1. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance
- Author
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Massimo Falconi, Luca Presotto, Carolina Bezzi, Paola Mapelli, Maria Picchio, Annarita Savi, Valentino Bettinardi, L. Gianolli, Paola Scifo, Ilaria Neri, Stefano Partelli, Bezzi, C, Mapelli, P, Presotto, L, Neri, I, Scifo, P, Savi, A, Bettinardi, V, Partelli, S, Gianolli, L, Falconi, M, Picchio, M, Bezzi, C., Mapelli, P., Presotto, L., Neri, I., Scifo, P., Savi, A., Bettinardi, V., Partelli, S., Gianolli, L., Falconi, M., and Picchio, M.
- Subjects
medicine.medical_specialty ,Treatment response ,Context (language use) ,Neuroendocrine tumors ,030218 nuclear medicine & medical imaging ,Diagnosis, Differential ,03 medical and health sciences ,0302 clinical medicine ,Radiomics ,Pancreatic neuroendocrine tumor ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Medical physics ,Clinical significance ,Pancreatic neuroendocrine tumors ,Texture analysi ,business.industry ,Reproducibility of Results ,General Medicine ,medicine.disease ,Pancreatic Neoplasms ,Neuroendocrine Tumors ,PET ,Texture analysis ,030220 oncology & carcinogenesis ,Radiomic ,business ,CT ,MRI - Abstract
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.
- Published
- 2021