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

NCT06167863 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2023-12-13

No results posted yet for this study

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.

Conditions

  • Radiomics
  • Deep Learning
  • Artificial Intelligence
  • Body Composition

Interventions

DIAGNOSTIC_TEST

radiomics

extracted image features from CT or MRI

Sponsors & Collaborators

  • Zhen Li

    lead OTHER

Principal Investigators

  • Li Dr · Tongji Hospital

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-08-31
Primary Completion
2023-10-31
Completion
2023-10-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06167863 on ClinicalTrials.gov