Multiphase CT Deep Learning for Bosniak II-III Renal Cysts

NCT07833124 | Completed

• 1 other identifier: zsurologyDLMforBosniakII-III
• Study Type: observational
• Target: 223
• Locations: 1 country
• Sites: 1

Brief Summary

This retrospective study combine radiomics and deep learning models to predict malignancy in Bosniak II-III cystic renal masses, aiming for improving preoperative risk assessment and reducing unnecessary surgery for benign lesions and avoiding delayed treatment of malignant disease. The main question it aims to answer is:

• How to specially predict malignancy in Bosniak II-III cystic renal masses？ The investigators retrospectively included patients diagnosed with Bosniak II-III cystic renal masses based on preoperative contrast-enhanced CT.

Conditions

Renal Cyst

Keywords

Convolutional Neural Networks; Deep Learning; Tomography, X-Ray Computed; Kidney Diseases, Cystic

Outcome Measures

Primary Outcomes (1)

• the area under the receiver operating characteristic curve

  The area under the receiver operating characteristic curve measures the overall ability of a binary classifier to distinguish between positive and negative classes, with values ranging from 0.5 (random guessing) to 1.0 (perfect classification).

  preoperatively

Secondary Outcomes (3)

• sensitivity

  preoperatively

• specificity

  preoperatively

• balanced accuracy

  preoperatively

Study Arms (2)

Benign

Patients who were istopathologically diagnosed benign renal cysts

Malignant

Patients who were istopathologically diagnosed malignant renal cysts

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population includes patients diagnosed with renal cyst in the participated centers. Clinical and imaging data were retrospectively collected from medical records, including demographic characteristics (age, gender, BMI), cyst characters(size, loction, aterality, number of septa, septal thickness, wall thickness, presence of calcification, enhancement pattern, mural nodules, and Bosniak classification). Histopathological findings were also collected when available and were used as the reference standard for lesions that underwent surgical resection.

You may qualify if:

• diagnosed with cystic renal masses

You may not qualify if:

• olid portion \> 25%;
• polycystic kidney disease;
• maximum diameter\<1cm;
• Von Hippel-Lindau syndrome;
• without complete CT examination or histopathology-proven CRMs;
• poor image quality;
• Bosniak I and Bosniak IV masses.

Sponsors & Collaborators

• Shanghai Zhongshan Hospital: lead
• Xuhui Central Hospital, Shanghai: collaborator
• Minhang Hospital, Fudan University: collaborator

Study Sites (1)

Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China

Shanghai, Shanghai Municipality, 200030, China

Location

MeSH Terms

Conditions

Kidney Diseases, Cystic

Condition Hierarchy (Ancestors)

Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 16, 2026
• First Posted: September 22, 2026
• Study Start: September 10, 2024
• Primary Completion: December 28, 2024
• Study Completion: August 31, 2026
• Last Updated: September 22, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Within six months after publication in the journal.
• Access Criteria: The data supporting this study are available from the enrolled institutions, but restrictions apply to their availability due to privacy reasons. Data can be accessed upon reasonable request from the corresponding author.

Locations

CN (1)