Staging Strategies and Their Association With Prognosis and Therapy in Lung Cancer With Cystic Airspaces

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorCentral South University

About this trial

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?

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Histologically confirmed non-small cell lung cancer (NSCLC), as verified by biopsy or postoperative pathological examination;

Patients who have undergone surgical lung resection;

Patients with complete preoperative chest CT imaging data;

Preoperative chest CT showing a well-defined gas-containing (air-filled) cystic component within the tumor.

Disqualifiers

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);

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;

Patients with concurrent other malignancies;

Patients with missing or poor-quality preoperative chest CT imaging data.

Trial population

Patients with LCCA who underwent surgical treatment in the Second Xiangya Hospital of Central South University

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

500 Participants
are grouped into 3 trial groups
Group A: group 1
Group B: group 2
Group C: group 3

Trial outcomes

Primary outcomes

1

DFS

Disease-Free Survival

Time frame
From enrollment to the end of surgery for 5 years
2

OS

Overall Survival

Time frame
From enrollment to the end of surgery for 5 years

Secondary outcomes

1

Rate of T-stage reclassification

T-stage reclassification

Time frame
From enrollment to the end of surgery for 5 years
2

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
3

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
4

Oncogenic driver genetic alterations

Results of driver gene mutation testing

Time frame
From enrollment to the end of surgery for 5 years

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Central South University

Lead sponsor

Second Xiangya Hospital of Central South University

Sponsor institution