This study aims to investigate the feasibility and efficiency of CT radiomic analysis which serves as a high through-put analytical strategy applied to image big-data resource in evaluating and predicting the response of immunotherapeutics. A multi-center retrospective diagnostic test has been designed for this aim to compare the predictive performance of clinical model, qualitative model incorporating semantic CT features and image-based quantitative radiomic model. The reference standard of therapeutic effect is determined by the latest evaluation result utilizing iRECIST within 365 days after recruited. This study intends to enroll 400 participates who had been diagnosed with advanced somatic solid tumor confirmed by histo- or cyto-pathological examination and were planning to receive immunotherapy.
Study Type
OBSERVATIONAL
Enrollment
285
Clinical: A diagnostic model incorporating clinical variables (Age, Pathological diagnosis, level of serum tumor biomarkers etc.)
Semantic: A diagnostic model incorporating semantic radiological features (shape, location, border, density etc.)
Radiomic: A diagnostic model incorporating quantitative radiomic features (histogram, texture, morphology etc.)
Tianjin Medical University Cancer Institute And Hospital
Tianjin, Tianjin Municipality, China
Area Under the receiver operating characteristic curve (ROC)
Area under curve (AUC) of each diagnostic model
Time frame: 6 months and 1 year since course start (365 days)
Disease Control Rate
Disease Control Rate of immunotherapy
Time frame: 6 months and 1 year since course start (365 days)
Incidence
Incidence of immune-related adverse events
Time frame: Through study completion, an average of 1 year
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