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

• Published in: Radiologia medica Vol. 128; no. 2; pp. 203 - 211
• Main Authors: Bicci, Eleonora, Cozzi, Diletta, Cavigli, Edoardo, Ruzga, Ron, Bertelli, Elena, Danti, Ginevra, Bettarini, Silvia, Tortoli, Paolo, Mazzoni, Lorenzo Nicola, Busoni, Simone, Miele, Vittorio
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01.02.2023; Springer Nature B.V
• Subjects: Bias; Chest Radiology; Cluster analysis; Clustering; Computed tomography; Correlation analysis; Diagnostic Radiology; Feature extraction; Heterogeneity; Humans; Image contrast; Image enhancement; Imaging; Interventional Radiology; Ki-67 Antigen; Lung - diagnostic imaging; Lung - pathology; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lymphatic Metastasis; Medical imaging; Medicine; Medicine & Public Health; Metastasis; Neuroendocrine Tumors - diagnostic imaging; Neuroendocrine Tumors - pathology; Neuroradiology; Radiology; Radiomics; Reproducibility; Reproducibility of Results; Statistical significance; Statistical tests; Tomography, X-Ray Computed - methods; Tumors; Ultrasound; Ki-67; Lung carcinoids; Computed tomography; Lung cancer; Radiomics
• ISSN: 1826-6983; 0033-8362; 1826-6983
• DOI: 10.1007/s11547-023-01592-y

Background The aim is to find a correlation between texture features extracted from neuroendocrine (NET) lung cancer subtypes, both Ki-67 index and the presence of lymph-nodal mediastinal metastases detected while using different computer tomography (CT) scanners. Methods Sixty patients with a confirmed pulmonary NET histological diagnosis, a known Ki-67 status and metastases, were included. After subdivision of primary lesions in baseline acquisition and venous phase, 107 radiomic features of first and higher orders were extracted. Spearman’s correlation matrix with Ward’s hierarchical clustering was applied to confirm the absence of bias due to the database heterogeneity. Nonparametric tests were conducted to identify statistically significant features in the distinction between patient groups (Ki-67 < 3—Group 1; 3 ≤ Ki-67 ≤ 20—Group 2; and Ki-67 > 20—Group 3, and presence of metastases). Results No bias arising from sample heterogeneity was found. Regarding Ki-67 groups statistical tests, seven statistically significant features ( p value < 0.05) were found in post-contrast enhanced CT; three in baseline acquisitions. In metastasis classes distinction, three features (first-order class) were statistically significant in post-contrast acquisitions and 15 features (second-order class) in baseline acquisitions, including the three features distinguishing between Ki-67 groups in baseline images (MCC, ClusterProminence and Strength). Conclusions Some radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness.