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Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1- 13 C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised 13 C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of 13 C-pyruvate to 13 C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI. This review will focus on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment. A comparison of hyperpolarised 13 C-MRI with state-of-the-art multiparametric MRI is likely to reveal the unique additional information and applications offered by the technique.

Objectives(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies.MethodsIn this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed.ResultsFifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93–0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27–9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC.ConclusionsRadiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures.Key Points• Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons.• A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score.• Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.

Inhibition of the chemokine receptor CXCR4 in combination with blockade of the PD-1/PD-L1 T cell checkpoint induces T cell infiltration and anticancer responses in murine and human pancreatic cancer. Here we elucidate the mechanism by which CXCR4 inhibition affects the tumor immune microenvironment. In human immune cell-based chemotaxis assays, we find that CXCL12-stimulated CXCR4 inhibits the directed migration mediated by CXCR1, CXCR3, CXCR5, CXCR6, and CCR2, respectively, chemokine receptors expressed by all of the immune cell types that participate in an integrated immune response. Inhibiting CXCR4 in an experimental cancer medicine study by 1-wk continuous infusion of the small-molecule inhibitor AMD3100 (plerixafor) induces an integrated immune response that is detected by transcriptional analysis of paired biopsies of metastases from patients with microsatellite stable colorectal and pancreatic cancer. This integrated immune response occurs in three other examples of immunemediated damage to noninfected tissues: Rejecting renal allografts, melanomas clinically responding to anti-PD1 antibody therapy, and microsatellite instable colorectal cancers. Thus, signaling by CXCR4 causes immune suppression in human pancreatic ductal adenocarcinoma and colorectal cancer by impairing the function of the chemokine receptors that mediate the intratumoral accumulation of immune cells.

Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2−), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2− invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.

Vascular calcification is a complex biological process that is a hallmark of atherosclerosis. While macrocalcification confers plaque stability, microcalcification is a key feature of high-risk atheroma and is associated with increased morbidity and mortality. Positron emission tomography and X-ray computed tomography (PET/CT) imaging of atherosclerosis using 18 F-sodium fluoride ( 18 F-NaF) has the potential to identify pathologically high-risk nascent microcalcification. However, the precise molecular mechanism of 18 F-NaF vascular uptake is still unknown. Here we use electron microscopy, autoradiography, histology and preclinical and clinical PET/CT to analyse 18 F-NaF binding. We show that 18 F-NaF adsorbs to calcified deposits within plaque with high affinity and is selective and specific. 18 F-NaF PET/CT imaging can distinguish between areas of macro- and microcalcification. This is the only currently available clinical imaging platform that can non-invasively detect microcalcification in active unstable atherosclerosis. The use of 18 F-NaF may foster new approaches to developing treatments for vascular calcification. Atherosclerotic plaques with macrocalcification are stable, whereas microcalcification is a key feature of rupture-prone plaques. Here the authors show that 18 F-NaF PET/CT imaging can distinguish between macro- and microcalcification providing a potential, non-invasive imaging technique to identify patients with high-risk atheroma.

Deuterium metabolic imaging (DMI) and hyperpolarized 13C-pyruvate MRI (13C-HPMRI) are two emerging methods for non-invasive and non-ionizing imaging of tissue metabolism. Imaging cerebral metabolism has potential applications in cancer, neurodegeneration, multiple sclerosis, traumatic brain injury, stroke, and inborn errors of metabolism. Here we directly compare these two non-invasive methods at 3 T for the first time in humans and show how they simultaneously probe both oxidative and non-oxidative metabolism. DMI was undertaken 1–2 h after oral administration of [6,6′-2H2]glucose, and 13C-MRI was performed immediately following intravenous injection of hyperpolarized [1–13C]pyruvate in ten and nine normal volunteers within each arm respectively. DMI was used to generate maps of deuterium-labelled water, glucose, lactate, and glutamate/glutamine (Glx) and the spectral separation demonstrated that DMI is feasible at 3 T. 13C-HPMRI generated maps of hyperpolarized carbon-13 labelled pyruvate, lactate, and bicarbonate. The ratio of 13C-lactate/13C-bicarbonate (mean 3.7 ± 1.2) acquired with 13C-HPMRI was higher than the equivalent 2H-lactate/2H-Glx ratio (mean 0.18 ± 0.09) acquired using DMI. These differences can be explained by the route of administering each probe, the timing of imaging after ingestion or injection, as well as the biological differences in cerebral uptake and cellular physiology between the two molecules. The results demonstrate these two metabolic imaging methods provide different yet complementary readouts of oxidative and reductive metabolism within a clinically feasible timescale. Furthermore, as DMI was undertaken at a clinical field strength within a ten-minute scan time, it demonstrates its potential as a routine clinical tool in the future.

