Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
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|ClinicalTrials.gov Identifier: NCT03648151|
Recruitment Status : Recruiting
First Posted : August 27, 2018
Last Update Posted : August 27, 2018
|Condition or disease|
|Lung Cancer Image, Body|
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.
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.
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.
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.
|Study Type :||Observational|
|Estimated Enrollment :||500 participants|
|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|
- 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
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
|Contact: Si Hongwei, MD||13966773801 ext +email@example.com|
|First Affiliated Hospital of Anhui Medical University||Recruiting|
|Hefei, Anhui, China, 230022|
|Contact: Si Hongwei +8613966773801|
|First Affiliated Hospital of Shanxi Medical University||Recruiting|
|Taiyuan, Shanxi, China, 030001|
|Contact: Li Sijin 13934519222 ext +86 firstname.lastname@example.org|
|Study Chair:||Li Sijin, MD||First Affiliated Hospital of Shanxi Medical University|