Ultrasound Imaging Based on Ultrasound Bronchoscopy in Respiratory Diseases: a Retrospective, Single-center, Confirmatory Study

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

197 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-01-01

Study Completion Date

2024-04-01

Brief Summary

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ABSTRACT Background and objective: To establish a ultrasound radiomics machine learning model based on endobronchial ultrasound (EBUS)to 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 EBUS.based radiomics and dimensionality reduction was performed on thesefeatures by the least absolute shrinkage and selection operator (LASSO)EBUS-based radiomics model was established by support vector machine(SVM).205 lesions were randomly divided into a training group (n=143)and a validation group (n=62). The diagnostic efficiency was evaluated byreceiver operating characteristic (ROC).Results: 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 0.892(95% CI: 0.885-0.899), with an accuracy of 85.3%, sensitivity of 93.2%and specificity of 79.8%.In the verification group, the SVM modeldemonstrated an AUC of 0.906 (95% C: 0.890-0.923),along with anaccuracy of 74.2%,sensitivity of 70.3%, and specificity of 74.1% Conclusion:EBUS-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

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Detailed Description

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Conditions

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Benign and Malignant Lymph Nodes

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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training group

SVM model

Intervention Type DIAGNOSTIC_TEST

Bronchoscopic ultrasound images were analyzed according to SVM mode

validation group

SVM model

Intervention Type DIAGNOSTIC_TEST

Bronchoscopic ultrasound images were analyzed according to SVM mode

Interventions

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SVM model

Bronchoscopic ultrasound images were analyzed according to SVM mode

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* (1)chest CT showing enlarged mediastinal or hilar lymph nodes or positive findings of mediastinal and hilar lymph nodes on PET/CT (SUV≥2.5); (2)patients who underwent EBUS-TBNA examination; (3)no contraindications for surgery.