Lung CancerNCT06685458
Constructing a Predictive Model for Differentiating Between Benign and Malignant Solid Pulmonary Nodules Based on Clinical and Imaging Features.
An observational study of Lung Cancer and Solid Pulmonary Nodules, run by The Third Affiliated Hospital of Kunming Medical College..
Trial summary
Study Objective: To comprehensively analyze the preoperative clinical and imaging characteristics of solid pulmonary nodules, investigate the risk factors associated with malignant solid pulmonary nodules, and provide a reference for preoperative treatment decisions. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Solid Pulmonary Nodules, Pulmonary Nodules
- What is being tested?
- Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
- How many people
- 320 participants
- Where
- No sites listed yet
- Study dates
- 15 Nov 2024to 20 Feb 2025
- 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: The study population includes patients aged 18 years and older with radiologically diagnosed solid pulmonary nodules. These patients are undergoing preoperative clinical and imaging evaluations and subsequent surgical resection to confirm the benign or malignant nature of the nodules
Who may be able to join
- (1) All subjects provided CT imaging obtained from the Third Affiliated Hospital of Kunming Medical University within 2-week period prior to surgery; (2) Complete clinicopathological data of solid nodules were obtained; (3) Surgical intervention for one or more SPN; (4) No prior anti-tumor treatments like radiotherapy or chemotherapy; (5) Age 18 years or older.
Who cannot join
- (1) Patients with incomplete imaging data or medical records; (2) Lung infections that could affect image analysis; (3) Significant respiratory movement artifacts in images impairing imaging analysis; (4) Inconsistent locations of SPN in postoperative pathology reports and preoperative CT images.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Combined With Clinical and Imaging Features, the Prediction Model of Benign and Malignant Solid Pulmonary Nodules Was Constructed
Study Objective:
To comprehensively analyze the preoperative clinical and imaging characteristics of solid pulmonary nodules, investigate the risk factors associated with malignant solid pulmonary nodules, and provide a reference for preoperative treatment decisions.
Read more
Significance of the Study:
According to the 2020 Global Cancer Report, lung cancer remains the leading cause of cancer-related deaths worldwide. While the majority of patients with stage I lung cancer achieve long-term survival, survival rates for advanced-stage patients are extremely low. Early screening, diagnosis, and treatment of lung cancer are crucial.
With the widespread implementation of early lung cancer screening, a growing number of pulmonary nodules are being detected, among which solid pulmonary nodules constitute a significant proportion. Unlike ground-glass nodules, accurately distinguishing between benign and malignant solid nodules is critical for determining appropriate treatment strategies. For benign solid nodules, follow-up observation is the preferred approach, whereas early surgical intervention is essential for malignant solid nodules.
Although previous studies have explored the correlation between clinical and imaging characteristics, they have not conducted systematic analyses, and most have been based on small sample sizes. Therefore, this study aims to conduct a comprehensive analysis of preoperative clinical and imaging characteristics, build a predictive model to differentiate between benign and malignant solid pulmonary nodules, and provide a reliable reference for selecting treatment strategies.
Read the detailed description
Our study evaluate patients with SPN from the Third Affiliated Hospital of Kunming Medical University. The patient selection followed specific inclusion and exclusion criteria. Inclusion criteria included: (1) All subjects provided CT imaging obtained from the Third Affiliated Hospital of Kunming Medical University within 2-week period prior to surgery; (2) Complete clinicopathological data of solid nodules were obtained; (3) Surgical intervention for one or more SPN; (4) No prior anti-tumor treatments like radiotherapy or chemotherapy; (5) Age 18 years or older. Exclusion criteria involved: (1) Patients with incomplete imaging data or medical records; (2) Lung infections that could affect image analysis; (3) Significant respiratory movement artifacts in images impairing imaging analysis; (4) Inconsistent locations of SPN in postoperative pathology reports and preoperative CT images.
- Conditions
- Lung Cancer, Solid Pulmonary Nodules, Pulmonary Nodules
- Being tested
- Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
- Keywords
- Lung Cancer Screening, Nodule Malignancy Prediction, Preoperative Imaging Analysis
- Medical subject headings
- Lung Neoplasms, Multiple Pulmonary Nodules
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Preoperative Clinical and Imaging Feature Evaluation for Predictive ModelingOther
This study involves preoperative evaluation of clinical and imaging features for constructing a predictive model to differentiate benign and malignant solid pulmonary nodules. Surgical resection is performed to obtain pathological confirmation as the reference standard.
| Study group | Role | What they receive |
|---|---|---|
| Benign Nodule Group | Participants with benign solid pulmonary nodules. Receives: Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling | |
| Malignant Nodule Group | Participants with malignant solid pulmonary nodules. Receives: Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of predictive model
The primary outcome is the area under the receiver operating characteristic curve (AUC) of the predictive model in distinguishing benign from malignant solid pulmonary nodules, based on preoperative clinical and imaging features.
