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CompletedNCT06167863Updated Dec 13, 2023

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

An observational study in Radiomics, Deep Learning and Artificial Intelligence, sponsored by Zhen Li. Completed at 1 site in China. Per ClinicalTrials.gov, last updated 2023-12-13.

Sponsored by Zhen Li · Observational

Study type
Observational
Model
Cohort
Time perspective
Retrospective
Enrollment
1,000
Sex
All
01

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.

Read the detailed 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.

02

Conditions studied

  • Radiomics
  • Deep Learning
  • Artificial Intelligence
  • Body Composition

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03

In context

Kidney Neoplasms

956 studies on the registry are indexed under Kidney Neoplasms; 210 are open to participants now.

This study's enrollment of 1,000 is above the median of 218 across 216 observational studies indexed under Kidney Neoplasms.

Browse Kidney Neoplasms studies →

Lead sponsor

Zhen Li is the lead sponsor of 5 studies on the registry; 4 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

patients diagnosed with renal tumors

Inclusion criteria

  • Our hospital admits patients with renal tumors in the urology department. The diagnosis is confirmed through surgical pathology, and the patients' imaging data is obtained through contrast-enhanced Computed Tomography or Magnetic Resonance examination in the radiology department.

Exclusion criteria

Exclusion Criteria:

  • Patients who have undergone puncture, microwave, interventional therapies before the examination, or who have received chemotherapy or radiotherapy;
  • Patients with poor respiratory coordination, resulting in significant image artifacts;
  • Lesions are cystic, without discernible regions of interest, or with multiple regions of necrosis within the lesion;
  • Lesions are too small, with a diameter of less than 1cm;
  • Thin-slice imaging is not available in the CT scan.
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Study design

Observational model
Cohort
Time perspective
Retrospective
Enrollment
1,000 participants (actual)
Patient registry
No

Groups and cohorts

  • WHO ISUP grading high

    high-grade refer to Grades 3 and 4 tumours with an unfavourable prognosis

    Diagnostic Test: radiomics

  • WHO ISUP grading low

    low-grade refer to Grades 1 and 2 tumours with a promising prognosis

    Diagnostic Test: radiomics

Interventions

  • Diagnostic testradiomics

    extracted image features from CT or MRI

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What researchers measure

Primary outcomes

  1. WHO/ISUP grade pathologically

    The WHO ISUP grade of the tumor indicated in the post-operative surgical pathology report

    Time frame: 1 month

Secondary outcomes

  1. pathological T stage

    pathological T stage according to the eighth TNM system.

    Time frame: 1 month

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Study locations

1 site
  • Zhen Li
    Wuhan, Hubei 430030, China
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References and documents

Individual participant data

Plan to share: Undecided

No publications or documents are linked to this record.

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Dec 13, 2023, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT06167863
Lead sponsor
Zhen Li
Responsible party
Zhen Li (professor, Tongji Hospital) — Sponsor-investigator
First posted
Dec 13, 2023
Start date
Aug 31, 2023
Primary completion
Oct 31, 2023
Completion
Oct 31, 2023
Last update
Dec 13, 2023

Study contacts

Li Dr
principal investigator · Tongji Hospital

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Dec 2023. You cannot join it, but the record below documents what was studied.

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