Aim 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. Materials and methods Twenty-fve 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 was divided into two groups: responders (PFS≤11 months) and non-responders (PFS>11 months). 3D segmentation was performed on whole liver of naïve CT scans in arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). A total of 107 radiomic features were extracted and compared between two groups (T test or Mann–Whitney), radiomics performance assessed with receiver operating characteristic curve, Kaplan–Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death, and interobserver variability assessed. All signifcant radiomic comparisons were validated by using a synthetic external cohort. P<0.05 is considered signifcant. Results 15/25 patients were classifed 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 signifcant differences between two groups (P<0.05) with the best performance (internal cohort AUC 0.86–0.84, P<0.0001; external cohort AUC 0.84–0.90; 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 specifcity 66.7%. Three features achieved 0.77≤ICC<0.83 and one ICC=0.92. Conclusions In patients afected 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

Caruso, Damiano
Primo
;
Polici, Michela
Secondo
;
Rinzivillo, Maria;Zerunian, Marta;Nacci, Ilaria;Marasco, Matteo;Magi, Ludovica;Tarallo, Mariarita;Gargiulo, Simona;Iannicelli, Elsa;Annibale, Bruno;Laghi, Andrea
Penultimo
;
Panzuto, Francesco
Ultimo
2022

Abstract

Aim 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. Materials and methods Twenty-fve 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 was divided into two groups: responders (PFS≤11 months) and non-responders (PFS>11 months). 3D segmentation was performed on whole liver of naïve CT scans in arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). A total of 107 radiomic features were extracted and compared between two groups (T test or Mann–Whitney), radiomics performance assessed with receiver operating characteristic curve, Kaplan–Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death, and interobserver variability assessed. All signifcant radiomic comparisons were validated by using a synthetic external cohort. P<0.05 is considered signifcant. Results 15/25 patients were classifed 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 signifcant differences between two groups (P<0.05) with the best performance (internal cohort AUC 0.86–0.84, P<0.0001; external cohort AUC 0.84–0.90; 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 specifcity 66.7%. Three features achieved 0.77≤ICC<0.83 and one ICC=0.92. Conclusions In patients afected 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/11573/1651886
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