Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.
胰腺囊性病变（PCLs）涵盖了一系列具有不同恶性潜能的非肿瘤性与肿瘤性病变，其诊断与临床管理面临重大挑战。部分PCLs是胰腺癌的癌前病变，而另一些则始终保持良性，因此需要精确鉴别以实现最佳临床管理。目前PCLs的常规管理策略主要依赖影像学检查、超声内镜引导下细针穿刺活检，并结合临床与生化数据。然而，图像判读的主观依赖性及PCLs复杂的形态学特征，常导致诊断不确定性和临床管理策略的差异。本文系统评述当前PCLs的诊断与监测实践，阐明各类病变的特征，并指出传统方法的潜在局限性。进而探讨人工智能在革新PCLs管理中的潜力：通过深度学习算法实现胰腺及病灶的自动分割，运用影像组学分析异质性特征，这些人工智能驱动策略可提升诊断准确性与风险分层能力。这些先进技术能提供更客观、可重复的评估，辅助临床医生制定随访周期和手术干预决策。早期研究显示，人工智能方法可通过早期识别高风险病灶、减少对良性囊肿的不必要干预，显著改善患者预后。最后本文强调，人工智能驱动策略有望重塑PCLs管理格局，最终推动胰腺癌预防水平的提升。
Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations
肿瘤(癌症)患者之家