Pathology, Renal Cell Cancer, Renal Neoplasms
Conditions
Keywords
Articicial Intelligence, Renal tumors, Prediction Model, Surgery, Radiomics, Pathomics
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.
Interventions
None listed
Sponsors
Shao Pengfei
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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;
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Assessing the performance of AI models by the AUC comprehensive assessment model | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis | From enrollment to the end of 5-years' follow up | Kaplan-Meier Survival Analysis s a non-parametric statistic mainly used to figure out factors which indicate survival. |
Countries
China
Contacts
Primary ContactShao Pengfei, Professor
Backup ContactMiao Haoqi, Postgraduate
Outcome results
None listed