Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer.
Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer.
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DOI：
10.2196/26601
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发表时间：
2021-06-17
影响因子：
3.2
通讯作者：
Shams S
中科院分区：
文献类型：
--
作者：
Enriquez JS;Chu Y;Pudakalakatti S;Hsieh KL;Salmon D;Dutta P;Millward NZ;Lurie E;Millward S;McAllister F;Maitra A;Sen S;Killary A;Zhang J;Jiang X;Bhattacharya PK;Shams S
目前对无创影像标志物的需求尚未得到满足，这些标志物可以帮助在诊断和早期时间点识别胰腺导管腺癌（PDAC）的侵袭性亚型，并在肿瘤缩小前评估治疗效果。在过去的几年中，有两个主要的发展可能对建立PDAC和胰腺癌恶性前病变的成像生物标志物产生重大影响：(1)超极化代谢(HP)-磁共振（MR），它将传统MR的灵敏度提高了10,000倍以上，实现了实时代谢测量；(2)人工智能的应用。我们这篇综述的目的是讨论这两个令人兴奋但独立的发展（HP-MR和AI）在PDAC成像和检测领域，从现有的文献到目前为止。按照PRISMA范围审查扩展指南（PRISMA- scr）进行系统审查。最近临床指南中引用的关于利用HP-MR和/或AI对PDAC患者进行早期检测、侵袭性评估和早期治疗效果的研究摘自PubMed和谷歌Scholar数据库。根据预先定义的排除和纳入标准对研究进行了回顾，并根据HP-MR和/或AI在PDAC诊断中的应用进行了分组。本综述的部分目的是强调通过任何成像方式早期发现胰腺癌的知识差距，并强调人工智能和HP-MR如何解决这一关键差距。我们回顾了HP-MR在PDAC中的应用，包括6项临床前研究和1项临床试验。我们还回顾了几篇与hp - mr相关的文章，这些文章描述了在PDAC中具有许多功能应用的新探针。在人工智能方面，我们回顾了所有符合我们关于人工智能应用于评估PDAC中的计算机断层扫描（CT）和MR图像的纳入标准的现有论文。随着人工智能的出现及其跨多模态数据学习的独特能力，以及使用HP-MR的敏感代谢成像，PDAC中的这一知识差距可以得到充分解决。CT是一种可获得和广泛的成像方式，因为它是负担得起的；仅因为这个原因，大多数讨论的数据都是基于CT成像数据集。虽然本综述中纳入的MR相关论文相对较少，但我们相信随着MR成像和HP-MR的快速采用，在不久的将来会有更多关于胰腺癌成像的临床数据。人工智能、HP-MR和胰腺癌多模态成像信息的整合可能会导致PDAC早期检测、评估侵袭性和早期疗效的实时生物标志物的发展。
There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR–related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.
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影响因子：
5.5
作者：
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通讯作者：
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