Radiomics in advanced gastroenteropancreatic neuroendocrine neoplasms: Identifying responders to somatostatin analogs.

Abstract

To evaluate a radiomic strategy for predicting progression in advanced gastroenteropancreatic neuroendocrine tumor (GEP-NET) patients treated with somatostatin analogs (SSAs). Fifty-eight patients with GEP-NETs and liver metastases, with baseline computerized tomography (CT) scans from June 2013 to November 2020, were studied retrospectively. Data collected included progression-free survival (PFS), overall survival (OS), tumor grading, death, and Ki67 index. Patients were categorized into progressive and non-progressive groups. Two radiologists performed 3D liver segmentation on baseline CT scans using 3DSlicer v4.10.2. One hundred six radiomic features were extracted and analyzed (T-test or Mann-Whitney). Radiomic feature efficacy was evaluated via receiver operating characteristic curves, and both univariate and multivariate logistic regression were used to develop predictive models. A significance level of p < .05 was maintained. Of 55 patients, 38 were progressive (median PFS and OS: 14 and 34 months, respectively), and 17 were non-progressive (median PFS and OS: 58 months each). Six radiomic features significantly differed between groups (p < .05), with an area under the curve (AUC) range of 0.64-0.74. Ki67 was the only clinical parameter significantly associated with progression risk (odds ratio (OR) = 1.14, p < .05). The combined radiomic features and Ki67 model proved most effective, showing an AUC of 0.814 (p = .008). The radiomic model alone did not reach statistical significance (p = .07). A combined model incorporating radiomic features and the Ki67 index effectively predicts disease progression in GEP-NET patients eligible for SSA treatment.