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Prediction Model for Multiple Pulmonary Nodules

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ClinicalTrials.gov Identifier: NCT03795181
Recruitment Status : Completed
First Posted : January 7, 2019
Last Update Posted : August 28, 2019
Sponsor:
Information provided by (Responsible Party):
Jun Wang, Peking University People's Hospital

Study Description
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Brief Summary:
This study compares the sensitivity, specificity and accuracy of radiologists, thoracic surgeons and a predictive model (PKUM model) to discriminate malignancy from benign nodules in patients with multiple pulmonary nodules.

Condition or disease
Multiple Pulmonary Nodules

Detailed Description:
It is clinically difficult to diagnose and manage patients with multiple pulmonary nodules (MPNs). We have developed a web-based mathematic model (PKUM model) by using a multi-centric database from three institutions (Peking University People's Hospital, Haidian Section of Peking University Third Hospital, People's Hospital Affiliated to Hebei Medical University) to predict the probability of a nodule to be malignant in patients with MPNs. This prospectively observational study will recruit patients with MPNs between January 2019 and March 2019, allowing radiologists, surgeons, and a predictive model (PKUM model) to discriminate malignancy from benign nodules, and compare their sensitivity, specificity, and accuracy.
Study Design
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Study Type : Observational
Actual Enrollment : 59 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Prediction Model for Multiple Pulmonary Nodules
Actual Study Start Date : January 1, 2019
Actual Primary Completion Date : March 30, 2019
Actual Study Completion Date : March 30, 2019
Groups and Cohorts
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Outcome Measures
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Primary Outcome Measures :
  1. Performance of PKUM model [ Time Frame: 3 months ]
    Area under receiver operating characteristic curve (AUC) of PKUM model in predicting the probability of a nodule to be malignant in patients with multiple pulmonary nodules.


Secondary Outcome Measures :
  1. Comparison between PKUM model and clinicians [ Time Frame: 3 months ]
    Comparison of sensitivity and specificity of radiologists, thoracic surgeons, and PKUM model in predicting the probability of a nodule to be malignant in patients with multiple pulmonary nodules.

  2. Performance of PKUM model in equivocal nodules which is difficult to judge by clinicians [ Time Frame: 3 months ]
    Sensitivity and specificity of PKUM model in predicting malignant probability of equivocal nodules judged by radiologists and thoracic surgeons.


Eligibility Criteria
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Ages Eligible for Study:   18 Years to 90 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
The population include patients with multiple pulmonary nodules who will be diagnosed and treated between January 1，2019 and March 30, 2019. Patients with at least 2 nodules resected will be enrolled in this study.
Criteria

Inclusion Criteria:

  • Patients with newly discovered, 4-30 mm multiple pulmonary nodules shown on thoracic CT scans
  • Patients with at least two nodules resected for pathological evaluation

Exclusion Criteria:

  • History of malignancy within 5 years
  • Presence of pneumonia or pleural effusion on thoracic CT scans
  • Patients with none or only one nodule resected
  • Patients with initial chemo-radiation therapy
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03795181


Locations
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China
Peking University People's Hospital
Beijing, China, 100044
Sponsors and Collaborators
Peking University People's Hospital
Investigators
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Principal Investigator: Jun Wang Peking University People's Hospital
Study Director: Yuqing Huang Haidian Section of Peking University Third Hospital
Study Director: Jiabao Liu People's Hospital Affiliated to Hebei Medical University
Study Director: Yingtai Chen Beijing Aerospace 711 Hospital
Study Director: Mingru Li Beijing Aerospace 731 Hospital
More Information
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Responsible Party: Jun Wang, Chief，Thoracic Surgery Service, Peking University People's Hospital
ClinicalTrials.gov Identifier: NCT03795181    
Other Study ID Numbers: PTHO1902
First Posted: January 7, 2019    Key Record Dates
Last Update Posted: August 28, 2019
Last Verified: August 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Jun Wang, Peking University People's Hospital:
multiple pulmonary nodules; predictive model
Additional relevant MeSH terms:
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Multiple Pulmonary Nodules
Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
 Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases