Renal CystNCT07833124
Multiphase CT Deep Learning for Bosniak II-III Renal Cysts
An observational study of Renal Cyst, run by Shanghai Zhongshan Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Renal Cyst
- How many people
- 223 participants
- Where
- 1 siteChina
- Study dates
- 10 Sep 2024to 31 Aug 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- diagnosed with cystic renal masses
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China
Shanghai, Shanghai Municipality, 200030
About this study
Official title: A Multiphase CT-based Deep Learning Model for Predicting Malignancy in Bosniak II-III Cystic Renal Masses
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.
Read the detailed description
Patients diagnosed with Bosniak II-III cystic renal masses based on preoperative contrast-enhanced CT were included. The exclusion criteria were solid portion > 25%, polycystic kidney disease, maximum diameter<1cm, Von Hippel-Lindau syndrome, without complete CT examination or histopathology-proven CRMs, poor image quality and Bosniak I and Bosniak IV masses. Deep learning models were developed to classify renal cystic lesions as benign or malignant using multiphase CT images. The performance measure were the area under the receiver operating characteristic curve, sensitivity, specificity, and balanced accuracy.
- Conditions
- Renal Cyst
- Keywords
- Convolutional Neural Networks, Deep Learning, Tomography, X-Ray Computed, Kidney Diseases, Cystic
- Medical subject headings
- Kidney Diseases, Cystic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Benign | Patients who were istopathologically diagnosed benign renal cysts | |
| Malignant | Patients who were istopathologically diagnosed malignant renal cysts |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: preoperatively
Secondary outcomes (3)
sensitivity
Sensitivity measures the proportion of actual positive cases that are correctly identified by the model. A higher sensitivity indicates that the model is better at detecting positive cases and produces fewer false negatives.
Measured: preoperatively
specificity
Specificity measures the proportion of actual negative cases that are correctly identified by the model. A higher specificity indicates that the model is better at correctly excluding negative cases and produces fewer false positives.
Measured: preoperatively
balanced accuracy
Balanced accuracy is the average of sensitivity and specificity. It provides an overall measure of classification performance while giving equal importance to the positive and negative classes, making it particularly useful when the classes are imbalanced.
Measured: preoperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Sep 2026
- Study start10 Sep 2024
- Primary completion28 Dec 2024
- Study completion31 Aug 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Renal CystStudies of the same condition, open and under way.
- More trials run by Shanghai Zhongshan HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 223 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Xuhui Central Hospital, Shanghai, Fudan University
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07833124
- Sponsor's protocol ID
- zsurologyDLMforBosniakII-III
- Status last verified
- Sep 2026
- First submitted
- 16 Sep 2026
- Last updated
- 22 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Clinical data and extracted radiomics feature data, excluding patient information.
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multiphase CT-based Deep Learning Model for Predicting Malignancy in Bosniak II-III Cystic Renal Masses
- ClinicalTrials.gov ID
- NCT07833124
- Acronym / sponsor ID
- zsurologyDLMforBosniakII-III
- Conditions
- Renal Cyst
- Keywords
- Convolutional Neural Networks, Deep Learning, Tomography, X-Ray Computed, Kidney Diseases, Cystic
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 223 participants (actual)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Collaborators
- Xuhui Central Hospital, Shanghai, Fudan University
- Study start
- 10 Sep 2024 (actual)
- Primary completion
- 28 Dec 2024 (actual)
- Study completion
- 31 Aug 2026 (actual)
- First posted
- 22 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Patients diagnosed with Bosniak II-III cystic renal masses based on preoperative contrast-enhanced CT were included. The exclusion criteria were solid portion > 25%, polycystic kidney disease, maximum diameter<1cm, Von Hippel-Lindau syndrome, without complete CT examination or histopathology-proven CRMs, poor image quality and Bosniak I and Bosniak IV masses. Deep learning models were developed to classify renal cystic lesions as benign or malignant using multiphase CT images. The performance measure were the area under the receiver operating characteristic curve, sensitivity, specificity, and balanced accuracy.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Benign | – | –Patients who were istopathologically diagnosed benign renal cysts |
| Malignant | – | –Patients who were istopathologically diagnosed malignant renal cysts |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- diagnosed with cystic renal masses
Key exclusion criteria (7 listed)
- olid portion > 25%;
- polycystic kidney disease;
- maximum diameter<1cm;
- Von Hippel-Lindau syndrome;
- without complete CT examination or histopathology-proven CRMs;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
the area under the receiver operating characteristic curve
Time frame: preoperatively
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).
3 secondary outcomes
sensitivity
Time frame: preoperatively
specificity
Time frame: preoperatively
balanced accuracy
Time frame: preoperatively
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2026.
- Individual participant dataSharing planned: Yes. Clinical data and extracted radiomics feature data, excluding patient information.
Related trials for Renal Cyst
RecruitingObservational study
Genetic Analysis of Birt Hogg-Dube Syndrome and Characterization of Predisposition to Kidney Cancer
Kidney Neoplasms, Kidney Cancer
RecruitingObservational study
Rare Disease Patient Registry & Natural History Study - Coordination of Rare Diseases at Sanford
Rare Disorders, Undiagnosed Disorders
RecruitingPhase 3
A Study to See if Tolvaptan is Safe in Infants and Children Who at Enrollment Are 28 Days to Less Than 18 Years Old With Autosomal Recessive Polycystic Kidney Disease (ARPKD)
Autosomal Recessive Polycystic Kidney (ARPKD)
RecruitingObservational study
ARPKD Database Study
Hepato/Renal Fibrocystic Disease, Autosomal Recessive Polycystic Kidney Disease
RecruitingObservational study
Surveillance of Complex Renal Cysts - The SOCRATIC Study
Complex Renal Cyst
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Renal Cyst, run by Shanghai Zhongshan Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the area under the receiver operating characteristic curve (time frame: preoperatively). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2026.