Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer

Abstract

Background: 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).

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: 13C; HP-MR; MRI; artificial intelligence; assessment of treatment response; cancer; deep learning; detection; early detection; efficacy; hyperpolarization; imaging; marker; metabolic imaging; pancreatic cancer; pancreatic ductal adenocarcinoma; probes; review; treatment.

Figure 1

(a) Schematic showing pyruvate metabolism inside a cell. The [1-13C] pyruvate can be converted to 13C-lactate, 13C-alanine, and 13C-bicarbonate in the presence of enzymes lactate dehydrogenase-A (LDHA), alanine transferase (ALT), and pyruvate decarboxylase, respectively. (b) Downstream products of pyruvate metabolism such as lactate and alanine can be imaged using hyperpolarized magnetic resonance. A 3D, real-time readout of the signals, as shown here, can be created using standard software such as Chenomx.

Figure 2

Cartoon showing the challenges of imaging pancreatic cancer at early stages and how artificial intelligence can interface with hyperpolarized magnetic resonance (HP-MR), anatomical magnetic resonance imaging (MRI), and pathology data toward developing biomarkers of pancreatic cancer premalignancy. This approach may become the standard of care in the clinic of the future. CT: computed tomography.

Figure 3

PRISMA flow chart showing the selection criteria of the publications to include in this review. AI: artificial intelligence; PDAC: pancreatic ductal adenocarcinoma.

Figure 4

Schematic illustrating the concept of leveraging anatomical magnetic resonance imaging (MRI), hyperpolarized magnetic resonance (HP-MR), and artificial intelligence as complementary modalities toward developing actionable biomarkers of pancreatic ductal adenocarcinoma. CNNs: convolutional neural networks; EHR: electronic health record.