Ignite Creation Date:
2024-05-06 @ 7:36 PM
Last Modification Date:
2024-10-26 @ 3:09 PM
Study NCT ID:
NCT06062173
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-01-03
First Post:
2023-08-29
Brief Title:
Preoperative Prediction of Adherent Perirenal Fat
Sponsor:
The First Hospital of Jilin University
Organization:
The First Hospital of Jilin University
Study Overview
Official Title:
Preoperative Prediction of Adherent Perirenal Fat Based on CT Radiomics Combined With Deep Learning a Prospective Multicenter Study
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2023-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
APF
Brief 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
Detailed 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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None