Lung Cancer Associated With Cystic Airspaces
Conditions
Brief summary
The goal of this observational study is to determine the most accurate tumor size measurement method for T-staging and prognostic assessment in lung cancer with cystic airspaces (LCCA). The main questions it aims to answer are: * What is the optimal T-staging approach for accurately classifying lung cancer with cystic airspaces （LCCA) and predicting patient outcomes? * How do imaging features of cystic lesions correlate with their pathological characteristics? * What is the relationship between imaging features of cystic airspace-associated lesions and patient prognosis? * Can optimizing the T-staging method improve clinical decision-making in patients with LCCA?
Interventions
None listed
Sponsors
Central South University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
1. Histologically confirmed non-small cell lung cancer (NSCLC), as verified by biopsy or postoperative pathological examination; 2. Patients who have undergone surgical lung resection; 3. Patients with complete preoperative chest CT imaging data; 4. Preoperative chest CT showing a well-defined gas-containing (air-filled) cystic component within the tumor.
Exclusion criteria
1. History of pulmonary diseases that could produce cystic lung lesions (e.g., tuberculosis, pulmonary fungal infections, bullae, emphysema, Lymphangioleiomyomatosis \[LAM\], or Birt-Hogg-Dubé \[BHD\] syndrome); 2. Systemic anti-tumor therapies, including chemotherapy, radiotherapy, or targeted therapies (such as monoclonal antibodies, small-molecule tyrosine kinase inhibitors, among others), were administered prior to enrollment; 3. Patients with concurrent other malignancies; 4. Patients with missing or poor-quality preoperative chest CT imaging data.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| DFS | From enrollment to the end of surgery for 5 years | Disease-Free Survival |
| OS | From enrollment to the end of surgery for 5 years | Overall Survival |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Rate of T-stage reclassification | From enrollment to the end of surgery for 5 years | T-stage reclassification |
| AI-based extraction of radiologic characteristics of cystic airspace-associated lesions | From enrollment to the end of surgery for 5 years | Machine learning was employed to extract and analyze the radiologic characteristics of cystic airspace-associated lesions. |
| AI to extract and analyze pathological features | From enrollment to the end of surgery for 5 years | Machine learning was employed to extract and analyze pathological features |
| Oncogenic driver genetic alterations | From enrollment to the end of surgery for 5 years | Results of driver gene mutation testing |
| Receipt of postoperative adjuvant therapy | From enrollment to the end of surgery for 5 years | Postoperative adjuvant therapy was ascertained from medical records |
Countries
China
Contacts
CONTACTChen Chen
Outcome results
None listed