A Hybrid Clinical Molecular Nomogram Accurately Predicts Survival in PRRT Treated GEP-NETs
Introduction: Clinical assessment has limited prognostic ability. Mathematical tools –nomograms- that incorporate multiple parameters are more effective. Novel transcriptomic data has added value in defining NET biology in tissue and blood. A blood multigene expression analysis test is effective as a molecular prognostic marker for PFS.
Aim(s): Design a combined clinical and gene expression nomogram (CGEN) to predict OS and PFS in PRRT-treated GEP-NETs.
Materials and methods: 177Lu-PRRT-treated GEP-NETs (n=57) were followed: median 15 months (range 1-33). Clinical nomogram data included 10 variables (age, gender, grade, Ki67, stage, symptoms, liver mets, SSA use, surgery, CgA). Hazard ratios were calculated for the clinical nomogram (10) and 51 NET transcripts (Cox-proportional modeling) to design a hybrid nomogram. OS and PFS (RECIST criteria) were analyzed (ROC, Kaplan-Meier survival).
Conference: 14th Annual ENETSConcerence (2017)
Presenting Author: Kidd M
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