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Abstract Suspensions of oxygen‐filled bubbles are under active investigation as potential means of relieving tissue hypoxia. Intravenous administration of large quantities of bubbles is, however, undesirable. Previous work by the authors has demonstrated that tumor oxygen levels can be increased following oral administration of phospholipid stabilized oxygen nanobubbles. The aim of this study was to determine whether this would enhance the efficacy of sonodynamic therapy (SDT), which is known to be inhibited in hypoxic tissue. Experiments were conducted in a murine model of pancreatic cancer. Animals were treated with SDT (intratumoural injection of 1 mM Rose Bengal followed by exposure to 1 MHz ultrasound, 0.1 kHz pulse repetition frequency, 30% duty cycle, 3.5 W cm−2 for 3.5 minutes) either with or without a prior gavage of oxygen bubbles. A statistically significant reduction in the rate of tumor growth was observed in the groups receiving oxygen nanobubbles either 5 or 20 minutes before SDT. Separate measurements of tumor oxygen using a fiber optic probe and expression of hypoxia inducible factor (HIF)1α following tumor excision, confirmed the change in tumor oxygen levels. These findings offer a potentially promising new approach to relieving tissue hypoxia in order to facilitate cancer therapy.

Background and purpose: This study aimed to assess the role of T1 mapping and oxygen-enhanced MRI in patients undergoing radical dose radiotherapy for HPV positive oropharyngeal cancer, which has not yet been examined in an OE-MRI study. Materials and methods: Variable Flip Angle T1 maps were acquired on a 3T MRI scanner while patients (n = 12) breathed air and/or 100 % oxygen, before and after fraction 10 of the planned 30 fractions of chemoradiotherapy (‘visit 1’ and ‘visit 2’, respectively). The analysis aimed to assess to what extent (1) native R1 relates to patient outcome; (2) OE-MRI response relates to patient outcome; (3) changes in mean R1 before and after radiotherapy related to clinical outcome in patients with oropharyngeal squamous cell carcinoma. Results: Due to the radiotherapy being largely successful, the sample sizes of non-responder groups were small, and therefore it was not possible to properly assess the predictive nature of OE-MRI. The tumour R1 increased in some patients while decreasing in others, in a pattern that was overall consistent with the underlying OE-MRI theory and previously reported tumour OE-MRI responses. In addition, we discuss some practical challenges faced when integrating this technique into a clinical trial, with the aim that sharing this is helpful to researchers planning to use OE-MRI in future clinical studies. Conclusion: Altogether, these results suggest that further clinical OE-MRI studies to assess hypoxia and radiotherapy response are worth pursuing, and that there is important work to be done to improve the robustness of the OE-MRI technique in human applications in order for it to be useful as a widespread clinical technique.

Background and purpose: Oxygen-enhanced magnetic resonance imaging (MRI) and T1-mapping was used to explore its effectiveness as a prognostic imaging biomarker for chemoradiotherapy outcome in anal squamous cell carcinoma. Materials and methods: T2-weighted, T1 mapping, and oxygen-enhanced T1 maps were acquired before and after 8–10 fractions of chemoradiotherapy and examined whether the oxygen-enhanced MRI response relates to clinical outcome. Patient response to treatment was assessed 3 months following completion of chemoradiotherapy. A mean T1 was extracted from manually segmented tumour regions of interest and a paired two-tailed t-test was used to compare changes across the patient population. Regions of subcutaneous fat and muscle tissue were examined as control ROIs. Results: There was a significant increase in T1 of the tumour ROIs across patients following the 8–10 fractions of chemoradiotherapy (paired t-test, p

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors ΔBrainAge=AgePredicted−AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between ΔBrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrainAge from models such as this network were predictive of any health outcomes.

Abstract Spatially Targeted Mass Spectrometry (MS) analysis using survey scans with an imaging modality often requires consecutive tissue slices, because of the tissue damage during survey scan or due to incompatible sample preparation requirements between the survey modality and MS. We report two spatially targeted MS analysis workflows based on polarized light imaging guidance that use the same tissue sample for survey and targeted analysis. The first workflow is applicable for thin-slice analysis, and uses transmission-polarimetry-guided Desorption ElectroSpray Ionization Mass Spectrometry (DESI-MS), and confirmatory H&E histopathology analysis on the same slice; this is validated using quantitative digital pathology methods. The second workflow explores a polarimetry-guided MS platform for thick tissue assessment by developing reflection-mode polarimetric imaging coupled with a hand-held Picosecond InfraRed Laser (PIRL) MS ablation probe that requires minimal tissue removal to produce detectable signal. Tissue differentiation within 5–10 s of sampling with the hand-held probe is shown using multivariate statistical methods of the MS profiles. Both workflows were tasked with differentiating necrotic cancer sites from viable cancers using a breast tumour model, and their performance was evaluated. The use of the same tissue surface addresses mismatches in guidance due to intrinsic changes in tissue morphology over consecutive sections.