Lekkas Georgios, Vrochidou Eleni, Papakostas George A
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece.
Bioengineering (Basel). 2025 Aug 6;12(8):849. doi: 10.3390/bioengineering12080849.
The advancement of artificial intelligence (AI), deep learning, and radiomics has introduced novel methodologies for the detection, classification, prognosis, and treatment evaluation of pancreatic ductal adenocarcinoma (PDAC). As the integration of AI into medical imaging continues to evolve, its potential to enhance early detection, refine diagnostic precision, and optimize treatment strategies becomes increasingly evident. However, despite significant progress, various challenges remain, particularly in terms of clinical applicability, generalizability, interpretability, and integration into routine practice. Understanding the current state of research is crucial for identifying gaps in the literature and exploring opportunities for future advancements. This literature review aims to provide a comprehensive overview of the existing studies on AI applications in PDAC, with a focus on disease detection, classification, survival prediction, treatment response assessment, and radiogenomics. By analyzing the methodologies, findings, and limitations of these studies, we aim to highlight the strengths of AI-driven approaches while addressing critical gaps that hinder their clinical translation. Furthermore, this review aims to discuss future directions in the field, emphasizing the need for multi-institutional collaborations, explainable AI models, and the integration of multi-modal data to advance the role of AI in personalized medicine for PDAC.
人工智能（AI）、深度学习和放射组学的发展为胰腺导管腺癌（PDAC）的检测、分类、预后评估和治疗评价引入了新方法。随着AI在医学成像中的整合不断发展，其在提高早期检测、提升诊断精度和优化治疗策略方面的潜力日益明显。然而，尽管取得了重大进展，但仍存在各种挑战，特别是在临床适用性、通用性、可解释性以及融入常规实践方面。了解当前的研究现状对于识别文献中的差距以及探索未来进展的机会至关重要。这篇文献综述旨在全面概述关于AI在PDAC中应用的现有研究，重点关注疾病检测、分类、生存预测、治疗反应评估和放射基因组学。通过分析这些研究的方法、结果和局限性，我们旨在突出AI驱动方法的优势，同时解决阻碍其临床转化的关键差距。此外，本综述旨在讨论该领域的未来方向，强调多机构合作、可解释AI模型以及多模态数据整合的必要性，以推动AI在PDAC个性化医疗中的作用。
