Renal TumorNCT07743749
An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors
An observational study of Renal Tumor and Kidney Neoplasm, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences at 1 site in China.
Trial summary
This retrospective + prospective, non-interventional study aims to develop and evaluate artificial intelligence methods for the detection, pathological subtyping, and histological grading of renal tumors using magnetic resonance imaging (MRI). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Renal Tumor, Kidney Neoplasm
- What is being tested?
- MRI-Based Artificial Intelligence Analysis
- How many people
- 900 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This retrospective + prospective, hospital-based study includes adult patients with renal tumors who underwent preoperative renal MRI as part of routine clinical care and had corresponding pathological diagnoses. Eligible cases include benign and malignant renal tumor subtypes. Existing de-identified MRI, pathological, demographic, clinical, and laboratory data are collected for artificial intelligence model development and evaluation. No healthy volunteers are included, and no additional examinations, treatments, or study-specific follow-up are performed.
Who may be able to join
- Patients aged 18 years or older.
- Patients diagnosed with a renal tumor.
- Availability of preoperative renal magnetic resonance imaging examinations.
- Availability of a corresponding pathological diagnosis, including pathological subtype and, where applicable, histological grade.
- Magnetic resonance images that can be successfully retrieved and are of - - sufficient quality for image analysis.
Who cannot join
- Absence of renal magnetic resonance imaging data.
- Absence of a corresponding pathological diagnosis or insufficient pathological subtype or grading information.
- Magnetic resonance images that cannot be retrieved, opened, or read.
- Poor image quality that precludes reliable image annotation or artificial intelligence analysis.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing
xiongjun YE, 010-87787170, yexiongjun@cicams.ac.cn
About this study
This retrospective + prospective, non-interventional study aims to develop and evaluate artificial intelligence methods for the detection, pathological subtyping, and histological grading of renal tumors using magnetic resonance imaging (MRI). Approximately 900 adult patients with available preoperative renal MRI examinations and postoperative pathological results will be included. The pathological findings will be used as the reference standard for model development and evaluation. In addition to MRI data, selected demographic, clinical, and laboratory information may be incorporated to improve model performance. The study will not change participants' diagnosis, treatment, or follow-up, and no additional examinations or interventions will be required. All study data will be de-identified before analysis. The ultimate goal is to develop an MRI-based intelligent diagnostic approach that may assist clinicians in the preoperative assessment and individualized management of patients with renal tumors.
Read the detailed description
Renal tumors include multiple benign and malignant pathological subtypes with substantial differences in biological behavior, treatment strategy, and prognosis. Surgical planning and clinical management are closely related to the pathological subtype and histological grade of the tumor. However, accurately determining these pathological characteristics before surgery using conventional MRI interpretation remains challenging.
This is a retrospective + prospective, observational, and non-interventional study. Adult patients with renal tumors will be identified from existing clinical records. Eligible patients will have available renal MRI examinations and corresponding pathological diagnoses, including pathological subtype and, when applicable, histological grade. Cases with unreadable MRI data or images of insufficient quality for analysis will be excluded.
Existing study data will include multisequence MRI examinations, such as T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging, when available. Demographic information, relevant clinical history, laboratory results, and radiology report information may also be collected. Pathological findings will serve as the reference standard for model training and evaluation. All data will be de-identified before processing and analysis.
The study will develop artificial intelligence models for the following tasks:
- Detection and localization of renal tumors on multisequence MRI.
- Segmentation of renal tumors and extraction of quantitative imaging features.
- Classification of common benign and malignant renal tumor subtypes.
- Identification of rare pathological subtypes using small-sample or cross-modal learning methods.
- Prediction of histological grade for malignant renal tumors.
- Integration of MRI, demographic, clinical, and laboratory information to improve pathological subtyping and grading.
The dataset will be divided into model-development and validation datasets. Additional cases collected from different time periods or participating sources may be used for independent testing. Model performance will be evaluated by comparing artificial intelligence predictions with pathological diagnoses.
This study does not assign any treatment or diagnostic intervention. It will not affect participants' routine clinical care and will not require additional imaging examinations, blood collection, surgery, medication, or follow-up visits. The study is intended to develop an intelligent MRI-based diagnostic system that may support preoperative decision-making for patients with renal tumors.
