Lee Junhao, Lin Tingting, He Yifei, Wu Ye, Qin Jiaolong
School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, China.
Department of Medical and Radiation Oncology, Affiliated Sanming First Hospital of Fujian Medical University, Sanming, China.
Med Oncol. 2025 May 28;42(7):222. doi: 10.1007/s12032-025-02759-5.
Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imaging methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), struggle to assess tumor characteristics and vascular involvement, which are crucial for treatment planning. This paper explores the potential of diffusion magnetic resonance imaging (dMRI) in enhancing pancreatic cancer diagnosis and treatment. Diffusion-based techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), combined with emerging AI‑powered analysis, provide insights into tissue microstructure, allowing for earlier detection and improved evaluation of tumor cellularity. These methods may help assess prognosis and monitor therapy response by tracking diffusion and perfusion metrics. However, challenges remain, such as standardized protocols and robust data analysis pipelines. Ongoing research, including deep learning applications, aims to improve reliability, and dMRI shows promise in providing functional insights and improving patient outcomes. Further clinical validation is necessary to maximize its benefits.
胰腺癌是一种侵袭性很强的恶性肿瘤，其发病率和死亡率不断上升，通常在晚期才被诊断出来。传统的成像方法，如计算机断层扫描（CT）和磁共振成像（MRI），难以评估肿瘤特征和血管受累情况，而这些对于治疗方案的制定至关重要。本文探讨了扩散磁共振成像（dMRI）在增强胰腺癌诊断和治疗方面的潜力。基于扩散的技术，如扩散加权成像（DWI）、扩散张量成像（DTI）、体素内不相干运动（IVIM）和扩散峰度成像（DKI），与新兴的人工智能驱动分析相结合，能够深入了解组织微观结构，实现肿瘤细胞的早期检测和更好评估。这些方法可通过追踪扩散和灌注指标，帮助评估预后并监测治疗反应。然而，挑战依然存在，如标准化方案和强大的数据分析流程。正在进行的研究，包括深度学习应用，旨在提高可靠性，dMRI在提供功能见解和改善患者预后方面显示出前景。需要进一步的临床验证以最大化其益处。
