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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-01-01

Study Completion Date

2025-11-01

Brief Summary

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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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Detailed Description

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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.

Conditions

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UTUC

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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AI-UTUC

Patients with Upper Tract Urothelial Carcinoma (UTUC) who underwent radical nephroureterectomy (RNU)

Deep learning system for prognostication prediction in upper tract urothelial carcinoma

Intervention Type OTHER

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

Interventions

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Deep learning system for prognostication prediction in upper tract urothelial carcinoma

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

Intervention Type OTHER

Eligibility Criteria

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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.

Eligible Sex

ALL

Accepts Healthy Volunteers

No