Measured: Within 2 years after surgical resection and pathological confirmation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Nov 2024
- Study start15 Nov 2024 (estimated)
- Primary completion30 Jan 2025 (estimated)
- Study completion20 Feb 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 320 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Third Affiliated Hospital of Kunming Medical College.
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yantao Yang, The Third Affiliated Hospital of Kunming Medical College.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06685458
- Sponsor's protocol ID
- KYLX2024-271
- Status last verified
- Nov 2024
- First submitted
- 10 Nov 2024
- Last updated
- 12 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets generated and/or analyzed during the current study are not publicly available due sharing data is not included in our research institution review board.
Publications
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020 Jan;70(1):7-30. doi: 10.3322/caac.21590. Epub 2020 Jan 8.
PubMed 31912902Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, Nicholson AG, Groome P, Mitchell A, Bolejack V; International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee, Advisory Boards, and Participating Institutions; International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee Advisory Boards and Participating Institutions. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016 Jan;11(1):39-51. doi: 10.1016/j.jtho.2015.09.009.
PubMed 26762738Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12.
PubMed 30207593Choo E. Testing: High-Resolution Chest Computed Tomography Scan. Springer New York. 2014.
Yip R, Li K, Liu L, Xu D, Tam K, Yankelevitz DF, Taioli E, Becker B, Henschke CI. Controversies on lung cancers manifesting as part-solid nodules. Eur Radiol. 2018 Feb;28(2):747-759. doi: 10.1007/s00330-017-4975-9. Epub 2017 Aug 23.
PubMed 28835992Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006 Apr;239(1):34-49. doi: 10.1148/radiol.2391050343.
PubMed 16567482McWilliams A, Tammemagi MC, Mayo JR, Roberts H, Liu G, Soghrati K, Yasufuku K, Martel S, Laberge F, Gingras M, Atkar-Khattra S, Berg CD, Evans K, Finley R, Yee J, English J, Nasute P, Goffin J, Puksa S, Stewart L, Tsai S, Johnston MR, Manos D, Nicholas G, Goss GD, Seely JM, Amjadi K, Tremblay A, Burrowes P, MacEachern P, Bhatia R, Tsao MS, Lam S. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013 Sep 5;369(10):910-9. doi: 10.1056/NEJMoa1214726.
PubMed 24004118Sim YT, Goh YG, Dempsey MF, Han S, Poon FW. PET-CT evaluation of solitary pulmonary nodules: correlation with maximum standardized uptake value and pathology. Lung. 2013 Dec;191(6):625-32. doi: 10.1007/s00408-013-9500-6. Epub 2013 Sep 8.
PubMed 24013495Chu ZG, Zhang Y, Li WJ, Li Q, Zheng YN, Lv FJ. Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations. BMC Cancer. 2019 Nov 7;19(1):1060. doi: 10.1186/s12885-019-6274-0.
PubMed 31699047Ye T, Deng L, Wang S, Xiang J, Zhang Y, Hu H, Sun Y, Li Y, Shen L, Xie L, Gu W, Zhao Y, Fu F, Peng W, Chen H. Lung Adenocarcinomas Manifesting as Radiological Part-Solid Nodules Define a Special Clinical Subtype. J Thorac Oncol. 2019 Apr;14(4):617-627. doi: 10.1016/j.jtho.2018.12.030. Epub 2019 Jan 17.
PubMed 30659988Sun K, You A, Wang B, Song N, Wan Z, Wu F, Zhao W, Zhou F, Li W. Clinical T1aN0M0 lung cancer: differences in clinicopathological patterns and oncological outcomes based on the findings on high-resolution computed tomography. Eur Radiol. 2021 Oct;31(10):7353-7362. doi: 10.1007/s00330-021-07865-2. Epub 2021 Apr 15.
PubMed 33860370Zhao WJ. Preliminary study on CT radiomics to differentiate tuberculosis, adenocarcinoma, and non-tuberculous infectious lesions manifesting as solid pulmonary nodules or masses. 2024.
Li M, Han R, Song W, Wang X, Guo F, Su D, Yu T, Wang Y. [Three Dimensional Volumetric Analysis of Solid Pulmonary Nodules on Chest CT: Cancer Risk Assessment]. Zhongguo Fei Ai Za Zhi. 2016 May 20;19(5):279-85. doi: 10.3779/j.issn.1009-3419.2016.05.05. Chinese.
