Lung Cancer Associated With Cystic AirspacesNCT07820423
Multimodal AI Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
An observational study of Lung Cancer Associated With Cystic Airspaces, run by Central South University at 1 site in China.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer Associated With Cystic Airspaces
- How many people
- 600 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2025to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 1. Patients with non-small cell lung cancer (NSCLC) confirmed by biopsy 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.
Who cannot join
- 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 malignancies. 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Xiangya Hospital of Central South University
Changsha, Hunan, 410000
Xue He, 13755171445, hexuehuxi@csu.edu.cn
About this study
Official title: Interpretable Multimodal Artificial Intelligence Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
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?
Read the detailed description
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.
- Keywords
- lung cancer associated with cystic airspaces;, artificial intelligence, computed tomography
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Group 1 | Low-risk pathology group | |
| Group 2 | High-risk pathology group |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Within 30 days after surgery
Secondary outcomes (1)
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.
Measured: Within 30 days after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Sep 2026
- Study start1 May 2025
- Primary completion30 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung Cancer Associated With Cystic AirspacesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Central South University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chen Chen, Central South University
- Investigators
- Chen Chen, M.D., Study director, Second Xiangya Hospital of Central South University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07820423
- Sponsor's protocol ID
- LYEC2026-K0198
- Status last verified
- Sep 2026
- First submitted
- 3 Sep 2026
- Last updated
- 15 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Interpretable Multimodal Artificial Intelligence Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
- ClinicalTrials.gov ID
- NCT07820423
- Acronym / sponsor ID
- LYEC2026-K0198
- Keywords
- lung cancer associated with cystic airspaces;, artificial intelligence, computed tomography
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Central South UniversityUniversity, hospital or other
- Study start
- 1 May 2025 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 15 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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?
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group 1 | – | –Low-risk pathology group |
| Group 2 | – | –High-risk pathology group |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (4 listed)
- 1. Patients with non-small cell lung cancer (NSCLC) confirmed by biopsy 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.
Key exclusion criteria (5 listed)
- 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 malignancies. 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Predictive performance of the AI-based multimodal radiomics model for pathological high-risk features in LCCA
Time frame: Within 30 days after surgery
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.
1 secondary outcome
Area under the receiver operating characteristic curve of the multimodal, CT imaging, and tabular models for pathological high-risk features in LCCA
Time frame: Within 30 days after surgery
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Sep 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Chen Chen, M.D. study director
Second Xiangya Hospital of Central South University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer Associated With Cystic Airspaces, run by Central South University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Chen Prof. (+8673185295188, chenchen1981412@csu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive performance of the AI-based multimodal radiomics model for pathological high-risk features in LCCA (time frame: Within 30 days after surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Sep 2026.