An observational study in Lung Cancer Associated With Cystic Airspaces, sponsored by Central South University. Recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-15.
Sponsored by Central South University · Observational
The goal of this observational study is to develop and validate an artificial intelligence (AI)-based multimodal radiomics model that integrates preoperative CT imaging features and clinical data to predict pathological high-risk features in patients with lung cancer associated with cystic airspaces (LCCA).
The main questions it aims to answer are:
Can an AI-based multimodal radiomics model accurately predict pathological high-risk features in LCCA before surgery? Does the integration of CT imaging features and clinical variables improve preoperative risk stratification compared with imaging or clinical information alone?
Lung cancer associated with cystic airspaces (LCCA) is an uncommon radiological presentation of lung cancer characterized by cystic or air-containing spaces with adjacent or surrounding abnormal lung tissue. Although many LCCA lesions are detected at an early clinical stage, their pathological behavior can vary considerably. Some tumors may contain high-risk pathological features associated with greater invasive potential and a less favorable prognosis. Identifying these features before surgery may therefore help improve individualized surgical planning and clinical decision-making.
This observational study aims to develop and validate an artificial intelligence (AI)-based multimodal model for the preoperative prediction of pathological high-risk features in patients with LCCA. The model will integrate information derived from preoperative chest computed tomography (CT) images with routinely available clinical and radiological variables.
Eligible patients with surgically resected LCCA will be included in the study. Preoperative CT images will be analyzed using AI-based imaging methods to extract tumor-related imaging information. Clinical and radiological characteristics available before surgery will also be collected. These data will be integrated to construct a multimodal prediction model.
The reference standard will be the final postoperative pathological examination of the resected tumor. Pathological high-risk features will include predefined histological and invasive characteristics associated with more aggressive tumor behavior. The ability of the multimodal model to predict these pathological features before surgery will be evaluated using measures including the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
The performance of the multimodal model will also be compared with models based on CT imaging information alone and clinical or tabular information alone to determine whether combining multiple sources of preoperative information provides additional predictive value. Model interpretation methods will be used to identify imaging regions and clinical variables that contribute to individual predictions, thereby improving the transparency and clinical interpretability of the AI model.
The study includes model development and independent evaluation. The overall objective is to determine whether multimodal AI can provide a noninvasive and clinically interpretable approach for identifying patients with LCCA who are more likely to harbor pathological high-risk features before surgical treatment.
5,422 studies on the registry are indexed under Lung Neoplasms; 1,346 are open to participants now.
This study's planned enrollment of 600 is above the median of 200 across 1,434 observational studies indexed under Lung Neoplasms.
Browse Lung Neoplasms studies →Central South University is the lead sponsor of 134 studies on the registry; 32 are open to participants now.
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This observational study will include patients with pathologically confirmed non-small cell lung cancer (NSCLC) who underwent pulmonary tumor resection and had preoperative CT features consistent with lung cancer associated with cystic airspaces (LCCA). Patients with complete imaging, clinical, and pathological data will be included for model development and validation.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
1. Patients with non-small cell lung cancer (NSCLC) confirmed by or postoperative pathological examination.
2. Patients who underwent surgical resection of a pulmonary tumor, including lobectomy, segmentectomy, or wedge resection.
3. Patients with complete preoperative chest CT imaging data. 4. Patients whose preoperative chest CT showed a well-defined air-containing cystic component within the tumor, consistent with the radiological features of lung cancer associated with cystic airspaces (LCCA).
5. Patients with available clinical and pathological data required for analysis.
Exclusion Criteria:
1. Patients with a history of pulmonary diseases that may cause cystic lung lesions, such as pulmonary tuberculosis, pulmonary fungal infection, lymphangioleiomyomatosis (LAM), or Birt-Hogg-Dubé (BHD) syndrome. Emphysema will not be considered an exclusion criterion; however, patients with severe emphysema will be excluded if it significantly affects the identification, boundary delineation, or imaging feature assessment of the target lesion.
2. Patients who received systemic antitumor therapy before enrollment, including chemotherapy, radiotherapy, targeted therapy, or immunotherapy.
3. Patients with other primary . 4. Patients with missing preoperative chest CT images or CT images of insufficient quality for analysis.
5. Patients without a definite pathological diagnosis or with incomplete pathological results.
6. Patients with missing clinical data.
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.
Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Biopsy and archival tissue
A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.
Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Cancer (malignancy)
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Low-risk pathology group
High-risk pathology group
Predictive performance of the AI-based multimodal radiomics model for pathological high-risk features in LCCA
Pathological high-risk features will be determined based on the final postoperative pathological examination. The predictive performance of the AI-based multimodal model will be quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
Time frame: Within 30 days after surgery
Area under the receiver operating characteristic curve of the multimodal, CT imaging, and tabular models for pathological high-risk features in LCCA
The predictive performance of the AI-based multimodal model will be compared with that of the CT imaging model and the tabular model for predicting pathological high-risk features. Model performance will be quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Differences in AUC between models will be evaluated using DeLong's test.
Time frame: Within 30 days after surgery
Started May 2025 (actual). Primary completion is estimated for Dec 2026, 19 months after the start.
Of 591 non-industry observational studies under Lung Neoplasms, half reached primary completion within 65 months of starting (the median), counting those still running; a quarter within 25 and three quarters within 162. This study's planned 19 months is below that.
Counting every sponsor, half of 681 studies reached it within 57 months; a quarter within 25 and three quarters within 135.
How these figures are worked out, and which studies are left out of them.
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