RadiomicsNCT06167863
Retrospective Analysis of the Correlation Between Imaging Features and Pathology, Prognosis in Renal Tumors
An observational study of Radiomics and Deep Learning, run by Zhen Li at 1 site in China.
Trial summary
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Radiomics, Deep Learning, Artificial Intelligence
- What is being tested?
- radiomics
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 31 Aug 2023to 31 Oct 2023
- 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: patients diagnosed with renal tumors
Who may be able to join
- Our hospital admits patients with renal tumors in the urology department. The diagnosis is confirmed through surgical pathology, and the patients' imaging data is obtained through contrast-enhanced Computed Tomography or Magnetic Resonance examination in the radiology department.
Who cannot join
- Patients who have undergone puncture, microwave, interventional therapies before the examination, or who have received chemotherapy or radiotherapy;
- Patients with poor respiratory coordination, resulting in significant image artifacts;
- Lesions are cystic, without discernible regions of interest, or with multiple regions of necrosis within the lesion;
- Lesions are too small, with a diameter of less than 1cm;
- Thin-slice imaging is not available in the CT scan.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Retrospective Analysis of the Correlation Between CT/MRI Imaging Features and Pathology, Prognosis in Patients With Renal Tumors
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Radiomics provides internal features of lesions for precise diagnosis, prognosis prediction, and personalized treatment planning. Early and accurate diagnosis of renal tumors is crucial, but it's challenging due to morphological and pathological overlap between benign and malignant lesions. The accurate diagnosis of RCC, especially for small tumors, remains a significant challenge. Recent studies have shown a relationship between body composition, obesity, and renal tumors. Common indicators like body weight and BMI fail to reflect body composition accurately. Research on the role of body composition, including adipose tissue, in tumor pathology could improve clinical diagnosis and treatment planning.
Read the detailed description
Renal cell carcinoma (RCC) accounts for 80-90% of malignant tumors in the kidney and has the highest mortality rate among genitourinary tumors. Imaging examinations play an important role in the diagnosis, preoperative assessment, selection of surgical methods, and evaluation of therapeutic efficacy in RCC. However, traditional imaging primarily reflects the morphological and functional changes of the tumor and cannot reflect the internal heterogeneity. In the current era of precision medicine, radiomics can provide internal features of lesions that cannot be observed by the naked eye, enabling precise diagnosis, prognosis prediction, efficacy evaluation, and personalized treatment planning for tumors. Renal cell carcinoma is highly elusive, with over 30% of patients already experiencing metastasis at the time of initial diagnosis, and it is insensitive to radiotherapy and chemotherapy. Early diagnosis and differential diagnosis of renal tumors are important prognostic factors that affect patient survival and treatment. Given the different treatment approaches, preoperative differentiation of lesion nature holds significant clinical significance. However, there is some overlap in the morphological and pathological features between benign and malignant lesions of the kidney, making it difficult to differentially diagnose such tumors using existing imaging techniques alone. Therefore, the accurate diagnosis of renal cell carcinoma, especially for small renal tumors (≤4cm), remains a significant challenge. In recent years, the relationship between body composition, such as obesity, and renal tumors has received increasing attention. Previous studies have shown a close association between obesity and kidney cancer. Common indicators such as body weight, BMI, and waist circumference fail to effectively reflect body composition, including various fat and muscle distributions and relative amounts. Different body compositions have different physiological functions and varying impacts on tumors. Further specific research on the true role of various body compositions, including adipose tissue, in tumor pathology would aid in clinical diagnosis and subsequent treatment planning.
- Conditions
- Radiomics, Deep Learning, Artificial Intelligence, Body Composition
- Being tested
- radiomics
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
radiomicsDiagnostic test
extracted image features from CT or MRI
| Study group | Role | What they receive |
|---|---|---|
| WHO ISUP grading high | high-grade refer to Grades 3 and 4 tumours with an unfavourable prognosis Receives: Diagnostic Test: radiomics | |
| WHO ISUP grading low | low-grade refer to Grades 1 and 2 tumours with a promising prognosis Receives: Diagnostic Test: radiomics |
What is being measured
The primary outcome is the main question the study is designed to answer.
WHO/ISUP grade pathologically
The WHO ISUP grade of the tumor indicated in the post-operative surgical pathology report
Measured: 1 month
Secondary outcomes (1)
pathological T stage
pathological T stage according to the eighth TNM system.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Dec 2023
- Study start31 Aug 2023
- Primary completion31 Oct 2023
- Study completion31 Oct 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for RadiomicsStudies 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
- 1,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Zhen Li
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Zhen Li, Tongji Hospital
- Investigators
- Li Dr, Principal investigator, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06167863
- Sponsor's protocol ID
- TJ-IRB20230893
- Status last verified
- Dec 2023
- First submitted
- 30 Nov 2023
- Last updated
- 13 Dec 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Li S, Zhou Z, Gao M, Liao Z, He K, Qu W, Li J, Kamel IR, Chu Q, Zhang Q, Li Z. Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study. Int J Surg. 2024 Jul 1;110(7):4221-4230. doi: 10.1097/JS9.0000000000001358.