PubMed 27215456Ma X. Development and validation of a combined model based on imaging features and circulating tumor cells for differentiating benign and malignant solid pulmonary nodules. 2024.
Zhu LL. Analysis of malignant risk factors and imaging and tumor marker expression characteristics in patients with solitary pulmonary nodules. 2022.
Study at a glance
From the registry record, last updated by the sponsor on 12 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Combined With Clinical and Imaging Features, the Prediction Model of Benign and Malignant Solid Pulmonary Nodules Was Constructed
- ClinicalTrials.gov ID
- NCT06685458
- Acronym / sponsor ID
- KYLX2024-271
- Conditions
- Lung Cancer, Solid Pulmonary Nodules, Pulmonary Nodules
- Keywords
- Lung Cancer Screening, Nodule Malignancy Prediction, Preoperative Imaging Analysis
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 320 participants (estimated)
- Lead sponsor
- The Third Affiliated Hospital of Kunming Medical College.University, hospital or other
- Study start
- 15 Nov 2024 (estimated)
- Primary completion
- 30 Jan 2025 (estimated)
- Study completion
- 20 Feb 2025 (estimated)
- First posted
- 12 Nov 2024 (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.
Study Objective:
To comprehensively analyze the preoperative clinical and imaging characteristics of solid pulmonary nodules, investigate the risk factors associated with malignant solid pulmonary nodules, and provide a reference for preoperative treatment decisions.
Significance of the Study:
According to the 2020 Global Cancer Report, lung cancer remains the leading cause of cancer-related deaths worldwide. While the majority of patients with stage I lung cancer achieve long-term survival, survival rates for advanced-stage patients are extremely low. Early screening, diagnosis, and treatment of lung cancer are crucial.
With the widespread implementation of early lung cancer screening, a growing number of pulmonary nodules are being detected, among which solid pulmonary nodules constitute a significant proportion. Unlike ground-glass nodules, accurately distinguishing between benign and malignant solid nodules is critical for determining appropriate treatment strategies. For benign solid nodules, follow-up observation is the preferred approach, whereas early surgical intervention is essential for malignant solid nodules.
Although previous studies have explored the correlation between clinical and imaging characteristics, they have not conducted systematic analyses, and most have been based on small sample sizes. Therefore, this study aims to conduct a comprehensive analysis of preoperative clinical and imaging characteristics, build a predictive model to differentiate between benign and malignant solid pulmonary nodules, and provide a reliable reference for selecting treatment strategies.
Detailed description
Our study evaluate patients with SPN from the Third Affiliated Hospital of Kunming Medical University. The patient selection followed specific inclusion and exclusion criteria. Inclusion criteria included: (1) All subjects provided CT imaging obtained from the Third Affiliated Hospital of Kunming Medical University within 2-week period prior to surgery; (2) Complete clinicopathological data of solid nodules were obtained; (3) Surgical intervention for one or more SPN; (4) No prior anti-tumor treatments like radiotherapy or chemotherapy; (5) Age 18 years or older. Exclusion criteria involved: (1) Patients with incomplete imaging data or medical records; (2) Lung infections that could affect image analysis; (3) Significant respiratory movement artifacts in images impairing imaging analysis; (4) Inconsistent locations of SPN in postoperative pathology reports and preoperative CT images.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Benign Nodule Group | – | Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive ModelingParticipants with benign solid pulmonary nodules. |
| Malignant Nodule Group | – | Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive ModelingParticipants with malignant solid pulmonary nodules. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population includes patients aged 18 years and older with radiologically diagnosed solid pulmonary nodules. These patients are undergoing preoperative clinical and imaging evaluations and subsequent surgical resection to confirm the benign or malignant nature of the nodules
Key inclusion criteria (1 listed)
- (1) All subjects provided CT imaging obtained from the Third Affiliated Hospital of Kunming Medical University within 2-week period prior to surgery; (2) Complete clinicopathological data of solid nodules were obtained; (3) Surgical intervention for one or more SPN; (4) No prior anti-tumor treatments like radiotherapy or chemotherapy; (5) Age 18 years or older.
