Constructing a Predictive Model for Differentiating Between Benign and Malignant Solid Pulmonary Nodules Based on Clinical and Imaging Features.
NCT ID: NCT06685458
Last Updated: 2024-11-12
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
320 participants
OBSERVATIONAL
2024-11-15
2025-02-20
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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Benign Nodule Group
Participants with benign solid pulmonary nodules.
Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
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.
Malignant Nodule Group
Participants with malignant solid pulmonary nodules.
Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
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.
Interventions
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Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
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.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
No
Sponsors
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The Third Affiliated Hospital of Kunming Medical College.
OTHER
Responsible Party
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Yantao Yang
Physician
Central Contacts
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References
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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.
Goldstraw 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.
Bray 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.
Choo 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.
Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006 Apr;239(1):34-49. doi: 10.1148/radiol.2391050343.
McWilliams 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.
Sim 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.
Chu 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.
Ye 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.
Sun 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.
Zhao 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.
Ma 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.
Other Identifiers
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KYLX2024-271
Identifier Type: -
Identifier Source: org_study_id
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