Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance

• Published in: European journal of nuclear medicine and molecular imaging Vol. 48; no. 12; pp. 4002 - 4015
• Main Authors: Bezzi, C., Mapelli, P., Presotto, L., Neri, I., Scifo, P., Savi, A., Bettinardi, V., Partelli, S., Gianolli, L., Falconi, M., Picchio, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021; Springer Nature B.V
• Subjects: Advanced Image Analyses (Radiomics and Artificial Intelligence); Cardiology; Clinical significance; Diagnosis; Diagnosis, Differential; Differential diagnosis; Humans; Imaging; Medicine; Medicine & Public Health; Neoplasms; Neuroendocrine tumors; Neuroendocrine Tumors - diagnostic imaging; Nuclear Medicine; Oncology; Optimization; Orthopedics; Pancreas; Pancreatic cancer; Pancreatic Neoplasms - diagnostic imaging; Predictions; Radiology; Radiomics; Reproducibility of Results; Review Article; Therapeutic applications; Tumors; Workflow; Pancreatic neuroendocrine tumors; CT; Texture analysis; MRI; PET; Radiomics
• ISSN: 1619-7070; 1619-7089
• DOI: 10.1007/s00259-021-05338-8

Purpose To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. Methods Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. Results Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. Conclusions Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.