Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT06062173 |
| Other study ID # |
wangyanbo |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 5, 2020 |
| Est. completion date |
December 2024 |
Study information
| Verified date |
June 2023 |
| Source |
The First Hospital of Jilin University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
In addition to kidney tumor specific factors, adherent perirenal fat is one of the most
important causes of technical complications in kidney surgery, and currently, there is a lack
of widely used non-invasive predictive models in clinical practice. In this study, a deep
learning algorithm based on CT imaging and nomogram was proposed to identify and predict the
presence of adherent perirenal fat. This study includes the construction of a prediction
model based on CT imaging and the verification of the prediction model.
Description:
Importance:
For patients with kidney tumors requiring surgical treatment, adhesive perirenal fat is a
frustrating variable that surgeons encounter during surgery, but the current image-dependent
kidney morphometric scoring system used to predict the potential difficulty of surgery
ignores this factor. Accurate preoperative prediction of perirenal fat status remains an
urgent need.
Purpose:
To determine whether radiomics features of perirenal fat derived from computed tomography
images can provide valuable information for judging perirenal fat status, develop a
prediction model based on CT radiomics combined with deep learning, and validate the
performance of the model in an independent cohort.
Design, setup and participants:
The study included one retrospective dataset and one prospective dataset from four medical
centers between January 2020 and September 2023. Kidney plain CT scan was performed in xx
adult patients with partial nephrectomy or radical nephrectomy. The training set, validation
set, and internal test set were provided by the First Hospital of Jilin University, and the
external test set was provided by the First Hospital of Siping City, Liaoyuan Central
Hospital and Dongfeng County Hospital. This diagnostic study used single-institution data
from January 2020 to May 2023 to extract imaging omics features from the perirenal fat region
(independent sample T-test, minimum absolute contraction, and selection operator logistic
regression was used to screen for the best imaging omics features). Univariate and
multivariate analyses of clinical variables in patients prior to renal surgery were performed
to determine independent predictors of adherent perirenal fat in the clinical setting.
Different classifiers were used to build prediction models using only the image-omics
features and fusion prediction models using independent clinical predictors combined with the
image-omics features. Its performance is verified in two test sets.
Main achievements and measures:
The discriminant performance of the image omics model was evaluated by the area under the
receiver operating characteristic curve and confirmed by decision curve analysis.
