Clinical trial NCT06714916: Optimising Renal Tumour Management Through Artificial Intelligence Modules
Official title: Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Renal Neoplasms
Pathology
Renal Cell Cancer
Keywords: Articicial Intelligence
Renal tumors
Prediction Model
Surgery
Radiomics
Pathomics
Lead sponsor: The First Affiliated Hospital with Nanjing Medical University
Sponsor class: OTHER
First posted: 2024-12-04
Enrollment: 2100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with renal tumors on imaging examinations who underwent surgery

Brief 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. 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.
2. whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor.
3. 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.

Eligibility criteria: Inclusion Criteria:

* 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;

Exclusion Criteria:

* 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;

Primary outcomes:
measure: Assessing the performance of AI models by the "AUC" comprehensive assessment model
timeFrame: From enrollment to the end of 5-years' follow up
Secondary outcomes:
measure: Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis
timeFrame: From enrollment to the end of 5-years' follow up
Other outcomes:
Locations:
city: Nanjing
country: China
facility: The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
state: Jiangsu
Study officials:
References: