Retrospective Analysis of the Correlation Between Imaging Features and Pathology, Prognosis in Renal Tumors

Artificial Intelligence Clinical Trial

Official title: Retrospective Analysis of the Correlation Between CT/MRI Imaging Features and Pathology, Prognosis in Patients With Renal Tumors

Status Completed

Clinical 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. 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Artificial Intelligence
• Body Composition
• Deep Learning
• Kidney Neoplasms
• Radiomics

• NCT number: NCT06167863
• Study type: Observational
• Source: Tongji Hospital
• Status: Completed
• Start date: August 31, 2023
• Completion date: October 31, 2023