Lung CancerNCT06771947
Predicting Symptom Trajectories After Thoracoscopic Lung Cancer Surgery Using an Interpretable Machine Learning Model
An observational study of Lung Cancer, run by Guangdong Provincial People's Hospital at 1 site in China.
Trial summary
Patients suffer from a variety of symptoms after thoracoscopic surgery. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Lung Cancer
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2025to 1 Feb 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Lung cancer patient underwent thoracoscopic surgery and completed the MD Anderson Symptom Inventory-Lung Cancer module postoperatively.
Who may be able to join
- Age 18-80 years old;
- Pathologically diagnosed lung cancer;
- Undergo thoracoscopic surgery, including video-assisted thoracoscopy and robotic-assisted thoracoscopic surgery;
- no prior history of malignancy or lung surgery;
- Have the ability to complete the scale.
Who cannot join
- Converted to thoracotomy during thoracoscopic surgery;
- Unable to complete the postoperative scale at least two times;
- Missing data values exceeding 30 percent.
Trial locations and contacts
1 site in 1 country.
China1 site
Guangdong Provincial People's Hospital
Guangdong, 510080
Guibin Qiao, 13602749153, guibinqiao@126.com
Zijie Li, 13433747658, zijie_li10@163.com
About this study
Patients suffer from a variety of symptoms after thoracoscopic surgery. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Latent class mixed modeling (LCMM) will be used to dentify subgroups of patients with similar symptom trajectories. Machine learning models were developed to predict postoperative symptom trajectories based on collected information. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.
Read the detailed description
Thoracoscopic lung cancer surgery is a widely utilized approach for treating early and locally advanced lung cancer. Despite the advantages of thoracoscopic surgery, such as minimal invasion and rapid recovery, patients still suffer from a variety of symptoms such as pain, shortness of breath, sleep disorders or fatigue after surgery, which seriously affects the quality of life. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Patients are invited to fill out the MD Anderson Symptom Inventory-Lung Cancer module after thoracoscopic surgery. Symptoms of interest include pain, shortness of breath, sleep disturbance, and fatigue. Moderate to severe symptoms were defined as a score of ≥ 4. Latent class mixed modeling (LCMM), a clustering technique, can identify subgroups of patients with similar symptom trajectories based on longitudinal patient-reported outcome (PRO) data. Machine learning models were developed to predict postoperative symptom trajectories based on collected information including demographic and clinical information, and operative data. The machine learning models mainly include Random Forest, Support Vector Machines, Neural Networks, XGBoost, etc. The most appropriate model is selected, and model interpretation is performed using the SHAP method. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.
- Conditions
- Lung Cancer
- Keywords
- Lung cancer, Machine learning, Patient-reported outcome, Thoracoscopy surgery
- Medical subject headings
- Lung Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Symptom trajectories after thoracoscopic surgery
Postoperative symptoms of interest included pain, shortness of breath, sleep disturbances, and fatigue.
Measured: Within four weeks after thoracoscopic surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Jan 2025
- Study start1 Mar 2025 (estimated)
- Primary completion1 Jan 2026 (estimated)
- Study completion1 Feb 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Guangdong Provincial People's HospitalThe sponsor's other registered studies.
- 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
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guangdong Provincial People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, GuiBin Qiao, Guangdong Provincial People's Hospital
- Investigators
- Guibin Qiao, Principal investigator, Guangdong Provincial People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06771947
- Sponsor's protocol ID
- LC-ML
- Status last verified
- Apr 2024
- First submitted
- 8 Jan 2025
- Last updated
- 13 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Symptom Trajectories After Thoracoscopic Lung Cancer Surgery Using an Interpretable Machine Learning Model
- ClinicalTrials.gov ID
- NCT06771947
- Acronym / sponsor ID
- LC-ML
- Conditions
- Lung Cancer
- Keywords
- Lung cancer, Machine learning, Patient-reported outcome, Thoracoscopy surgery
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Guangdong Provincial People's HospitalUniversity, hospital or other
- Study start
- 1 Mar 2025 (estimated)
- Primary completion
- 1 Jan 2026 (estimated)
- Study completion
- 1 Feb 2026 (estimated)
- First posted
- 13 Jan 2025 (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.
Patients suffer from a variety of symptoms after thoracoscopic surgery. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Latent class mixed modeling (LCMM) will be used to dentify subgroups of patients with similar symptom trajectories. Machine learning models were developed to predict postoperative symptom trajectories based on collected information. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.
Detailed description
Thoracoscopic lung cancer surgery is a widely utilized approach for treating early and locally advanced lung cancer. Despite the advantages of thoracoscopic surgery, such as minimal invasion and rapid recovery, patients still suffer from a variety of symptoms such as pain, shortness of breath, sleep disorders or fatigue after surgery, which seriously affects the quality of life. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Patients are invited to fill out the MD Anderson Symptom Inventory-Lung Cancer module after thoracoscopic surgery. Symptoms of interest include pain, shortness of breath, sleep disturbance, and fatigue. Moderate to severe symptoms were defined as a score of ≥ 4. Latent class mixed modeling (LCMM), a clustering technique, can identify subgroups of patients with similar symptom trajectories based on longitudinal patient-reported outcome (PRO) data. Machine learning models were developed to predict postoperative symptom trajectories based on collected information including demographic and clinical information, and operative data. The machine learning models mainly include Random Forest, Support Vector Machines, Neural Networks, XGBoost, etc. The most appropriate model is selected, and model interpretation is performed using the SHAP method. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Lung cancer patient underwent thoracoscopic surgery and completed the MD Anderson Symptom Inventory-Lung Cancer module postoperatively.
Key inclusion criteria (5 listed)
- Age 18-80 years old;
- Pathologically diagnosed lung cancer;
- Undergo thoracoscopic surgery, including video-assisted thoracoscopy and robotic-assisted thoracoscopic surgery;
- no prior history of malignancy or lung surgery;
- Have the ability to complete the scale.
Key exclusion criteria (3 listed)
- Converted to thoracotomy during thoracoscopic surgery;
- Unable to complete the postoperative scale at least two times;
- Missing data values exceeding 30 percent.
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
Symptom trajectories after thoracoscopic surgery
Time frame: Within four weeks after thoracoscopic surgery
Postoperative symptoms of interest included pain, shortness of breath, sleep disturbances, and fatigue.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jan 2025.
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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. This study is not recruiting at the moment.
Guibin Qiao principal investigator
Guangdong Provincial People's Hospital
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, run by Guangdong Provincial People's Hospital 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 5 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 13 Jan 2025. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Guibin Qiao, MD (13602749153, guibinqiao@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Symptom trajectories after thoracoscopic surgery (time frame: Within four weeks after thoracoscopic 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 13 Jan 2025.