NCT06126159

Brief Summary

The knowledge of the histological diagnosis and its subtype of a renal parenchymal tumor is important for determine whether the choice of a specific regimen of chemotherapy, target therapy and immunotherapy could be suitable and effective for treating this tumor. Computed tomography (CT) has been considered as an excellent imaging modality for detecting intra-tumoral fat, and most of renal angiomyolipomas (AML) could be thus confidently diagnosed on computed tomography by showing intra-tumoral fat. However, if a renal parenchymal tumor has no detectable fat in the tumor on computed tomography, there is a long list of its diagnosis including benign neoplasms as angiomyolipoma with minimal fat, oncocytoma, metanephric adenoma, etc., epitheloid angiomyolipoma (eAML) malignant potential, malignant neoplasms as renal cell carcinoma (RCC), sarcoma, malignant eAML, etc. Furthermore, there are three kinds of anticancer drug (antiangiogenetic drug, mammalian target of rapamycin inhibitors, immune modulators, and whether the anticancer drug is effective mainly depending on subtypes of RCCs. Nonetheless, computed tomography could not reliably differentiate histological types of renal parenchymal masses except renal AMLs with abundant fat. Therefore, for patients without established diagnoses by imaging examinations, further biopsy of the renal tumor is usually mandatory to validate the histological diagnosis and subtype. Thus, this study plans to enroll 60 patients with renal parenchymal masses which show no intra-tumoral fat on computed tomography. All enrolled patients will undergo multiparametric and fat-detection magnetic resonance imaging (MRI).

Trial Health

75
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
44

participants targeted

Target at P25-P50 for not_applicable

Timeline
8mo left

Started Feb 2019

Longer than P75 for not_applicable

Geographic Reach
1 country

1 active site

Status
active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress92%
Feb 2019Dec 2026

Study Start

First participant enrolled

February 11, 2019

Completed
4.7 years until next milestone

First Submitted

Initial submission to the registry

November 6, 2023

Completed
7 days until next milestone

First Posted

Study publicly available on registry

November 13, 2023

Completed
3.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2026

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2026

Last Updated

March 20, 2026

Status Verified

March 1, 2026

Enrollment Period

7.9 years

First QC Date

November 6, 2023

Last Update Submit

March 18, 2026

Conditions

Keywords

kidneyangiomyolipomarenal cell carcinomaimaging characteristicsmagnetic resonance imagingimmunohistochemistry stain

Outcome Measures

Primary Outcomes (3)

  • MR characteristics assessment- T2WI

    T2-weighted images (T2WI)

    3 years

  • MR characteristics assessment- ADC

    Apparent diffusion coefficient (ADC)

    3 years

  • MR characteristics assessment- IVIM

    Intravoxel incoherent motion (IVIM)

    3 years

Secondary Outcomes (6)

  • Immunohistochemistry (IHC) statin- mTOR

    3 years

  • Immunohistochemistry (IHC) statin- Phospho-mTOR

    3 years

  • Immunohistochemistry (IHC) statin- Rheb

    3 years

  • Immunohistochemistry (IHC) statin- S6K

    3 years

  • Immunohistochemistry (IHC) statin- pS6K

    3 years

  • +1 more secondary outcomes

Study Arms (1)

multiparametric and fat-detection magnetic resonance imaging (MRI)

EXPERIMENTAL

detecting the small amount of fat with the use of fat-detecting pulse sequences on MRI

Diagnostic Test: multiparametric and fat-detection magnetic resonance imaging (MRI)

Interventions

Differentiating of renal AMLs with minimal fat and RCCs

multiparametric and fat-detection magnetic resonance imaging (MRI)

Eligibility Criteria

Age20 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Age ≥ 20 years old
  • Have renal parenchymal masses with no detectable intra-tumoral fat on computed tomography (CT)
  • Normal renal function (i.e.: estimated glomerular filtration rate ≧ 60 mL/min/1.73 m2)
  • No allergy history of iodinated contrast medium

You may not qualify if:

  • Pregnant or lactating woman
  • Withdrawal of informed consent
  • Those who have not completed MRI
  • Those who did not receive renal tumor biopsy

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Li-Jen Wang

Taoyuan District, Taiwan, 333, Taiwan

Location

MeSH Terms

Conditions

Kidney NeoplasmsAngiomyolipomaCarcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Urologic NeoplasmsUrogenital NeoplasmsNeoplasms by SiteNeoplasmsFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesKidney DiseasesUrologic DiseasesMale Urogenital DiseasesNeoplasms, Adipose TissueNeoplasms, Connective and Soft TissueNeoplasms by Histologic TypePerivascular Epithelioid Cell NeoplasmsAdenocarcinomaCarcinomaNeoplasms, Glandular and Epithelial

Study Officials

  • Li-Jen Wang, M.D., M.P.H.

    Chang Gung Memorial Hospital

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
OTHER
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Medical Imaging Department Director

Study Record Dates

First Submitted

November 6, 2023

First Posted

November 13, 2023

Study Start

February 11, 2019

Primary Completion (Estimated)

December 31, 2026

Study Completion (Estimated)

December 31, 2026

Last Updated

March 20, 2026

Record last verified: 2026-03

Data Sharing

IPD Sharing
Will not share

Plan to make individual participant data

Locations