MRI is a widely available clinical tool for cancer diagnosis and treatment monitoring. MRI provides excellent soft tissue imaging, using a wide range of contrast mechanisms, and can non-invasively detect tissue metabolites. These approaches can be used to distinguish cancer from normal tissues, to stratify tumor aggressiveness, and to identify changes within both the tumor and its microenvironment in response to therapy. In this review, the role of MRI in immunotherapy monitoring will be discussed and how it could be utilized in the future to address some of the unique clinical questions that arise from immunotherapy. For example, MRI could play a role in identifying pseudoprogression, mixed response, T cell infiltration, cell tracking, and some of the characteristic immune-related adverse events associated with these agents. The factors to be considered when developing MRI imaging biomarkers for immunotherapy will be reviewed. Finally, the advantages and limitations of each approach will be discussed, as well as the challenges for future clinical translation into routine clinical care. Given the increasing use of immunotherapy in a wide range of cancers and the ability of MRI to detect the microstructural and functional changes associated with successful response to immunotherapy, the technique has great potential for more widespread and routine use in the future for these applications.

Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T1 and T2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7–1.3% for T1, 2.0–7.8% for T2, 1.4–2.5% for normalized PD) and reproducibility (CVs of 2.0–5.8% for T1, 7.4–10.2% for T2, 5.2–9.2% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T1 and T2, supporting the translation of MRF-based fast quantitative imaging into clinical applications.

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naïve patient and by leveraging the analytical power of multi-sample analyses, we reveal evidence of diversification among metastatic lineages. UV-induced mutations dominate the trunk, whereas APOBEC-associated mutations are found in the branches of the evolutionary tree. Multi-sample analyses from a further seven patients confirmed that lineage diversification was pervasive, representing an important mode of melanoma dissemination. Our analyses demonstrate that joint analysis of cancer cell fraction estimates across multiple metastases can uncover previously unrecognised levels of tumour heterogeneity and highlight the limitations of inferring heterogeneity from a single biopsy. Metastatic melanoma is associated with a poor prognosis and understanding the genetic features of metastases may enable better treatment strategies. Here, the authors analyse multiple metastases from individual patients finding high levels of heterogeneity in metastases from different organs.

Background Tumour vascular density assessed from CD-31 immunohistochemistry (IHC) images has previously been shown to have prognostic value in breast cancer. Current methods to measure vascular density, however, are time-consuming, suffer from high inter-observer variability and are limited in describing the complex tumour vasculature morphometry. Methods We propose a method for automatically measuring a range of vascular parameters from CD-31 IHC images, which together provide a detailed description of the vasculature morphology. We first used a U-Net based convolutional neural network, trained and validated using 36 partially annotated whole slide images from 27 patients, to segment vessel structures and tumour regions from which the measurements are taken. The model also segments the vascular smooth muscle, benign epithelium, adipose tissue, stroma, lymphocyte clusters, nerves and CD-31 positive leukocytes, and we applied it to an additional 21 images from 15 patients. Using these segmentations, we investigated the relationship between the various tissue types and the vasculature and studied the relationship of various vascular parameters with clinical parameters. We also performed a 3D histology analysis on a separate tumour sample as a proof of principle, providing a more comprehensive visualization of vasculature morphology compared to the standard 2D cross-section of a tissue sample. Results Using two-way cross-validation, we show that vessels were accurately segmented, with Dice scores of 0.875 and 0.856, and were accurately identified, with F1 scores of 0.777 and 0.748. All vascular parameters exhibit strong ( r > 0.7 ) and significant ( p <0.001) correlations with measurements taken from the manual ground truth vessel segmentations. A significant relationship between the major/minor axis ratio, a measure of elongation, and the tumour grade was found. Conclusion Our proposed method shows promise as a tool for studying the tumour vasculature and its relationship with surrounding cells and tissue types. Furthermore, the correlation with tumour grade highlights the clinical relevance of our approach. These findings suggest that our method could have substantial implications for improving prognostic assessments and personalizing therapeutic strategies in breast cancer treatment.