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?
Study Type
OBSERVATIONAL
Enrollment
500
The Second Xiangya Hospital of Central South University
Changsha, Hunan, China
RECRUITINGDFS
Disease-Free Survival
Time frame: From enrollment to the end of surgery for 5 years
OS
Overall Survival
Time frame: From enrollment to the end of surgery for 5 years
Rate of T-stage reclassification
T-stage reclassification
Time frame: From enrollment to the end of surgery for 5 years
AI-based extraction of radiologic characteristics of cystic airspace-associated lesions
Machine learning was employed to extract and analyze the radiologic characteristics of cystic airspace-associated lesions.
Time frame: From enrollment to the end of surgery for 5 years
AI to extract and analyze pathological features
Machine learning was employed to extract and analyze pathological features
Time frame: From enrollment to the end of surgery for 5 years
Oncogenic driver genetic alterations
Results of driver gene mutation testing
Time frame: From enrollment to the end of surgery for 5 years
Receipt of postoperative adjuvant therapy
Postoperative adjuvant therapy was ascertained from medical records
Time frame: From enrollment to the end of surgery for 5 years
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