Renal NeoplasmsNCT06714916
Optimising Renal Tumour Management Through Artificial Intelligence Modules
An observational study of Renal Neoplasms and Pathology, run by Shao Pengfei at 1 site in China.
Trial summary
The goal of this observational study is to improve the management of people with renal tumour by multimodal artificial intelligence(AI). It will also measure the accuracy of the predictions from AI models. The main questions it aims to answer are: 1. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Renal Neoplasms, Pathology, Renal Cell Cancer
- How many people
- 2,100 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2025to 31 Dec 2033
- How long will I take part?
- 5 Years
- 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: Patients with renal tumors on imaging examinations who underwent surgery
Who may be able to join
- Patients with renal tumor which can be treated by surgery;
- Complete CECT within 30 days before surgery;
- Patients who fully understand this study and sign the informed consent;
Who cannot join
- Patients with any item missing from the baseline clinical and pathological information;
- Patients who has already metastasized by the time the tumor is discovered;
- Previous treatment in any form, including surgery, targeted therapy and immunotherapy;
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
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu, 210036
Shao Pengfei, Professor, +8613851925825, spf032@hotmail.com
Miao Haoqi, Postgraduate, +8613276636957, mhq@stu.njmu.edu.cn
About this study
Official title: Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis
The goal of this observational study is to improve the management of people with renal tumour by multimodal artificial intelligence(AI). It will also measure the accuracy of the predictions from AI models. The main questions it aims to answer are:
- whether the AI module can accurately provide tumor-related information such as Benign or malignant, subtypes, grading, stage, etc. by learning from preoperative CT images.
- whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor.
- whether the AI module can integrate CT images and pathology slides, offering supplementary prognostic information to improve postoperative survival.
Participants who complete a CT(usually Contrast-enhanced CT, CECT) examination and undergo radical or partial nephrectomy will carry out active surveillance and record postoperative survival data for 5 years.
Read the detailed description
In this study, AI model will explore and clarify features in renal tumor CT images and pathological images that are difficult to detect manually, and then correlate them with clinical outcomes, thereby improving the diagnosis and treatment process for renal tumors. Firstly, the model can accurately distinguish renal tumor subtypes and predict stage, grade, and complexity so as to svoid misdiagnosis and assist clinicians in formulating treatment plans. Secondly, by learning from surgical videos, the model can provide additional information during surgerys, such as important anatomical landmarks, location of tumors. Finally, combining radiomics and pathomics, the model can differentiate between high-risk and low-risk patients after surgery, thus providing personalized prognostic guidance.
- Conditions
- Renal Neoplasms, Pathology, Renal Cell Cancer
- Keywords
- Articicial Intelligence, Renal tumors, Prediction Model, Surgery, Radiomics, Pathomics
- Medical subject headings
- Kidney Neoplasms, Carcinoma, Renal Cell
What is being measured
The primary outcome is the main question the study is designed to answer.
Assessing the performance of AI models by the "AUC" comprehensive assessment model
"AUC" refers to the area under the ROC (Receiver Operating Characteristic) curve, which indicates the performance of the model in predicting immunohistochemistry-related pathological information of prostate cancer after surgery, and the AUC ranges from 0-1, with the larger value indicating the better prediction effect.
Measured: From enrollment to the end of 5-years' follow up
Secondary outcomes (1)
Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis
Kaplan-Meier Survival Analysis s a non-parametric statistic mainly used to figure out factors which indicate survival.
Measured: From enrollment to the end of 5-years' follow up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Dec 2024
- Study start1 Jan 2025
- Primary completion1 Jan 2028 (estimated)
- Study completion31 Dec 2033 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Renal NeoplasmsStudies 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
- Prospective
- Enrolment
- 2,100 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- Shao Pengfei
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Shao Pengfei, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06714916
- Sponsor's protocol ID
- 2024-SR-961
- Status last verified
- Dec 2024
- First submitted
- 2 Dec 2024
- Last updated
- 19 Mar 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis
- ClinicalTrials.gov ID
- NCT06714916
- Acronym / sponsor ID
- 2024-SR-961
- Conditions
- Renal Neoplasms, Pathology, Renal Cell Cancer
- Keywords
- Articicial Intelligence, Renal tumors, Prediction Model, Surgery, Radiomics, Pathomics
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,100 participants (estimated)
- Lead sponsor
- Shao PengfeiUniversity, hospital or other
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 1 Jan 2028 (estimated)
- Study completion
- 31 Dec 2033 (estimated)
- First posted
- 4 Dec 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.
The goal of this observational study is to improve the management of people with renal tumour by multimodal artificial intelligence(AI). It will also measure the accuracy of the predictions from AI models. The main questions it aims to answer are:
- whether the AI module can accurately provide tumor-related information such as Benign or malignant, subtypes, grading, stage, etc. by learning from preoperative CT images.
- whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor.
- whether the AI module can integrate CT images and pathology slides, offering supplementary prognostic information to improve postoperative survival.
Participants who complete a CT(usually Contrast-enhanced CT, CECT) examination and undergo radical or partial nephrectomy will carry out active surveillance and record postoperative survival data for 5 years.
Detailed description
In this study, AI model will explore and clarify features in renal tumor CT images and pathological images that are difficult to detect manually, and then correlate them with clinical outcomes, thereby improving the diagnosis and treatment process for renal tumors. Firstly, the model can accurately distinguish renal tumor subtypes and predict stage, grade, and complexity so as to svoid misdiagnosis and assist clinicians in formulating treatment plans. Secondly, by learning from surgical videos, the model can provide additional information during surgerys, such as important anatomical landmarks, location of tumors. Finally, combining radiomics and pathomics, the model can differentiate between high-risk and low-risk patients after surgery, thus providing personalized prognostic guidance.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with renal tumors on imaging examinations who underwent surgery
Key inclusion criteria (3 listed)
- Patients with renal tumor which can be treated by surgery;
- Complete CECT within 30 days before surgery;
- Patients who fully understand this study and sign the informed consent;
Key exclusion criteria (3 listed)
- Patients with any item missing from the baseline clinical and pathological information;
- Patients who has already metastasized by the time the tumor is discovered;
- Previous treatment in any form, including surgery, targeted therapy and immunotherapy;
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
Assessing the performance of AI models by the "AUC" comprehensive assessment model
Time frame: From enrollment to the end of 5-years' follow up
"AUC" refers to the area under the ROC (Receiver Operating Characteristic) curve, which indicates the performance of the model in predicting immunohistochemistry-related pathological information of prostate cancer after surgery, and the AUC ranges from 0-1, with the larger value indicating the better prediction effect.
1 secondary outcome
Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis
Time frame: From enrollment to the end of 5-years' follow up
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 19 Mar 2025.
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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 Neoplasms and Pathology, run by Shao Pengfei 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 3 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 19 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?
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 Shao Pengfei, Professor (+8613851925825, spf032@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Assessing the performance of AI models by the "AUC" comprehensive assessment model (time frame: From enrollment to the end of 5-years' follow up). 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 19 Mar 2025.