Research on New Diagnosis and Treatment Technologies for Early Lung Cancer

Jisong Zhang

Status: Completed

Conditions: Lung Cancer, Peripheral Pulmonary Nodules

Treatments: Diagnostic Test: Preoperative navigation path planning using the VBN navigation system; Diagnostic Test: Preoperative navigation path planning using the SARS-pro navigation system

Study type: Interventional

Funder types: Other

Identifiers

NCT07000721

SirRRSH2024-1000

Details and patient eligibility

About

To verify the clinical effectiveness and safety of the airway tree navigation system constructed by artificial intelligence (AI) in the navigation diagnosis of peripheral pulmonary nodules (PPLs).

Full description

Early diagnosis and treatment of lung cancer is of great significance, in which navigated tracheoscopic biopsy is an important tool for confirming the diagnosis of early lung cancer. Conventional navigation software realizes airway reconstruction and guides biopsy by recognizing differences in HU values on computed tomography scans. It is difficult for conventional navigation software to recognize the reconstruction due to the special characteristics of small airways that are susceptible to interference and collapse. Therefore, an AI deep learning approach can realize accurate construction of small airways and guide accurate biopsy. This study intends to validate the clinical effectiveness and safety of the AI-constructed airway tree navigation system in the navigational diagnosis of peripheral pulmonary nodules (PPLs).

Enrollment: 92 patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Aged 18 years or above.
2. Patients with one or more peripheral lung nodules suspected to be lung cancer or poorly absorbing lesions on conventional anti-infective therapy.
3. Patients with nodule diameters ≤30 mm (diameters mentioned in the text are the average of the maximum and minimum diameters).
4. The nodules were pure ground glass nodules, partially solid nodules, or solid nodules.
5. The nodule is surrounded by lung parenchyma and is not visible in the bronchial lumen above the segment.

Exclusion criteria

1. Preoperative judgment that it is difficult for the patient to benefit from bronchoscopic biopsy (e.g., high risk of bleeding due to perivascular encasement of the lesion, difficulty in reaching the airway adjacent to the lesion due to previous lung surgery, etc.).
2. Those with incomplete clinical data.
3. Those with missing visits after biopsy.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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92 participants in 2 patient groups

New navigation system group

Experimental group

Description:

The new AI-constructed airway tree navigation system (SARS-pro) was used for preoperative navigation path planning. The SARS-pro navigation system was independently developed by the research group based on a conventional augmented reality optical navigation system (LungPro; Bronchus Company).

Treatment:

Diagnostic Test: Preoperative navigation path planning using the SARS-pro navigation system

Old navigation system group

Active Comparator group

Description:

Preoperative navigation path planning was performed using the old VBN system (LungPro; Bronchus Company).

Treatment:

Diagnostic Test: Preoperative navigation path planning using the VBN navigation system