Purpose: To test radiomic approach in patients with metastatic neuroendocrine tumors (NETs) treated with Everolimus, with the aim to predict progression free survival (PFS) and death. Material and Methods: Twenty-five patients with metastatic neuroendocrine tumors, 15/25 pancreatic(60%), 9/25 ileal(36%), 1/25 lung(4%), were retrospectively enrolled between August 2013 and December 2020. All patients underwent contrast-enhanced CT before starting Everolimus, histological diagnosis, tumor grading, PFS, overall survival (OS), death, and clinical data collected. Population divided into two groups: responders (PFS 12>months) and non-responders (PFS<12months). 3D-segmentation performed on whole liver of naïve CT-scans on arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). 107 radiomic features were extracted and compared between two groups (T-test or Mann-Withney); Radiomics performance assessed with receiver operating characteristic curve, Kaplan-Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death. P<0.05 considered significant. Results: 15/25 patients were classified as responders (median PFS 25 months and OS 29 months), and 10/25 as non-responders (median PFS 4.5 months and OS 23 months). Among radiomic parameters, Correlation and Imc1 showed significant differences between two groups (P<0.05) with the best performance (AUC 0.86-0.84, P<0.0001). Correlation <0.21 resulted correlated with death at Kaplan-Meyer analysis (P=0.02). Univariate analysis showed three radiomic features independently correlated with death, and in multivariate analysis radiomic model showed good performance with AUC 0.87, sensitivity 100%, and specificity 66.7%. Conclusion: In patients affected by metastatic NETs eligible for Everolimus treatment Radiomics could be used as imaging biomarker able to predict PFS and death.

CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors / Polici, M; Zerunian, M; Nacci, I; Guido, G; Iannicelli, E; Caruso, D; Laghi, A. - (2022). ((Intervento presentato al convegno ESGAR tenutosi a Lisbon.

CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors

Nacci I;
2022

Abstract

Purpose: To test radiomic approach in patients with metastatic neuroendocrine tumors (NETs) treated with Everolimus, with the aim to predict progression free survival (PFS) and death. Material and Methods: Twenty-five patients with metastatic neuroendocrine tumors, 15/25 pancreatic(60%), 9/25 ileal(36%), 1/25 lung(4%), were retrospectively enrolled between August 2013 and December 2020. All patients underwent contrast-enhanced CT before starting Everolimus, histological diagnosis, tumor grading, PFS, overall survival (OS), death, and clinical data collected. Population divided into two groups: responders (PFS 12>months) and non-responders (PFS<12months). 3D-segmentation performed on whole liver of naïve CT-scans on arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). 107 radiomic features were extracted and compared between two groups (T-test or Mann-Withney); Radiomics performance assessed with receiver operating characteristic curve, Kaplan-Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death. P<0.05 considered significant. Results: 15/25 patients were classified as responders (median PFS 25 months and OS 29 months), and 10/25 as non-responders (median PFS 4.5 months and OS 23 months). Among radiomic parameters, Correlation and Imc1 showed significant differences between two groups (P<0.05) with the best performance (AUC 0.86-0.84, P<0.0001). Correlation <0.21 resulted correlated with death at Kaplan-Meyer analysis (P=0.02). Univariate analysis showed three radiomic features independently correlated with death, and in multivariate analysis radiomic model showed good performance with AUC 0.87, sensitivity 100%, and specificity 66.7%. Conclusion: In patients affected by metastatic NETs eligible for Everolimus treatment Radiomics could be used as imaging biomarker able to predict PFS and death.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1645357
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