- Conditions
- Renal Tumor, Kidney Neoplasm
- Being tested
- MRI-Based Artificial Intelligence Analysis
- Keywords
- Magnetic Resonance Imaging, Artificial Intelligence, Deep Learning, Renal Tumor, Pathological Subtyping, Histological Grading, Multimodal Learning, Computer-Aided Diagnosis, Tumor Segmentation, Rare Renal Tumor Subtypes
- Medical subject headings
- Carcinoma, Renal Cell, Kidney Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MRI-Based Artificial Intelligence AnalysisDiagnostic test
Existing preoperative multisequence renal MRI images, including T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging when available, were retrospectively analyzed using artificial intelligence and deep learning methods. The models were developed to detect and segment renal tumors and to predict pathological subtype and histological grade. Postoperative pathological findings were used as the reference standard. No additional MRI examination or diagnostic procedure was performed for the study.
| Study group | Role | What they receive |
|---|---|---|
| Patients With Renal Tumors | Adult patients with pathologically confirmed renal tumors who underwent preoperative multisequence renal MRI as part of routine clinical care and had available pathological subtype and, when applicable, histological grade information. Existing de-identified MRI, pathological, clinical, and laboratory data were collected for artificial intelligence model development and evaluation. No additional examination, treatment, or study-specific intervention was administered. Receives: Diagnostic Test: MRI-Based Artificial Intelligence Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
The pathological subtype predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological diagnosis as the reference standard in the held-out test dataset. Accuracy will be calculated as the number of correctly classified renal tumors divided by the total number of renal tumors evaluated. Classification performance for individual pathological subtypes will also be summarized using sensitivity, specificity, and F1 score, where applicable.
Measured: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Accuracy of MRI-Based Artificial Intelligence for Histological Grading of Malignant Renal Tumors
The histological grade predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological grade as the reference standard. Histological grading will be assessed according to the four-tier World Health Organization/International Society of Urological Pathology grading system. Accuracy will be calculated as the number of malignant renal tumors with correctly predicted histological grade divided by the total number of malignant renal tumors evaluated.
Measured: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Secondary outcomes (2)
Performance of the Artificial Intelligence Model for Renal Tumor Detection
The ability of the artificial intelligence model to detect and localize renal tumors on multisequence MRI will be evaluated by comparing model-generated tumor locations with expert manual annotations. Detection performance will be summarized using sensitivity and the proportion of correctly localized renal tumors.
Measured: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Accuracy of Artificial Intelligence-Based Renal Tumor Segmentation
Artificial intelligence-generated renal tumor segmentations will be compared with pixel-level manual annotations prepared under the supervision of experienced physicians. Segmentation agreement will be evaluated using the Dice similarity coefficient.
Measured: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Aug 2026
- Study start1 Jan 2021
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Renal TumorStudies 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
- Case only
- Time perspective
- Other
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical Sciences
- Sponsor type
- University, hospital or other
- Collaborators
- Peking University People's Hospital, Shanxi Province Cancer Hospital, Chinese PLA General Hospital, RenJi Hospital, Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center
- Responsible party
- Principal investigator, Xiongjun Ye, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07743749
- Sponsor's protocol ID
- NCC-020738
- Status last verified
- Jul 2026
- First submitted
- 29 Jul 2026
- Last updated
- 4 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors
- ClinicalTrials.gov ID
- NCT07743749
- Acronym / sponsor ID
- NCC-020738
- Conditions
- Renal Tumor, Kidney Neoplasm
- Keywords
- Magnetic Resonance Imaging, Artificial Intelligence, Deep Learning, Renal Tumor, Pathological Subtyping, Histological Grading, Multimodal Learning, Computer-Aided Diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical SciencesUniversity, hospital or other
- Collaborators
- Peking University People's Hospital, Shanxi Province Cancer Hospital, Chinese PLA General Hospital, RenJi Hospital, Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 4 Aug 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 + prospective, non-interventional study aims to develop and evaluate artificial intelligence methods for the detection, pathological subtyping, and histological grading of renal tumors using magnetic resonance imaging (MRI). Approximately 900 adult patients with available preoperative renal MRI examinations and postoperative pathological results will be included. The pathological findings will be used as the reference standard for model development and evaluation. In addition to MRI data, selected demographic, clinical, and laboratory information may be incorporated to improve model performance. The study will not change participants' diagnosis, treatment, or follow-up, and no additional examinations or interventions will be required. All study data will be de-identified before analysis. The ultimate goal is to develop an MRI-based intelligent diagnostic approach that may assist clinicians in the preoperative assessment and individualized management of patients with renal tumors.
Detailed description
Renal tumors include multiple benign and malignant pathological subtypes with substantial differences in biological behavior, treatment strategy, and prognosis. Surgical planning and clinical management are closely related to the pathological subtype and histological grade of the tumor. However, accurately determining these pathological characteristics before surgery using conventional MRI interpretation remains challenging.
This is a retrospective + prospective, observational, and non-interventional study. Adult patients with renal tumors will be identified from existing clinical records. Eligible patients will have available renal MRI examinations and corresponding pathological diagnoses, including pathological subtype and, when applicable, histological grade. Cases with unreadable MRI data or images of insufficient quality for analysis will be excluded.
