Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population

[11] Vizcaino AP(2008)Reclassification of neuroendocrine tumors improves the separation of carcinoids and the prediction of survival J Thorac Oncol 126 1296-1311

[12] Parkin DM(2021)A deep look into radiomics Radiol Med 126 1388-1395

[13] Chong S(2021)Computed tomography-based radiomics approach in pancreatic tumors characterization Radiol Med 127 117-128

[14] Lee KS(2022)Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: Preliminary and monocentric experience Radiol Med 127 461-470

[15] Chung MJ(2022)Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases Radiol Med 126 745-760

[16] Danti G(2021)CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors Radiol Med 125 517-521

[17] Berti V(2020)Artificial intelligence: Who is responsible for the diagnosis? Radiol Med 126 63-71

[18] Abenavoli E(2021)Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey Radiol Med 125 465-473

[19] Jeung MY(2022)Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters Eur Radiol Experimental 136 804-811

[20] Gasser B(2020)Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors Radiol Med 124 951-960

[11] Vizcaino AP(2008)Reclassification of neuroendocrine tumors improves the separation of carcinoids and the prediction of survival J Thorac Oncol 126 1296-1311

[12] Parkin DM(2021)A deep look into radiomics Radiol Med 126 1388-1395

[13] Chong S(2021)Computed tomography-based radiomics approach in pancreatic tumors characterization Radiol Med 127 117-128

[14] Lee KS(2022)Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: Preliminary and monocentric experience Radiol Med 127 461-470

[15] Chung MJ(2022)Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases Radiol Med 126 745-760

[16] Danti G(2021)CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors Radiol Med 125 517-521

[17] Berti V(2020)Artificial intelligence: Who is responsible for the diagnosis? Radiol Med 126 63-71

[18] Abenavoli E(2021)Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey Radiol Med 125 465-473

[19] Jeung MY(2022)Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters Eur Radiol Experimental 136 804-811

[20] Gasser B(2020)Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors Radiol Med 124 951-960