Tumor GradingNCT06559046
A CT-BASED Deep Learning Model for Predicting WHO/ISUP Pathological Grades of Clear Cell Renal Cell Carcinoma (ccRCC) :A Multicenter Cohort Study
An observational study of Tumor Grading and Deep Learning, run by Ting Huang at 1 site in China.
Trial summary
This study aims to establish an effective deep learning model to extract relevant information about renal tumors and kidneys from computed tomography (CT) images and predict the pathological grades of clear cell renal cell carcinoma (ccRCC). Read more about this study
- Who is it for?
- Ages 30 to 88 years
- Conditions the study is for
- Tumor Grading, Deep Learning, Clear Cell Renal Cell Carcinoma
- How many people
- 483 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 30 Jun 2024
- 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 30 to 88 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study recruited three cohorts of patients with pathologically diagnosed ccRCC, including a total of 483 patients who underwent nephrectomy or partial nephrectomy
Who may be able to join
- Patients with a single kidney tumor have complete imaging and clinical data
- Contrast-enhanced CT scan within 30 days before surgery
- No treatment was performed before CT examination
Who cannot join
- Patients with tumor recurrence
- Obvious artifacts on CT images
- The tumor is cystic
- Multiple cysts on the affected kidney affect the delineation of renal parenchyma
Trial locations and contacts
1 site in 1 country.
China1 site
Department of Urology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua,Zhejiang, China
Jinhua, Zhejiang, 321000
About this study
This study aims to establish an effective deep learning model to extract relevant information about renal tumors and kidneys from computed tomography (CT) images and predict the pathological grades of clear cell renal cell carcinoma (ccRCC).
Retrospective data were collected from 483 ccRCC patients across three medical centers. Arterial phase and portal venous phase CT images from the dataset were segmented for renal tumors and kidneys. Three convolutional neural networks (CNNs) were employed to extract features from the regions of interest (ROI) in the CT images across multiple dimensions including 3D, 2.5D, and 2D. Least absolute shrinkage and selection (LASSO) regression was used for feature selection. The models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
- 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 |
|---|---|---|
| High grade | Not described | |
| Low grade | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)
AUC curve
Measured: 2019-2024
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)
DCA curve
Measured: 2019-2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Aug 2024
- Study start1 Jan 2019
- Primary completion30 Jun 2024
- Study completion30 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Tumor GradingStudies 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
- 483 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ting Huang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Ting Huang, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06559046
- Sponsor's protocol ID
- 2024107
- Status last verified
- Aug 2024
- First submitted
- 12 Aug 2024
- Last updated
- 20 Aug 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A CT-BASED Deep Learning Model for Predicting WHO/ISUP Pathological Grades of Clear Cell Renal Cell Carcinoma (ccRCC) :A Multicenter Cohort Study
- ClinicalTrials.gov ID
- NCT06559046
- Acronym / sponsor ID
- 2024107
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 483 participants (actual)
- Lead sponsor
- Ting HuangUniversity, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 30 Jun 2024 (actual)
- Study completion
- 30 Jun 2024 (actual)
- First posted
- 19 Aug 2024 (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 study aims to establish an effective deep learning model to extract relevant information about renal tumors and kidneys from computed tomography (CT) images and predict the pathological grades of clear cell renal cell carcinoma (ccRCC).
Retrospective data were collected from 483 ccRCC patients across three medical centers. Arterial phase and portal venous phase CT images from the dataset were segmented for renal tumors and kidneys. Three convolutional neural networks (CNNs) were employed to extract features from the regions of interest (ROI) in the CT images across multiple dimensions including 3D, 2.5D, and 2D. Least absolute shrinkage and selection (LASSO) regression was used for feature selection. The models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| High grade | – | – |
| Low grade | – | – |
Who may qualify
- Ages
- 30 to 88 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study recruited three cohorts of patients with pathologically diagnosed ccRCC, including a total of 483 patients who underwent nephrectomy or partial nephrectomy
Key inclusion criteria (3 listed)
- Patients with a single kidney tumor have complete imaging and clinical data
- Contrast-enhanced CT scan within 30 days before surgery
- No treatment was performed before CT examination
Key exclusion criteria (4 listed)
- Patients with tumor recurrence
- Obvious artifacts on CT images
- The tumor is cystic
- Multiple cysts on the affected kidney affect the delineation of renal parenchyma
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
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)
Time frame: 2019-2024
AUC curve
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)
Time frame: 2019-2024
DCA curve
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 20 Aug 2024.
- Individual participant dataSharing planned: No
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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 Tumor Grading and Deep Learning, run by Ting Huang 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 3 inclusion and 4 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 20 Aug 2024. 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?
predict the pathological grades of clear cell renal cell carcinoma (ccRCC) (time frame: 2019-2024). The study lists 2 primary outcomes in all. 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 20 Aug 2024.