Key exclusion criteria (1 listed)
- (1) Patients with incomplete imaging data or medical records; (2) Lung infections that could affect image analysis; (3) Significant respiratory movement artifacts in images impairing imaging analysis; (4) Inconsistent locations of SPN in postoperative pathology reports and preoperative CT images.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Preoperative Clinical and Imaging Feature Evaluation for Predictive ModelingThis study involves preoperative evaluation of clinical and imaging features for constructing a predictive model to differentiate benign and malignant solid pulmonary nodules. Surgical resection is performed to obtain pathological confirmation as the reference standard. | Other | Benign Nodule Group, Malignant Nodule Group |
Primary outcome
Diagnostic performance of predictive model
Time frame: Within 2 years after surgical resection and pathological confirmation
The primary outcome is the area under the receiver operating characteristic curve (AUC) of the predictive model in distinguishing benign from malignant solid pulmonary nodules, based on preoperative clinical and imaging features.
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 12 Nov 2024.
- Background: PubMed 31912902Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020 Jan;70(1):7-30. doi: 10.3322/caac.21590. Epub 2020 Jan 8.
- Background: PubMed 26762738Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, Nicholson AG, Groome P, Mitchell A, Bolejack V; International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee, Advisory Boards, and Participating Institutions; International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee Advisory Boards and Participating Institutions. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016 Jan;11(1):39-51. doi: 10.1016/j.jtho.2015.09.009.
- Background: PubMed 30207593Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12.
- BackgroundChoo E. Testing: High-Resolution Chest Computed Tomography Scan. Springer New York. 2014.
- Background: PubMed 28835992Yip R, Li K, Liu L, Xu D, Tam K, Yankelevitz DF, Taioli E, Becker B, Henschke CI. Controversies on lung cancers manifesting as part-solid nodules. Eur Radiol. 2018 Feb;28(2):747-759. doi: 10.1007/s00330-017-4975-9. Epub 2017 Aug 23.
- Background: PubMed 16567482Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006 Apr;239(1):34-49. doi: 10.1148/radiol.2391050343.
- Background: PubMed 24004118McWilliams A, Tammemagi MC, Mayo JR, Roberts H, Liu G, Soghrati K, Yasufuku K, Martel S, Laberge F, Gingras M, Atkar-Khattra S, Berg CD, Evans K, Finley R, Yee J, English J, Nasute P, Goffin J, Puksa S, Stewart L, Tsai S, Johnston MR, Manos D, Nicholas G, Goss GD, Seely JM, Amjadi K, Tremblay A, Burrowes P, MacEachern P, Bhatia R, Tsao MS, Lam S. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013 Sep 5;369(10):910-9. doi: 10.1056/NEJMoa1214726.
- Background: PubMed 24013495Sim YT, Goh YG, Dempsey MF, Han S, Poon FW. PET-CT evaluation of solitary pulmonary nodules: correlation with maximum standardized uptake value and pathology. Lung. 2013 Dec;191(6):625-32. doi: 10.1007/s00408-013-9500-6. Epub 2013 Sep 8.
- Background: PubMed 31699047Chu ZG, Zhang Y, Li WJ, Li Q, Zheng YN, Lv FJ. Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations. BMC Cancer. 2019 Nov 7;19(1):1060. doi: 10.1186/s12885-019-6274-0.
- Background: PubMed 30659988Ye T, Deng L, Wang S, Xiang J, Zhang Y, Hu H, Sun Y, Li Y, Shen L, Xie L, Gu W, Zhao Y, Fu F, Peng W, Chen H. Lung Adenocarcinomas Manifesting as Radiological Part-Solid Nodules Define a Special Clinical Subtype. J Thorac Oncol. 2019 Apr;14(4):617-627. doi: 10.1016/j.jtho.2018.12.030. Epub 2019 Jan 17.
- Background: PubMed 33860370Sun K, You A, Wang B, Song N, Wan Z, Wu F, Zhao W, Zhou F, Li W. Clinical T1aN0M0 lung cancer: differences in clinicopathological patterns and oncological outcomes based on the findings on high-resolution computed tomography. Eur Radiol. 2021 Oct;31(10):7353-7362. doi: 10.1007/s00330-021-07865-2. Epub 2021 Apr 15.
- BackgroundZhao WJ. Preliminary study on CT radiomics to differentiate tuberculosis, adenocarcinoma, and non-tuberculous infectious lesions manifesting as solid pulmonary nodules or masses. 2024.
- Individual participant dataSharing planned: No. The datasets generated and/or analyzed during the current study are not publicly available due sharing data is not included in our research institution review board.
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Solid Pulmonary Nodules, run by The Third Affiliated Hospital of Kunming Medical College..
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 1 inclusion and 1 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: opening soon, as of the sponsor's last update on 12 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
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 yantao yang (18288509115, 2443804244@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of predictive model (time frame: Within 2 years after surgical resection and pathological confirmation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06685458, which also lists 15 linked publications.
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 12 Nov 2024.