Integrating radiomics and 3D bioprinted tumor model: the next-generation approach to improve pancreatic cancer treatment
Abstract
from whole-pancreas CT images can accurately differentiate PC from normal pancreatic tissue, with a sensitivity of 100% and a specificity of 98.5% (6). Accurate tumor staging and prognosis assessment are crucial for determining the most appropriate treatment strategy. Radiomics provides quantitative data that reveals tumor heterogeneity, which is closely linked to tumor grade, vascular invasion, and metastasis, thereby facilitating more precise tumor staging and prognosis prediction (7). Additionally, radiomic features can offer insights into overall survival and recurrence risk, guiding personalized treatment planning. Wu et al. (8) developed an interpretable machine learning model that integrates intratumoral and peritumoral radiomic features with CT-derived body composition parameters to accurately predict early postoperative recurrence of PC, achieving excellent performance across multiple validation cohorts
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