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Not yet recruitingNCT06685458Updated Nov 12, 2024

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

An observational study in Lung Cancer, Solid Pulmonary Nodules and Pulmonary Nodules, sponsored by The Third Affiliated Hospital of Kunming Medical College.. Not yet recruiting. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2024-11-12.

Sponsored by The Third Affiliated Hospital of Kunming Medical College. · Observational

From the registry’s dates

  • Primary completion was expected by Jan 2025, 1 year 9 months ago, but the record still lists the study as not yet recruiting.
Study type
Observational
Model
Case-control
Time perspective
Retrospective
Enrollment
320
Ages
18 Years and older
Sex
All
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Study 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.

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.

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Conditions studied

  • Lung Cancer
  • Solid Pulmonary Nodules
  • Pulmonary Nodules

Keywords

  • Lung Cancer Screening
  • Nodule Malignancy Prediction
  • Preoperative Imaging Analysis
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In context

Lung Neoplasms

5,422 studies on the registry are indexed under Lung Neoplasms; 1,346 are open to participants now.

This study's planned enrollment of 320 is above the median of 200 across 1,434 observational studies indexed under Lung Neoplasms.

Browse Lung Neoplasms studies →

Lead sponsor

The Third Affiliated Hospital of Kunming Medical College. is the lead sponsor of 2 studies on the registry; 2 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

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

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

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

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.
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Study design

Observational model
Case-control
Time perspective
Retrospective
Enrollment
320 participants (estimated)
Patient registry
No

Groups and cohorts

  • Benign Nodule Group

    Participants with benign solid pulmonary nodules.

    Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling

  • Malignant Nodule Group

    Participants with malignant solid pulmonary nodules.

    Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling

Interventions

  • OtherPreoperative 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.

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What researchers measure

Primary outcomes

  1. 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.

    Time frame: Within 2 years after surgical resection and pathological confirmation

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Study locations

No study locations are listed for this record.

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References and documents

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 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 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 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. Erratum In: CA Cancer J Clin. 2020 Jul;70(4):313. doi: 10.3322/caac.21609. PubMed 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. PubMed 28835992 ↗
  • Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006 Apr;239(1):34-49. doi: 10.1148/radiol.2391050343. PubMed 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 on first screening CT. N Engl J Med. 2013 Sep 5;369(10):910-9. doi: 10.1056/NEJMoa1214726. PubMed 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. PubMed 24013495 ↗
  • 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. PubMed 31699047 ↗
  • 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. PubMed 30659988 ↗
  • 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. PubMed 33860370 ↗
  • 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. PubMed 27215456 ↗
  • 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.

Individual participant data

Plan to share: 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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Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Start is estimated for Nov 2024; primary completion for Jan 2025, 2 months later.

How these figures are worked out, and which studies are left out of them.

Registered
Nov 10, 2024
First posted
Nov 12, 2024
StartEst.
Nov 15, 2024
Primary completionEst.
Jan 30, 2025
CompletionEst.
Feb 20, 2025
Last update
Nov 12, 2024
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Nov 12, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT06685458
Lead sponsor
The Third Affiliated Hospital of Kunming Medical College.
Responsible party
Yantao Yang (Physician, The Third Affiliated Hospital of Kunming Medical College.) — Principal investigator
First posted
Nov 12, 2024
Start date
Nov 15, 2024 (estimated)
Primary completion
Jan 30, 2025 (estimated)
Completion
Feb 20, 2025 (estimated)
Last update
Nov 12, 2024

Study contacts

yantao yang
Contact
2443804244@qq.com
18288509115
lianhua ye
Contact
Lhye1204@aliyun.com
13769123627

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Nov 2024. You cannot join it, but the record below documents what was studied.

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