Existing study data will include multisequence MRI examinations, such as T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging, when available. Demographic information, relevant clinical history, laboratory results, and radiology report information may also be collected. Pathological findings will serve as the reference standard for model training and evaluation. All data will be de-identified before processing and analysis.
The study will develop artificial intelligence models for the following tasks:
- Detection and localization of renal tumors on multisequence MRI.
- Segmentation of renal tumors and extraction of quantitative imaging features.
- Classification of common benign and malignant renal tumor subtypes.
- Identification of rare pathological subtypes using small-sample or cross-modal learning methods.
- Prediction of histological grade for malignant renal tumors.
- Integration of MRI, demographic, clinical, and laboratory information to improve pathological subtyping and grading.
The dataset will be divided into model-development and validation datasets. Additional cases collected from different time periods or participating sources may be used for independent testing. Model performance will be evaluated by comparing artificial intelligence predictions with pathological diagnoses.
This study does not assign any treatment or diagnostic intervention. It will not affect participants' routine clinical care and will not require additional imaging examinations, blood collection, surgery, medication, or follow-up visits. The study is intended to develop an intelligent MRI-based diagnostic system that may support preoperative decision-making for patients with renal tumors.
Study design
- Observational model
- Case only
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients With Renal Tumors | – | Diagnostic Test: MRI-Based Artificial Intelligence AnalysisAdult patients with pathologically confirmed renal tumors who underwent preoperative multisequence renal MRI as part of routine clinical care and had available pathological subtype and, when applicable, histological grade information. Existing de-identified MRI, pathological, clinical, and laboratory data were collected for artificial intelligence model development and evaluation. No additional examination, treatment, or study-specific intervention was administered. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This retrospective + prospective, hospital-based study includes adult patients with renal tumors who underwent preoperative renal MRI as part of routine clinical care and had corresponding pathological diagnoses. Eligible cases include benign and malignant renal tumor subtypes. Existing de-identified MRI, pathological, demographic, clinical, and laboratory data are collected for artificial intelligence model development and evaluation. No healthy volunteers are included, and no additional examinations, treatments, or study-specific follow-up are performed.
Key inclusion criteria (5 listed)
- Patients aged 18 years or older.
- Patients diagnosed with a renal tumor.
- Availability of preoperative renal magnetic resonance imaging examinations.
- Availability of a corresponding pathological diagnosis, including pathological subtype and, where applicable, histological grade.
- Magnetic resonance images that can be successfully retrieved and are of - - sufficient quality for image analysis.
Key exclusion criteria (4 listed)
- Absence of renal magnetic resonance imaging data.
- Absence of a corresponding pathological diagnosis or insufficient pathological subtype or grading information.
- Magnetic resonance images that cannot be retrieved, opened, or read.
- Poor image quality that precludes reliable image annotation or artificial intelligence analysis.
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 |
|---|---|---|
| MRI-Based Artificial Intelligence AnalysisExisting preoperative multisequence renal MRI images, including T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging when available, were retrospectively analyzed using artificial intelligence and deep learning methods. The models were developed to detect and segment renal tumors and to predict pathological subtype and histological grade. Postoperative pathological findings were used as the reference standard. No additional MRI examination or diagnostic procedure was performed for the study. | Diagnostic test | Patients With Renal Tumors |
Primary outcome
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
Time frame: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
The pathological subtype predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological diagnosis as the reference standard in the held-out test dataset. Accuracy will be calculated as the number of correctly classified renal tumors divided by the total number of renal tumors evaluated. Classification performance for individual pathological subtypes will also be summarized using sensitivity, specificity, and F1 score, where applicable.
Accuracy of MRI-Based Artificial Intelligence for Histological Grading of Malignant Renal Tumors
Time frame: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
The histological grade predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological grade as the reference standard. Histological grading will be assessed according to the four-tier World Health Organization/International Society of Urological Pathology grading system. Accuracy will be calculated as the number of malignant renal tumors with correctly predicted histological grade divided by the total number of malignant renal tumors evaluated.
2 secondary outcomes
Performance of the Artificial Intelligence Model for Renal Tumor Detection
Time frame: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Accuracy of Artificial Intelligence-Based Renal Tumor Segmentation
Time frame: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Aug 2026.
- 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.
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 Tumor and Kidney Neoplasm, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences 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 5 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: looking for participants now, as of the sponsor's last update on 4 Aug 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xiongjun Ye (010-87787170 ext. 010-87787170, yexiongjun@cicams.ac.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors (time frame: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.). 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 4 Aug 2026.