Renal Cell Carcinoma (RCC)NCT06876584
The CT-based Deep Learning Model Predicts Complications in Partial Nephrectomy
An observational study of Renal Cell Carcinoma (RCC) and Renal Cyst, run by Du Lingzhi at 1 site in China.
Trial summary
The investigators combine radiomics and deep learning to analyze the lesions more thoroughly, aiming for a more accurate prediction of complications in partial nephrectomy, and compare this approach with traditional models. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Renal Cell Carcinoma (RCC), Renal Cyst
- How many people
- 1,474 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2024to 28 Feb 2025
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population includes patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy in the participated centers. Clinical and imaging data were retrospectively collected from medical records, including demographic characteristics (age, gender, BMI), tumor location (left or right kidney), surgical details (surgical approach, ischemia time), and perioperative complications. Patients were included based on the availability of complete clinical, surgical, and imaging data. Exclusion criteria comprised individuals with missing or unavailable imaging data, or no available enhanced CT images. The study aims to combine CT-based radiomics features and clinical features to develop a deep learning model to predict perioperative complications of partial nephrectomy, and compare with traditional anatomical classification models.
Who may be able to join
- Clinical diagnosis of renal cell carcinoma or renal cyst
- Underwent partial nephrectomy between June 2014 and July 2024
Who cannot join
- Missing or unavailable imaging data
- No available enhanced CT images
Trial locations and contacts
1 site in 1 country.
China1 site
Name: Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China
Shanghai, Xuhui District, 200032
About this study
Official title: The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy
The investigators combine radiomics and deep learning to analyze the lesions more thoroughly, aiming for a more accurate prediction of complications in partial nephrectomy, and compare this approach with traditional models.
Read the detailed description
In this study, patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy across multiple centers was included. And the participants were excluded if they had (a) missing or unavailable imaging data or (b) no available enhanced CT images. The cohort was divided into training and test sets at a 7:3 ratio. After that, the radiomics features were extracted from the images, and lasso regression was used to select features. Then a deep learning model was developed to predict complications and risk grades and compared with traditional classification models (RENAL and PADUA), demonstrating superior applicability.
- Conditions
- Renal Cell Carcinoma (RCC), Renal Cyst
- Keywords
- CT-based deep learning, complication prediction, traditional classification model, partial nephrectomy
- Medical subject headings
- Carcinoma, Renal Cell
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 |
|---|---|---|
| Complication 1 | Patients who experienced perioperative complications during the partial nephrectomy | |
| Complication 0 | Patients who didn't experience perioperative complications during the partial nephrectomy |
What is being measured
The primary outcome is the main question the study is designed to answer.
whether complications occurred
Retrospectively review the medical record system to determine whether patients developed postoperative complications.
Measured: perioperatively
Secondary outcomes (1)
Patients' risk grade
Based on the widely recognized Clavien-Dindo classification (CDC) system for surgical complications, these complications were categorized into four grades: Grade I, II, III, and IV. Risk grade was assigned accordingly: "no risk" is defined as no complications occurred, "grade low" is defined as the highest level of complication being Grade I, "grade moderate" is defined as the highest level of complication being Grade II, and "grade high" is defined as complications of Grade III or higher, which are life-threatening.
Measured: perioperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Mar 2025
- Study start1 Jun 2024
- Primary completion31 Dec 2024
- Study completion28 Feb 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Renal Cell Carcinoma (RCC)Studies of the same condition, open and under way.
- 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
- 1,474 participants (actual)
Sponsor and investigators
- Lead sponsor
- Du Lingzhi
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Zhongshan Hospital, Fudan University, Xuhui Central Hospital, Shanghai
- Responsible party
- Sponsor-investigator, Du Lingzhi, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06876584
- Sponsor's protocol ID
- zsurologyDLMforPNcomplication
- Status last verified
- Mar 2025
- First submitted
- 8 Mar 2025
- Last updated
- 14 Mar 2025
- Results posted
- No
- Data monitoring committee
- Yes
- 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 14 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy
- ClinicalTrials.gov ID
- NCT06876584
- Acronym / sponsor ID
- zsurologyDLMforPNcomplication
- Conditions
- Renal Cell Carcinoma (RCC), Renal Cyst
- Keywords
- CT-based deep learning, complication prediction, traditional classification model, partial nephrectomy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,474 participants (actual)
- Lead sponsor
- Du LingzhiUniversity, hospital or other
- Collaborators
- Shanghai Zhongshan Hospital, Fudan University, Xuhui Central Hospital, Shanghai
- Study start
- 1 Jun 2024 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 28 Feb 2025 (actual)
- First posted
- 14 Mar 2025 (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.
The investigators combine radiomics and deep learning to analyze the lesions more thoroughly, aiming for a more accurate prediction of complications in partial nephrectomy, and compare this approach with traditional models.
Detailed description
In this study, patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy across multiple centers was included. And the participants were excluded if they had (a) missing or unavailable imaging data or (b) no available enhanced CT images. The cohort was divided into training and test sets at a 7:3 ratio. After that, the radiomics features were extracted from the images, and lasso regression was used to select features. Then a deep learning model was developed to predict complications and risk grades and compared with traditional classification models (RENAL and PADUA), demonstrating superior applicability.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Complication 1 | – | –Patients who experienced perioperative complications during the partial nephrectomy |
| Complication 0 | – | –Patients who didn't experience perioperative complications during the partial nephrectomy |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population includes patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy in the participated centers. Clinical and imaging data were retrospectively collected from medical records, including demographic characteristics (age, gender, BMI), tumor location (left or right kidney), surgical details (surgical approach, ischemia time), and perioperative complications. Patients were included based on the availability of complete clinical, surgical, and imaging data. Exclusion criteria comprised individuals with missing or unavailable imaging data, or no available enhanced CT images. The study aims to combine CT-based radiomics features and clinical features to develop a deep learning model to predict perioperative complications of partial nephrectomy, and compare with traditional anatomical classification models.
Key inclusion criteria (2 listed)
- Clinical diagnosis of renal cell carcinoma or renal cyst
- Underwent partial nephrectomy between June 2014 and July 2024
Key exclusion criteria (2 listed)
- Missing or unavailable imaging data
- No available enhanced CT images
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
whether complications occurred
Time frame: perioperatively
Retrospectively review the medical record system to determine whether patients developed postoperative complications.
1 secondary outcome
Patients' risk grade
Time frame: perioperatively
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 14 Mar 2025.
- Individual participant dataSharing planned: Yes. Clinical data and extracted radiomics feature data, excluding patient information.
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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 Cell Carcinoma (RCC) and Renal Cyst, run by Du Lingzhi 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 2 inclusion and 2 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 14 Mar 2025. 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?
whether complications occurred (time frame: perioperatively). 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 14 Mar 2025.