Congress:
ECR25
Poster Number:
C-12773
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
K. Bartnik1, T. Bartczak2, M. Krzyziński2, K. Korzeniowski1, K. J. Lamparski1, T. Wróblewski2, K. Mech2, M. M. Januszewicz1, P. Biecek2; 1Warszawa/PL, 2Warsaw/PL
Disclosures:
Krzysztof Bartnik:
Grant Recipient: Integra WUM-PW (No. 1W12/ INTEGRA.1.6/N/23)
Tomasz Bartczak:
Nothing to disclose
Mateusz Krzyziński:
Nothing to disclose
Krzysztof Korzeniowski:
Nothing to disclose
Krzysztof Jacek Lamparski:
Nothing to disclose
Tadeusz Wróblewski:
Nothing to disclose
Katarzyna Mech:
Nothing to disclose
Magdalena Maria Januszewicz:
Nothing to disclose
Przemysław Biecek:
Nothing to disclose
Keywords:
Artificial Intelligence, Liver, Oncology, CT, CT-Quantitative, Chemoembolisation, Outcomes analysis, Cancer, Cirrhosis
This retrospective dataset includes extensive clinical data, baseline multiphase CT imaging, segmentations, and radiomics features of 233 treatment-naive patients. Inclusion criteria: 1) unresectable HCC; 2) conventional TACE; 3) Child-Pugh Class A/B; 4) baseline contrast-enhanced CT. Exclusion criteria: 1) liver transplantation, resection, or ablation; 2) other neoplasms. Outcome measures included overall survival (OS), progression-free survival (PFS), and TACE response. Segmentation masks for various internal organs were generated using nnU-Net model, while HCC were manually segmented in Slicer 3D by experienced radiologists. Radiomic features were extracted using PyRadiomics, following the Imaging Biomarker Standardization Initiative.