Seyithanoglu Deniz, Durak Gorkem, Keles Elif, Medetalibeyoglu Alpay, Hong Ziliang, Zhang Zheyuan, Taktak Yavuz B, Cebeci Timurhan, Tiwari Pallavi, Velichko Yuri S, Yazici Cemal, Tirkes Temel, Miller Frank H, Keswani Rajesh N, Spampinato Concetto, Wallace Michael B, Bagci Ulas
Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey.
Cancers (Basel). 2024 Dec 22;16(24):4268. doi: 10.3390/cancers16244268.
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和肿瘤性病变，在诊断和管理方面带来了重大挑战。虽然一些PCLs是胰腺癌的前驱病变，但其他的仍为良性，因此需要进行准确鉴别以实现最佳的患者护理。传统的PCL管理方法严重依赖于影像学检查、内镜超声（EUS）引导下的细针穿刺抽吸（FNA），以及临床和生化数据。然而，图像解读的观察者依赖性以及PCLs的复杂形态可能导致诊断的不确定性和患者管理策略的变异性。本综述批判性地评估了当前PCL的诊断和监测实践，展示了不同病变的特征，并强调了传统方法的潜在局限性。然后，我们探讨了人工智能（AI）改变PCL管理的潜力。人工智能驱动的策略，包括用于自动胰腺和病变分割的深度学习算法，以及用于分析异质性的放射组学，可以提高诊断准确性和风险分层。这些先进技术可以提供更客观、可重复的评估，帮助临床医生在随访间隔和手术干预的决策中提供帮助。早期结果表明，人工智能驱动的方法可以通过更早地检测高危病变和减少对良性囊肿的不必要操作，显著改善患者的治疗效果。最后，本综述强调，人工智能驱动的方法可能会重塑PCL管理的格局，最终改善胰腺癌的预防。
