RadiomicsAPFNCT06062173
Preoperative Prediction of Adherent Perirenal Fat.
An observational study of Radiomics, run by The First Hospital of Jilin University at 1 site in China.
Trial summary
In addition to kidney tumor specific factors, adherent perirenal fat is one of the most important causes of technical complications in kidney surgery, and currently, there is a lack of widely used non-invasive predictive models in clinical practice. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Radiomics
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 5 Jan 2020to Dec 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 18 to 90 years
- All sexes
- Adult, Older adult
Study population: Preoperative renal CT plain scan imaging data and related clinical data of patients who underwent partial nephrectomy or radical nephrectomy in the First Hospital of Jilin University from January 2022 to December 2022 were retrospectively collected. Imaging data and clinical data from other research centers from June 2023 to September 2023 were prospectively collected. Select the required data according to the exclusion criteria.
Who may be able to join
- （1）Renal tumors, patients requiring surgical treatment. (2) Patients with complete preoperative CT image data.
Who cannot join
- -(1) Preoperative complications such as acute urinary tract infection, hydronephrosis, pulmonary infection, autoimmune disease, and blood system disease.
- Severe respiratory movement artifacts in CT images. (3) Pregnant or breastfeeding women. (4) Patients who have received immunotherapy or chemoradiotherapy.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Preoperative Prediction of Adherent Perirenal Fat Based on CT Radiomics Combined With Deep Learning: a Prospective, Multicenter Study.
In addition to kidney tumor specific factors, adherent perirenal fat is one of the most important causes of technical complications in kidney surgery, and currently, there is a lack of widely used non-invasive predictive models in clinical practice. In this study, a deep learning algorithm based on CT imaging and nomogram was proposed to identify and predict the presence of adherent perirenal fat. This study includes the construction of a prediction model based on CT imaging and the verification of the prediction model.
Read the detailed description
Importance:
For patients with kidney tumors requiring surgical treatment, adhesive perirenal fat is a frustrating variable that surgeons encounter during surgery, but the current image-dependent kidney morphometric scoring system used to predict the potential difficulty of surgery ignores this factor. Accurate preoperative prediction of perirenal fat status remains an urgent need.
Purpose:
To determine whether radiomics features of perirenal fat derived from computed tomography images can provide valuable information for judging perirenal fat status, develop a prediction model based on CT radiomics combined with deep learning, and validate the performance of the model in an independent cohort.
Design, setup and participants:
The study included one retrospective dataset and one prospective dataset from four medical centers between January 2020 and September 2023. Kidney plain CT scan was performed in xx adult patients with partial nephrectomy or radical nephrectomy. The training set, validation set, and internal test set were provided by the First Hospital of Jilin University, and the external test set was provided by the First Hospital of Siping City, Liaoyuan Central Hospital and Dongfeng County Hospital. This diagnostic study used single-institution data from January 2020 to May 2023 to extract imaging omics features from the perirenal fat region (independent sample T-test, minimum absolute contraction, and selection operator logistic regression was used to screen for the best imaging omics features). Univariate and multivariate analyses of clinical variables in patients prior to renal surgery were performed to determine independent predictors of adherent perirenal fat in the clinical setting. Different classifiers were used to build prediction models using only the image-omics features and fusion prediction models using independent clinical predictors combined with the image-omics features. Its performance is verified in two test sets.
Main achievements and measures:
The discriminant performance of the image omics model was evaluated by the area under the receiver operating characteristic curve and confirmed by decision curve analysis.
- Conditions
- Radiomics
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 |
|---|---|---|
| Adherent perirenal fat group | The surgeon considers perirenal fat to be adherent. | |
| Non-adherent perirenal fat group | Perirenal fat is considered nonadherent by surgeons. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Radiomics features
Radiomics features related to the prediction of adherent perirenal fat.
Measured: From January 2020 to December 2023.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Oct 2023
- Study start5 Jan 2020
- Primary completionMar 2024 (estimated)
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for RadiomicsStudies of the same condition, open and under way.
