The investigators investigate the utility of FDG PET/CT based radiomics in lung cancer, including diagnosis and prognosis.
Recent studies have shown that, in addition to inter-tumor heterogeneity, tumors often display startling intratumoral heterogeneity in various features including histology, gene expression, genotype, and metastatic and proliferative potential, which is often associated with adverse tumor biology. Unfortunately, it is difficult to assess intratumoral heterogeneity with random sampling or biopsy as this does not represent the full extent of phenotypic or genetic variation within a tumor. Given the limitations of current biopsy strategies, there is an important potential for medical imaging, which has the ability to capture intratumoral heterogeneity in a non-invasive way. Borrowed from the concept in genomics and/or proteomics, radiomics was specifically proposed for medical or radiological images. It is a promising technique for improving diagnosis, staging, prognosis, treatment response prediction and potentially allowing personalization of cancer treatment. It is a process of extraction and analysis of high-dimensional image features from radiological images obtained with CT, MR or PET, which could be either qualitative or quantitative. The basic assumption of radiomics is that tumor biology could be captured by radiomic features . The purpose of this study is to investigate the utility of FDG PET/CT based radiomics in lung cancer. Four PET/CT centers will be involved in this study, in which more than 1000 patients diagnosed as lung cancer will be retrospectively enrolled.
Study Type
OBSERVATIONAL
Enrollment
1,500
No Interventions
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGCreation of a FDG PET/CT based radiomic score for survival
Multiple quantitative radiomic features including SUV, metabolic volume, shape and texture will be measured from FDG PET/CT images. The all subjects will be randomly separated into a training and validation data. The multiple image features will be aggregated into a single combined radiomic score for survival with an appropriate machine learning method and the training data.
Time frame: Time Frame: 3 years
Validation of a FDG PET/CT based radiomic score for survival
The created radiomic score developed in the primary outcome will be evaluated with the validation data in terms of survival(progress-free or overall survival)
Time frame: Time Frame: 3 years
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