CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study. (March 2023)

• Record Type: Journal Article
• Authors: Staal, Femke CR; Taghavi, M; Hong, Eun K; Tissier, Renaud; van Treijen, Mark; Heeres, Birthe C; van der Zee, Dennis; Tesselaar, Margot ET; Beets-Tan, Regina GH; Maas, Monique
• Is Part Of: Acta radiologica. Volume 64:Number 3(2023)
• Page Start: 1062
• Page End: 1070
• Publication Date: 2023-03
• Subjects: Radiomics -- computed tomography texture analysis -- neuroendocrine tumor -- hepatic metastasis -- response evaluation -- RECIST -- NET; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/02841851221106598
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment. Purpose: To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment. Material and Methods: A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model. Results: In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1–50 months). Radiomics models could not accurately classify response (area under the curve 0.44–0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model. Conclusion: Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.