Not yet recruiting NCT06685458

Constructing a Predictive Model for Differentiating Between Benign and Malignant Solid Pulmonary Nodules Based on Clinical and Imaging Features.

— Observational Lung Cancer Solid Pulmonary Nodules Pulmonary Nodules

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied: The protocol lists: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling.
Who it may be relevant to: Registry conditions: Lung Cancer, Solid Pulmonary Nodules, Pulmonary Nodules. Basic parameters: from 18 years · All.
What needs checking: Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place: Center list to be confirmed — check the primary protocol.
Next step: Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

Combined With Clinical and Imaging Features, the Prediction Model of Benign and Malignant Solid Pulmonary Nodules Was Constructed

Overview

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.

Interventions

Other 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.

Primary outcome measures

Diagnostic performance of predictive model [Time frame: Within 2 years after surgical resection and pathological confirmation]

Eligibility criteria

Inclusion criteria

(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

(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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model: Case-control

Study locations

Center list to be confirmed — check the primary protocol.

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. PMID 31912902
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 In PMID 26762738
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. PMID 30207593
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. PMID 28835992
Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006 Apr;239(1):34-49. doi: 10.1148/radiol.2391050343. PMID 16567482
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 PMID 24004118
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. PMID 24013495

Identifiers

NCT: NCT06685458 · KYLX2024-271

Primary sources (government registries)

ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