PubMed 38573065
Study at a glance
From the registry record, last updated by the sponsor on 13 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Retrospective Analysis of the Correlation Between CT/MRI Imaging Features and Pathology, Prognosis in Patients With Renal Tumors
- ClinicalTrials.gov ID
- NCT06167863
- Acronym / sponsor ID
- TJ-IRB20230893
- Conditions
- Radiomics, Deep Learning, Artificial Intelligence, Body Composition
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,000 participants (actual)
- Lead sponsor
- Zhen LiUniversity, hospital or other
- Study start
- 31 Aug 2023 (actual)
- Primary completion
- 31 Oct 2023 (actual)
- Study completion
- 31 Oct 2023 (actual)
- First posted
- 13 Dec 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.
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Radiomics provides internal features of lesions for precise diagnosis, prognosis prediction, and personalized treatment planning. Early and accurate diagnosis of renal tumors is crucial, but it's challenging due to morphological and pathological overlap between benign and malignant lesions. The accurate diagnosis of RCC, especially for small tumors, remains a significant challenge. Recent studies have shown a relationship between body composition, obesity, and renal tumors. Common indicators like body weight and BMI fail to reflect body composition accurately. Research on the role of body composition, including adipose tissue, in tumor pathology could improve clinical diagnosis and treatment planning.
Detailed description
Renal cell carcinoma (RCC) accounts for 80-90% of malignant tumors in the kidney and has the highest mortality rate among genitourinary tumors. Imaging examinations play an important role in the diagnosis, preoperative assessment, selection of surgical methods, and evaluation of therapeutic efficacy in RCC. However, traditional imaging primarily reflects the morphological and functional changes of the tumor and cannot reflect the internal heterogeneity. In the current era of precision medicine, radiomics can provide internal features of lesions that cannot be observed by the naked eye, enabling precise diagnosis, prognosis prediction, efficacy evaluation, and personalized treatment planning for tumors. Renal cell carcinoma is highly elusive, with over 30% of patients already experiencing metastasis at the time of initial diagnosis, and it is insensitive to radiotherapy and chemotherapy. Early diagnosis and differential diagnosis of renal tumors are important prognostic factors that affect patient survival and treatment. Given the different treatment approaches, preoperative differentiation of lesion nature holds significant clinical significance. However, there is some overlap in the morphological and pathological features between benign and malignant lesions of the kidney, making it difficult to differentially diagnose such tumors using existing imaging techniques alone. Therefore, the accurate diagnosis of renal cell carcinoma, especially for small renal tumors (≤4cm), remains a significant challenge. In recent years, the relationship between body composition, such as obesity, and renal tumors has received increasing attention. Previous studies have shown a close association between obesity and kidney cancer. Common indicators such as body weight, BMI, and waist circumference fail to effectively reflect body composition, including various fat and muscle distributions and relative amounts. Different body compositions have different physiological functions and varying impacts on tumors. Further specific research on the true role of various body compositions, including adipose tissue, in tumor pathology would aid in clinical diagnosis and subsequent treatment planning.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| WHO ISUP grading high | – | Diagnostic Test: radiomicshigh-grade refer to Grades 3 and 4 tumours with an unfavourable prognosis |
| WHO ISUP grading low | – | Diagnostic Test: radiomicslow-grade refer to Grades 1 and 2 tumours with a promising prognosis |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients diagnosed with renal tumors
Key inclusion criteria (1 listed)
- Our hospital admits patients with renal tumors in the urology department. The diagnosis is confirmed through surgical pathology, and the patients' imaging data is obtained through contrast-enhanced Computed Tomography or Magnetic Resonance examination in the radiology department.
Key exclusion criteria (5 listed)
- Patients who have undergone puncture, microwave, interventional therapies before the examination, or who have received chemotherapy or radiotherapy;
- Patients with poor respiratory coordination, resulting in significant image artifacts;
- Lesions are cystic, without discernible regions of interest, or with multiple regions of necrosis within the lesion;
- Lesions are too small, with a diameter of less than 1cm;
- Thin-slice imaging is not available in the CT scan.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| radiomicsextracted image features from CT or MRI | Diagnostic test | WHO ISUP grading high, WHO ISUP grading low |
Primary outcome
WHO/ISUP grade pathologically
Time frame: 1 month
The WHO ISUP grade of the tumor indicated in the post-operative surgical pathology report
1 secondary outcome
pathological T stage
Time frame: 1 month
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 13 Dec 2023.
- Derived from this study: PubMed 38573065Li S, Zhou Z, Gao M, Liao Z, He K, Qu W, Li J, Kamel IR, Chu Q, Zhang Q, Li Z. Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study. Int J Surg. 2024 Jul 1;110(7):4221-4230. doi: 10.1097/JS9.0000000000001358.
- Individual participant dataSharing planned: Undecided
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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.
Li Dr principal investigator
Tongji Hospital
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 and Deep Learning, run by Zhen Li 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 5 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 13 Dec 2023. 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?
WHO/ISUP grade pathologically (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06167863, which also lists 1 linked publication.
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 13 Dec 2023.