Antony Ajith, Mukherjee Sovanlal, Bi Yan, Collisson Eric A, Nagaraj Madhu, Murlidhar Murlidhar, Wallace Michael B, Goenka Ajit H
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Department of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL, USA.
Abdom Radiol (NY). 2024 Dec 30. doi: 10.1007/s00261-024-04775-x.
Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.
胰腺导管腺癌（PDAC）是美国癌症相关死亡的第三大主要原因，这主要归因于其较差的五年生存率以及频繁的晚期诊断。即使在高危人群中，早期检测的一个重大障碍是胰腺在诊断前阶段通常形态正常。然而，该疾病可从亚临床阶段迅速发展为广泛转移，从而削弱了筛查的有效性。最近，应用于横断面成像的人工智能（AI）在识别胰腺组织中人类肉眼通常难以察觉的细微早期变化方面显示出巨大潜力。此外，人工智能驱动的成像还有助于发现预后和预测生物标志物，这对于个性化治疗规划至关重要。本文独特地整合了关于人工智能在诊断前成像中检测隐匿性PDAC作用的批判性讨论，解决了模型通用性的挑战，并强调了标准化数据集和临床工作流程等解决方案。通过关注技术进步和实际应用，本文提供了一个前瞻性的概念框架，弥合了当前人工智能驱动的PDAC研究中的差距。
