OBJECTIVE: To investigate the usefulness of serum chromogranin A (CgA) for the prediction of tumor burden, therapeutic response, and nomogram-based survival in well-moderate nonfunctional pancreatic neuroendocrine tumors (NF-PNETs) with liver metastases (LMs). MATERIALS AND METHODS: This prospective study included 51 NF-PNETs of LMs patients, 134 other neuroendocrine tumors, and 125 controls. Serum CgA levels were determined by enzyme-linked immunosorbent assay at baseline and after treatment. LMs tumor burden was computed simultaneously from computed tomography/MRI scan with thin slices using a semiquantitative three-dimensional reconstruction approach. Predictive CgA for therapeutic response was assessed using the response evaluation criteria in solid tumors criteria. A nomogram to predict the prognostic value of CgA with variables selected in the multivariate Cox proportional hazards model was constructed; the accuracy of the nomogram was quantified by the (concordance index) C-index and a calibration plot. RESULTS: Considering NF-PNETs, CgA correlated with the tumor grade and differentiation (P<0.05). There was a nonlinear exponential regression between LMs tumor burden and CgA levels (P<0.001). The alteration in CgA correlated with therapeutic response (P<0.001). Increased CgA presented significantly lower progression-free survival than the stable/decreased CgA subgroup (P<0.001). For overall survival, a baseline CgA increase greater than 2.5 upper limit of normal level was predictive of a poor prognosis (P<0.001). Baseline CgA level, LMs tumor burden, and Ki-67 were selected as independent factors for the nomogram to predict overall survival; the nomogram showed fitting calibration with a C-index of 0.87 (95% confidence interval, 0.82-0.92). CONCLUSION: Serum CgA could be used to reflect tumor burden, evaluate the therapeutic response, and predict the survival outcomes for NF-PNETs with LMs. An effective nomogram including CgA was proposed for prediction.
OBJECTIVE: To investigate the usefulness of serum chromogranin A (CgA) for the prediction of tumor burden, therapeutic response, and nomogram-based survival in well-moderate nonfunctional pancreatic neuroendocrine tumors (NF-PNETs) with liver metastases (LMs). MATERIALS AND METHODS: This prospective study included 51 NF-PNETs of LMs patients, 134 other neuroendocrine tumors, and 125 controls. Serum CgA levels were determined by enzyme-linked immunosorbent assay at baseline and after treatment. LMs tumor burden was computed simultaneously from computed tomography/MRI scan with thin slices using a semiquantitative three-dimensional reconstruction approach. Predictive CgA for therapeutic response was assessed using the response evaluation criteria in solid tumors criteria. A nomogram to predict the prognostic value of CgA with variables selected in the multivariate Cox proportional hazards model was constructed; the accuracy of the nomogram was quantified by the (concordance index) C-index and a calibration plot. RESULTS: Considering NF-PNETs, CgA correlated with the tumor grade and differentiation (P<0.05). There was a nonlinear exponential regression between LMs tumor burden and CgA levels (P<0.001). The alteration in CgA correlated with therapeutic response (P<0.001). Increased CgA presented significantly lower progression-free survival than the stable/decreased CgA subgroup (P<0.001). For overall survival, a baseline CgA increase greater than 2.5 upper limit of normal level was predictive of a poor prognosis (P<0.001). Baseline CgA level, LMs tumor burden, and Ki-67 were selected as independent factors for the nomogram to predict overall survival; the nomogram showed fitting calibration with a C-index of 0.87 (95% confidence interval, 0.82-0.92). CONCLUSION: Serum CgA could be used to reflect tumor burden, evaluate the therapeutic response, and predict the survival outcomes for NF-PNETs with LMs. An effective nomogram including CgA was proposed for prediction.
Authors: Katiuscha Merath; Fabio Bagante; Eliza W Beal; Alexandra G Lopez-Aguiar; George Poultsides; Eleftherios Makris; Flavio Rocha; Zaheer Kanji; Sharon Weber; Alexander Fisher; Ryan Fields; Bradley A Krasnick; Kamran Idrees; Paula M Smith; Cliff Cho; Megan Beems; Carl R Schmidt; Mary Dillhoff; Shishir K Maithel; Timothy M Pawlik Journal: J Surg Oncol Date: 2018-02-15 Impact factor: 3.454
Authors: Amit Tirosh; Georgios Z Papadakis; Corina Millo; Samira M Sadowski; Peter Herscovitch; Karel Pacak; Stephen J Marx; Lily Yang; Pavel Nockel; Jasmine Shell; Patience Green; Xavier M Keutgen; Dhaval Patel; Naris Nilubol; Electron Kebebew Journal: Eur J Endocrinol Date: 2017-05 Impact factor: 6.664
Authors: Alessandra Pulvirenti; Deepthi Rao; Caitlin A Mcintyre; Mithat Gonen; Laura H Tang; David S Klimstra; Martin Fleisher; Lakshmi V Ramanathan; Diane Reidy-Lagunes; Peter J Allen Journal: HPB (Oxford) Date: 2018-10-23 Impact factor: 3.647