Ignite Creation Date:
2024-05-06 @ 8:22 PM
Last Modification Date:
2024-10-26 @ 3:26 PM
Study NCT ID:
NCT06351319
Status:
COMPLETED
Last Update Posted:
2024-05-24
First Post:
2024-04-02
Brief Title:
Ultrasound Imaging Based on Ultrasound Bronchoscopy in Respiratory Diseases a Retrospective Single-center Confirmatory Study
Sponsor:
Quncheng Zhang
Organization:
Henan Provincial Peoples Hospital
Study Overview
Official Title:
Ultrasound Imaging Based on Ultrasound Bronchoscopy in Respiratory Diseases a Retrospective Single-center Confirmatory Study
Status:
COMPLETED
Status Verified Date:
2024-05
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:
None
Brief Summary:
ABSTRACT Background and objective To establish a ultrasound radiomics machine learning model based on endobronchial ultrasound EBUSto assistdoctors in distinguishing between benign and malignant diagnoses ofmediastinal and hilar lymph nodes
Methods The clinical and ultrasonic image data of 197 patients wereretrospectively analyzed The radiomics features were extracted by EBUSbased radiomics and dimensionality reduction was performed on thesefeatures by the least absolute shrinkage and selection operator LASSOEBUS-based radiomics model was established by support vector machineSVM205 lesions were randomly divided into a training group n143and a validation group n62 The diagnostic efficiency was evaluated byreceiver operating characteristic ROCResults A total of 13 stable features with non-zero coefficients wereselected The support vector machine SV model exhibited promisingperformance in both the training and verification groups In the traininggroup the SVM model achieved an area under the curve AUC of 089295 CI 0885-0899 with an accuracy of 853 sensitivity of 932and specificity of 798In the verification group the SVM modeldemonstrated an AUC of 0906 95 C 0890-0923along with anaccuracy of 742sensitivity of 703 and specificity of 741 ConclusionEBUS-based radiomics model can be used to differentiatemediastinal and hilar benign and malignant lymph nodes The SVM modeldemonstrates superiority and holds potential as a diagnostic tool in clinical practice
Methods The clinical and ultrasonic image data of 197 patients wereretrospectively analyzed The radiomics features were extracted by EBUSbased radiomics and dimensionality reduction was performed on thesefeatures by the least absolute shrinkage and selection operator LASSOEBUS-based radiomics model was established by support vector machineSVM205 lesions were randomly divided into a training group n143and a validation group n62 The diagnostic efficiency was evaluated byreceiver operating characteristic ROCResults A total of 13 stable features with non-zero coefficients wereselected The support vector machine SV model exhibited promisingperformance in both the training and verification groups In the traininggroup the SVM model achieved an area under the curve AUC of 089295 CI 0885-0899 with an accuracy of 853 sensitivity of 932and specificity of 798In the verification group the SVM modeldemonstrated an AUC of 0906 95 C 0890-0923along with anaccuracy of 742sensitivity of 703 and specificity of 741 ConclusionEBUS-based radiomics model can be used to differentiatemediastinal and hilar benign and malignant lymph nodes The SVM modeldemonstrates superiority and holds potential as a diagnostic tool in clinical practice
Detailed Description:
None
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