Peripheral Pulmonary Lesions (PPLs)
Conditions
Keywords
Mobile Cone-Beam Computed Tomography, Artificial Intelligence, Peripheral Pulmonary Lesions
Brief summary
The purpose of this study is to evaluate the clinical value of mobile 4D CBCT technology in the diagnosis and treatment of peripheral pulmonary lesions and to validate its intraoperative dynamic three-dimensional CT imaging capability.
Detailed description
This is a single-arm, single-center, prospective study in which approximately 50 patients with peripheral pulmonary lesions will undergo mobile cone-beam computed tomography (CBCT) scanning. In the field of respiratory intervention, mobile CBCT enables real-time intraoperative three-dimensional CT imaging of lesions, providing a more intuitive visualization of the positional relationship between biopsy tools and the target area, offering significant value in bronchoscopic examinations. In this study, CBCT images will be acquired from participants and reconstructed into three-dimensional models. Subsequent analysis will be conducted to evaluate the performance of mobile 4D CBCT three-dimensional reconstruction technology.
Interventions
RADIATIONMobile CBCT Three-Dimensional Scanning
Mobile CBCT Three-Dimensional Scanning
Sponsors
Shanghai Chest Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age \>18 years old. 2. Patients whose chest CT scans indicate the presence of peripheral pulmonary lesions and require further diagnosis via transbronchial lung biopsy (TBLB). 3. Lesion size: 8 mm \< longest diameter of lesion ≤ 50 mm. 4. Understands the study and has provided signed informed consent.
Exclusion criteria
1. Contraindications to bronchoscopy are present. 2. Severe cardiopulmonary dysfunction or other conditions that may increase surgical risk. 3. The investigator considers that the patient has other conditions which make them unsuitable for participation in this study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| RMSE（Root Mean Squared Error） | 1 year | RMSE (Root Mean Square Error): A commonly used metric for measuring the difference between predicted values and actual values. It is applicable in regression problems and image quality assessment. A smaller RMSE value indicates more accurate predictions, with 0 representing perfect accuracy, while a larger value indicates greater error. |
| SSIM (Structural Similarity ) | 1 year | SSIM (Structural Similarity ): A metric used to measure the similarity between two images, comparing them across three dimensions: luminance, contrast, and structure. The SSIM value ranges from \[-1, 1\], where 1 indicates that the two images are identical, and 0 or negative values indicate complete dissimilarity. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Image Quality | 1 year | Three experienced experts scored the three-dimensional CT reconstruction images based on image quality using the following criteria:4 points: Exceptionally clear images;3 points: Generally clear images with a few blurred slices;2 points: Basically clear images but with several blurred slices;1 point: Unclear images. |
| Slice Continuity | 1 year | Three experienced experts scored the three-dimensional CT reconstruction images based on slice continuity using the following criteria:4 points: All slices exhibit continuous anatomical structures;3 points: Overall continuous with minor discontinuities in a few slices;2 points: Generally continuous but with noticeable discontinuities in multiple slices;1 point: Predominantly discontinuous across most slices. |
Countries
China
Contacts
Primary ContactJiayuan Sun
Outcome results
None listed