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Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03648151
Recruitment Status : Recruiting
First Posted : August 27, 2018
Last Update Posted : August 27, 2018
Sponsor:
Collaborator:
The First Affiliated Hospital of Anhui Medical University
Information provided by (Responsible Party):
Hongwei Si, The First Affiliated Hospital of Shanxi Medical University

Brief Summary:
Radiomics is an attractive field in objectively quantifying image features, and may overcome the subjectivity of visually interpreting computed tomography (CT), or positron emission tomography (PET). It is reported that the features related to treatment response, outcomes, tumor staging, tissue identification, and cancer genetics. Therefore, the investigators try to explore the key features for the outcome of lung cancer patients.

Condition or disease
Lung Cancer Image, Body

Detailed Description:

Radiomic Features:

PET/CT images, including other kinds of CT serials, were transported into a personal computer. Using the open source software of 3D-Slicer, volumes of interest (VOIs) for primary tumor, or even lymph nodes, was semi-automatically or manually segmented. And then, radiomic features were extracted.

PET Parameters:

Using combined CT VOIs, corresponding PET standard uptake value (SUV, no unit) were measured. For a foci (either tumor, or lymph node), mean, sum and maximum SUV were documented, and were used for training and validating models alongside radiomic features.

Feature Selection:

Data were analyzed by deep learning or random forests method, and top 20 variables were scored by their contribution to the regression (variable importance, VIMP). The generalized features were identified as the same ones between two kinds of image serials (for example, ordinary and thin-section CT, or PET and CT). Additionally, when three or more features met the criterion, a lower value of Akaike information criterion (AIC) which measures the relative quality of statistical models was used to find appropriate features with lower overfitting possibility.

Model Validation:

The developed model was validated internally and externally. The internal indices for independent continuous variable were accuracy (bias and absolute bias) and precision (correlation coefficient and R square), and that for independent classified or survival variable was c-index. The patients enrolled from another medical center were used for external validation.

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Study Type : Observational
Estimated Enrollment : 500 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
Actual Study Start Date : January 1, 2010
Estimated Primary Completion Date : December 31, 2019
Estimated Study Completion Date : December 31, 2019

Resource links provided by the National Library of Medicine





Primary Outcome Measures :
  1. Overall survival (OS) of lung cancer patients [ Time Frame: The patients were followed to December 31, 2019 ]
    The time from the scan date to death for any reason



Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
lung caner
Criteria

Inclusion Criteria:

  1. Pathologically diagnosed as lung caner.
  2. Accepted PET/CT scans at the hospitals either affiliated to Shanxi Medical University or Anhui Medical University
  3. Both PET and CT serials can be obtained
  4. Can be followed for treatment modalities (including chemotherapy regimens, radiotherapy dose, and et al), survival time and status, and other related information.

Exclusion Criteria:

  1. Simultaneously suffering from the cancers from other tissues and organs
  2. Have a history of diabetes, chronic heart diseases, or chronic renal failure

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): NCT03648151


Contacts
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Contact: Si Hongwei, MD 13966773801 ext +86 sihw@163.com

Locations
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China, Anhui
First Affiliated Hospital of Anhui Medical University Recruiting
Hefei, Anhui, China, 230022
Contact: Si Hongwei    +8613966773801      
China, Shanxi
First Affiliated Hospital of Shanxi Medical University Recruiting
Taiyuan, Shanxi, China, 030001
Contact: Li Sijin    13934519222 ext +86    lisjnm123@163.com   
Sponsors and Collaborators
The First Affiliated Hospital of Shanxi Medical University
The First Affiliated Hospital of Anhui Medical University
Investigators
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Study Chair: Li Sijin, MD First Affiliated Hospital of Shanxi Medical University

Publications:

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Responsible Party: Hongwei Si, Chief physician, The First Affiliated Hospital of Shanxi Medical University
ClinicalTrials.gov Identifier: NCT03648151    
Other Study ID Numbers: 20170501
First Posted: August 27, 2018    Key Record Dates
Last Update Posted: August 27, 2018
Last Verified: August 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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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 Hongwei Si, The First Affiliated Hospital of Shanxi Medical University:
Radomic feature
PET
CT
Additional relevant MeSH terms:
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Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases