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Congress: ECR24
Poster Number: C-14122
Type: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2024/C-14122
Authorblock: M. Scheschenja, J. Wessendorf, M. Bastian, S. Viniol, J. Jedelská, A. König, A. H. Mahnken; Marburg/DE
Disclosures:
Michael Scheschenja: Nothing to disclose
Joel Wessendorf: Nothing to disclose
Moritz Bastian: Nothing to disclose
Simon Viniol: Nothing to disclose
Jarmila Jedelská: Nothing to disclose
Alexander König: Nothing to disclose
Andreas H. Mahnken: Nothing to disclose
Keywords: Artificial Intelligence, Interventional vascular, Oncology, CT, CT-Angiography, Chemoembolisation, Outcomes analysis, Segmentation, Cancer, Endocrine disorders, Neoplasia
Purpose

Neuroendocrine neoplasms (NENs) represent rare and heterogeneous malignancies, predominantly manifesting within the gastrointestinal tract and lungs. At initial diagnosis, between 12 to 22 percent of cases present with metastases [1, 2].

The diverse nature of NENs poses a challenge in determining optimal therapeutic strategies, often necessitating a multidisciplinary approach for tailored decision-making. One such therapeutic approach for managing liver metastases involves Transarterial Chemoembolization (TACE) [3, 4]. However, discerning its suitability remains a complex task.

Radiomics emerges as a potential solution, offering support in therapy selection and decision-making processes. Notably, in NENs, radiomics has been employed to predict therapy response, particularly in treatment with everolimus [5]. While other studies have explored its utility in predicting therapy response in hepatocellular carcinoma patients undergoing TACE [6], its application in predicting therapy response after TACE for NEN patients remains uncharted territory.

GALLERY