Model for Bladder Cancer

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.

Study Overview

• Status: Recruiting
• Conditions: Develop a CT-based Tumor Budding Predictive Model for Bladder Cancer Using Deep Learning Algorithms
• Intervention / Treatment: Other: No specific interventions.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Yun Luo, Dr.; Phone Number: 13560189936; Email: luoyun8@mail.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510630
      • Recruiting
      • The Third Affiliated Hospital of Sun Yat-Sen University
      • Contact: Yun Luo, Dr.; Phone Number: 13560189936; Email: luoyun8@mail.sysu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

patients with bladder cancer

Description

Inclusion Criteria:

1. Bladder cancer patients treated from January 1, 2014, to January 1, 2023;
2. Hospitalized and underwent transurethral resection of bladder tumor (TURBT) or radical cystectomy;
3. Complete clinical, preoperative CT, and pathological data.

Exclusion Criteria:

1. Patients who previously underwent surgical treatment for bladder cancer at other centers, making it difficult to obtain their preoperative data;
2. Patients with other concurrent pelvic or urinary system malignancies;
3. Patients with poor quality, low resolution, or faded CT or pathological images.

Study Plan

How is the study designed?

Number of groups / cohorts

6

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
training cohort; for training the model, from Sun Yat-sen Memorial Hospital of Sun Yat-sen University.	Other: No specific interventions.; No specific interventions.
internal validation cohort; used to evaluate the model's performance, is from Sun Yat-sen Memorial Hospital of Sun Yat-sen University.	Other: No specific interventions.; No specific interventions.
external validation cohort 1; used to evaluate the model's performance, is from the Third Affiliated Hospital of Sun Yat-sen University.	Other: No specific interventions.; No specific interventions.
external validation cohort 2; used to evaluate the model's performance, is from the Second Affiliated Hospital of Dalian Medical University.	Other: No specific interventions.; No specific interventions.
external validation cohort 3; used to evaluate the model's performance, is from the First Affiliated Hospital of Chongqing Medical University.	Other: No specific interventions.; No specific interventions.
external validation cohort 4; used to evaluate the model's performance, is from the Yan'an Hospital Affiliated to Kunming Medical University.	Other: No specific interventions.; No specific interventions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Tumor Budding: An Overview	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.	One year after being discharged following surgery

Collaborators and Investigators

• Sponsor: Third Affiliated Hospital, Sun Yat-Sen University

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2024
• Primary Completion (Estimated): October 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: May 30, 2024
• First Submitted That Met QC Criteria: May 30, 2024
• First Posted (Estimated): June 4, 2024

Study Record Updates

• Last Update Posted (Estimated): June 4, 2024
• Last Update Submitted That Met QC Criteria: May 30, 2024
• Last Verified: May 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Urologic Diseases; Urinary Bladder Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Urinary Bladder Neoplasms
• Other Study ID Numbers: SL-II2023-303-02

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No