Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma

Upper Tract Urothelial Carcinoma (UTUC), characterized by its anatomical complexity and often aggressive clinical behavior, presents substantial difficulties in accurate diagnosis and reliable prognostication. The stratification of postoperative survival utilizing radiomics features derived from imaging and characteristics from whole slide images could prove instrumental in guiding therapeutic decisions to enhance patient outcomes. In this research, our objective is to construct a deep learning-based prognostic-stratification system designed for the automated prediction of overall and cancer-specific survival in individuals diagnosed with UTUC.

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Key information

Conditions

UTUC

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Urology, The First Affiliated Hospital of Chongqing Medical University, chongqing, chongqing 400016 Recruiting

Chongqing, 400016, China

About this study

Upper Tract Urothelial Carcinoma (UTUC) can be challenging to accurately diagnose and its course difficult to predict, as the disease manifestations and aggressiveness can differ significantly among individuals. This research seeks to create an innovative system employing artificial intelligence to process patient data, encompassing images from diagnostic scans and surgical pathology slides. This system would then be capable of automatically forecasting a patient's overall survival and their specific likelihood of surviving UTUC. Such insights could empower clinicians to tailor more effective treatment strategies for each individual patient.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Patients with Upper Tract Urothelial Carcinoma (UTUC) who had radical nephroureterectomy (RNU).
Contrast-enhanced CT scan (e.g., CT urography) less than two weeks before surgery.
Complete CT image data and clinical data.
Complete whole slide image data.

Exclusion criteria

Patients with a postoperative diagnosis of non-urothelial carcinoma.
Poor quality of CT images and/or whole slide image data.
Incomplete clinical and follow-up data.

Treatment and study plan

Deep learning system for prognostication prediction in upper tract urothelial carcinoma

Other

develop and validate a deep learning system for prognostication prediction in upper tract urothelial carcinoma based on CT radiomics and whole slide images.

Primary outcomes

Overall survival

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.

Secondary outcomes

Recurrence free survival

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

Sponsors and collaborators

Lead sponsor

Mingzhao Xiao

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: May 29, 2025
Registry last updated: May 29, 2025

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

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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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