Alterations in metabolism following radiotherapy affect therapeutic efficacy, although the mechanism underlying such alterations is unclear. A new imaging technique—named dynamic nuclear polarization (DNP) carbon-13 magnetic resonance imaging (MRI)—probes the glycolytic flux in a real-time, dynamic manner. The [1-¹³C]pyruvate is transported by the monocarboxylate transporter (MCT) into cells and converted into [1-¹³C]lactate by lactate dehydrogenase (LDH). To capture the early glycolytic alterations in the irradiated cancer and immune cells, we designed a preliminary DNP ¹³C-MRI study by using hyperpolarized [1-¹³C]pyruvate to study human FaDu squamous carcinoma cells, HMC3 microglial cells, and THP-1 monocytes before and after irradiation. The pyruvate-to-lactate conversion rate (k_PL [Pyr.]) calculated by kinetic modeling was used to evaluate the metabolic alterations. Western blotting was performed to assess the expressions of LDHA, LDHB, MCT1, and MCT4 proteins. Following irradiation, the pyruvate-to-lactate conversion rates on DNP ¹³C-MRI were significantly decreased in the FaDu and the HMC3 cells but increased in the THP-1 cells. Western blot analysis confirmed the similar trends in LDHA and LDHB expression levels. In conclusion, DNP ¹³C-MRI non-invasively captured the different glycolytic alterations among cancer and immune systems in response to irradiation, implying its potential for clinical use in the future.

Background: Immune checkpoint inhibitors (ICI) are promising treatment options for various cancers. However, their use is associated with immune-related adverse events (irAEs), including ICI-induced diabetes mellitus (ICI-DM). This study aimed to investigate the clinical features of ICI-DM, with a particular focus on alterations to pancreatic volume. Methods: We conducted a retrospective review of 2829 patients who received ICI treatment at the Chang Gung Memorial Hospital, Linkou, between January 2014 and December 2021. New-onset diabetes or diabetic ketoacidosis (DKA) was identified in ten patients receiving ICI therapy. Pancreatic volumes were assessed by manual segmentation of computed tomography (CT) images before and after ICI-DM diagnosis. Results: Among these ten patients, nivolumab was the most commonly used ICI (50.0%), followed by pembrolizumab (30.0%) and atezolizumab (20.0%). One patient received combination therapy with nivolumab and ipilimumab. The median age was 63.01 years (range: 40.1 − 87.8). ICI-DM developed after a median of 13.5 cycles (range: 2 − 42) of ICI treatment or 9.85 months (range:1.5 − 21.3) since ICI initiation. The initial presentation was DKA in 60.0% of patients. All patients had low or undetectable C-peptide levels (range: <0.033 − 0.133 nmol/L) and were negative for most type 1 diabetes mellitus (T1DM)-related autoantibodies; only one patient tested positive for glutamic acid decarboxylase antibodies. CT imaging revealed significant pancreatic atrophy, with a median pancreatic volume decrease of 19.92% (P = 0.038) from baseline and sustained significant decline at last follow-up (median − 37.14%, P = 0.012). Conclusions: ICI-DM is often accompanied by pancreatic atrophy and approximately two-thirds of patients initially present with DKA. Although the majority of ICI-DM patients lack T1DM-related autoantibodies, identifying diminished pancreatic volumes through CT imaging provides valuable clues into the subclinical aspects of ICI-DM development, aiding in the prevention of diabetic emergencies. Trial registration: Not applicable.

Hyperpolarized (HP) magnetic resonance imaging (MRI) is a groundbreaking imaging platform advancing from research to clinical practice, offering new possibilities for real-time, non-invasive metabolic imaging. This review explores the latest advancements, challenges, and future directions of HP MRI, emphasizing its transformative impact on both translational research and clinical applications. By employing techniques such as dissolution Dynamic Nuclear Polarization (dDNP), Parahydrogen-Induced Polarization (PHIP), Signal Amplification by Reversible Exchange (SABRE), and Spin-Exchange Optical Pumping (SEOP), HP MRI achieves enhanced nuclear spin polarization, enabling in vivo visualization of metabolic pathways with exceptional sensitivity. Current challenges, such as limited imaging windows, complex pre-scan protocols, and data processing difficulties, are addressed through innovative solutions like advanced pulse sequences, bolus tracking, and kinetic modeling. We highlight the evolution of HP MRI technology, focusing on its potential to revolutionize disease diagnosis and monitoring by revealing metabolic processes beyond the reach of conventional MRI and positron emission tomography (PET). Key advancements include the development of novel tracers like [2-13C]pyruvate and [1-13C]-alpha-ketoglutarate and improved data analysis techniques, broadening the scope of clinical metabolic imaging. Future prospects emphasize integrating artificial intelligence, standardizing imaging protocols, and developing new hyperpolarized agents to enhance reproducibility and expand clinical capabilities particularly in oncology, cardiology, and neurology. Ultimately, we envisioned HP MRI as a standardized modality for dynamic metabolic imaging in clinical practice.

Chromosomal instability (CIN) of gastric cancer is correlated with distinct outcomes. This study aimed to investigate the role of computed tomography (CT) imaging traits in predicting the CIN status of gastric cancer. We screened 443 patients in the Cancer Genome Atlas gastric cancer cohort to filter 40 patients with complete CT imaging and genomic data as the training cohort. CT imaging traits were subjected to logistic regression to select independent predictors for the CIN status. For the validation cohort, we prospectively enrolled 18 gastric cancer patients for CT and tumor genomic analysis. The imaging predictors were tested in the validation cohort using receiver operating characteristic curve (ROC) analysis. Thirty patients (75%) in the training cohort and 9 patients (50%) in the validation cohort had CIN subtype gastric cancers. Smaller tumor diameter (p = 0.017) and acute tumor transition angle (p = 0.045) independently predict CIN status in the training cohort. In the validation cohort, acute tumor transition angle demonstrated the highest accuracy, sensitivity, and specificity of 88.9%, 88.9%, and 88.9%, respectively, and areas under ROC curve of 0.89. In conclusion, this pilot study showed acute tumor transition angle on CT images may predict the CIN status of gastric cancer.

Lai YC, et al. Korean J Radiol. 2024 May;25(5):459-472. https://doi.org/10.3348/kjr.2024.0069