- More trials run by The First Hospital of Jilin UniversityThe 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
- Other
- Time perspective
- Other
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Hospital of Jilin University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- yanbo wang, Principal investigator, The First Hospital of Jilin University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06062173
- Sponsor's protocol ID
- wangyanbo
- Acronym
- APF
- Status last verified
- Jun 2023
- First submitted
- 29 Aug 2023
- Last updated
- 3 Jan 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Preoperative Prediction of Adherent Perirenal Fat Based on CT Radiomics Combined With Deep Learning: a Prospective, Multicenter Study.
- ClinicalTrials.gov ID
- NCT06062173
- Acronym / sponsor ID
- APF / wangyanbo
- Conditions
- Radiomics
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- The First Hospital of Jilin UniversityUniversity, hospital or other
- Study start
- 5 Jan 2020 (actual)
- Primary completion
- Mar 2024 (estimated)
- Study completion
- Dec 2024 (estimated)
- First posted
- 2 Oct 2023 (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.
In addition to kidney tumor specific factors, adherent perirenal fat is one of the most important causes of technical complications in kidney surgery, and currently, there is a lack of widely used non-invasive predictive models in clinical practice. In this study, a deep learning algorithm based on CT imaging and nomogram was proposed to identify and predict the presence of adherent perirenal fat. This study includes the construction of a prediction model based on CT imaging and the verification of the prediction model.
Detailed description
Importance:
For patients with kidney tumors requiring surgical treatment, adhesive perirenal fat is a frustrating variable that surgeons encounter during surgery, but the current image-dependent kidney morphometric scoring system used to predict the potential difficulty of surgery ignores this factor. Accurate preoperative prediction of perirenal fat status remains an urgent need.
Purpose:
To determine whether radiomics features of perirenal fat derived from computed tomography images can provide valuable information for judging perirenal fat status, develop a prediction model based on CT radiomics combined with deep learning, and validate the performance of the model in an independent cohort.
Design, setup and participants:
The study included one retrospective dataset and one prospective dataset from four medical centers between January 2020 and September 2023. Kidney plain CT scan was performed in xx adult patients with partial nephrectomy or radical nephrectomy. The training set, validation set, and internal test set were provided by the First Hospital of Jilin University, and the external test set was provided by the First Hospital of Siping City, Liaoyuan Central Hospital and Dongfeng County Hospital. This diagnostic study used single-institution data from January 2020 to May 2023 to extract imaging omics features from the perirenal fat region (independent sample T-test, minimum absolute contraction, and selection operator logistic regression was used to screen for the best imaging omics features). Univariate and multivariate analyses of clinical variables in patients prior to renal surgery were performed to determine independent predictors of adherent perirenal fat in the clinical setting. Different classifiers were used to build prediction models using only the image-omics features and fusion prediction models using independent clinical predictors combined with the image-omics features. Its performance is verified in two test sets.
Main achievements and measures:
The discriminant performance of the image omics model was evaluated by the area under the receiver operating characteristic curve and confirmed by decision curve analysis.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Adherent perirenal fat group | – | –The surgeon considers perirenal fat to be adherent. |
| Non-adherent perirenal fat group | – | –Perirenal fat is considered nonadherent by surgeons. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Preoperative renal CT plain scan imaging data and related clinical data of patients who underwent partial nephrectomy or radical nephrectomy in the First Hospital of Jilin University from January 2022 to December 2022 were retrospectively collected. Imaging data and clinical data from other research centers from June 2023 to September 2023 were prospectively collected. Select the required data according to the exclusion criteria.
Key inclusion criteria (1 listed)
- （1）Renal tumors, patients requiring surgical treatment. (2) Patients with complete preoperative CT image data.
Key exclusion criteria (2 listed)
- -(1) Preoperative complications such as acute urinary tract infection, hydronephrosis, pulmonary infection, autoimmune disease, and blood system disease.
- Severe respiratory movement artifacts in CT images. (3) Pregnant or breastfeeding women. (4) Patients who have received immunotherapy or chemoradiotherapy.
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
Radiomics features
Time frame: From January 2020 to December 2023.
Radiomics features related to the prediction of adherent perirenal fat.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jan 2024.
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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.
yanbo wang principal investigator
The First Hospital of Jilin University
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 Radiomics, run by The First Hospital of Jilin University 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 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: status not confirmed, as of the sponsor's last update on 3 Jan 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?
Results are not available in the source record.
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?
Radiomics features (time frame: From January 2020 to December 2023.). 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 3 Jan 2024.