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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.

The change in longitudinal relaxation rate (R1 ) produced by oxygen has been used as a means of inferring oxygenation levels in magnetic resonance imaging in numerous applications. The relationship between oxygen partial pressure (pO2 ) and R1 is linear and reproducible, and the slope represents the relaxivity of oxygen (r1Ox ) in that material. However, there is considerable variability in the values of r1Ox reported, and they have been shown to vary by field strength and temperature. Therefore, we have compiled 28 reported empirical values of the relaxivity of oxygen as a resource for researchers. Furthermore, we provide an empirical model for estimating the relaxivity of oxygen in water, saline, plasma, and vitreous fluids, accounting for magnetic field strength and temperature. The model agrees well (R2 = 0.93) with the data gathered from the literature for fields ranging from 0.011 to 8.45 T and temperatures of 21-40 °C. This provides a useful resource for researchers seeking to quantify pO2 in simple fluids in their studies, such as water and saline phantoms, or bodily fluids such as vitreous fluids, cerebrospinal fluids, and amniotic fluids.

The imaging technique known as Oxygen-Enhanced MRI is under development as a noninvasive technique for imaging hypoxia in tumours and pulmonary diseases. While promising results have been shown in preclinical experiments, clinical studies have mentioned experiencing difficulties with patient motion, image registration, and the limitations of single-slice images compared to 3D volumes. As clinical studies begin to assess feasibility of using OE-MRI in patients, it is important for researchers to communicate about the practical challenges experienced when using OE-MRI on patients to help the technique advance.We report on our experience with using two types of T1 mapping (MOLLI and VFA) for a recently completed OE-MRI clinical study on oropharyngeal squamous cell carcinoma.We report: (1) the artefacts and practical difficulties encountered in this study; (2) the difference in estimated T1 from each method used - the VFA T1 estimation was higher than the MOLLI estimation by 27% on average; (3) the standard deviation within the tumour ROIs - there was no significant difference in the standard deviation seen within the tumour ROIs from the VFA versus MOLLI; and (4) the OE-MRI response collected from either method. Lastly, we collated the MRI acquisition details from over 45 relevant manuscripts as a convenient reference for researchers planning future studies.We have reported our practical experience from an OE-MRI clinical study, with the aim that sharing this is helpful to researchers planning future studies. In this study, VFA was a more useful technique for using OE-MRI in tumours than MOLLI T1 mapping.

Background:Under normal physiological conditions, the spin–lattice relaxation rate (R1) in blood is influenced by many factors, including hematocrit, field strength, and the paramagnetic effects of deoxyhemoglobin and dissolved oxygen. In addition, techniques such as oxygen-enhanced magnetic resonance imaging (MRI) require high fractions of inspired oxygen to induce hyperoxia, which complicates the R1 signal further. A quantitative model relating total blood oxygen content to R1 could help explain these effects. Purpose:To propose and assess a general model to estimate the R1 of blood, accounting for hematocrit, SO2, PO2, and B0 under both normal physiological and hyperoxic conditions. Study Type:Mathematical modeling. Population:One hundred and twenty-six published values of R1 from phantoms and animal models. Field Strength/Sequence:5–8.45 T. Assessment:We propose a two-compartment nonlinear model to calculate R1 as a function of hematocrit, PO2, and B0. The Akaike Information Criterion (AIC) was used to select the best-performing model with the fewest parameters. A previous model of R1 as a function of hematocrit, SO2, and B0 has been proposed by Hales et al, and our work builds upon this work to make the model applicable under hyperoxic conditions (SO2> 0.99). Models were assessed using the AIC, mean squared error (MSE), coefficient of determination (R2), and Bland–Altman analysis. The effect of volume fraction constants WRBC and Wplasmawas assessed by the SD of resulting R1. The range of the model was determined by the maximum and minimum B0, hematocrit, SO2, and PO2of the literature data points. Statistical Tests:Bland–Altman, AIC, MSE, coefficient of determination (R2), SD. Results:The model estimates agreed well with the literature values of R1 of blood (R2=0.93, MSE=0.0013 s−2), and its performance was consistent across the range of parameters: B0=1.5–8.45 T, SO2=0.40–1, PO2=30–700 mmHg. Data Conclusion:Using the results from this model, we have quantified and explained the contradictory decrease in R1 reported in oxygen-enhanced MRI and oxygen-delivery experiments. Level of Evidence:3 Technical Efficacy:Stage 1

PySyft is an open-source multi-language library enabling secure and private machine learning by wrapping and extending popular deep learning frameworks such as PyTorch in a transparent, lightweight, and user-friendly manner. Its aim is to both help popularize privacy-preserving techniques in machine learning by making them as accessible as possible via Python bindings and common tools familiar to researchers and data scientists, as well as to be extensible such that new Federated Learning (FL), Multi-Party Computation, or Differential Privacy methods can be flexibly and simply implemented and integrated. This chapter will introduce the methods available within the PySyft library and describe their implementations. We will then provide a proof-of-concept demonstration of a FL workflow using an example of how to train a convolutional neural network. Next, we review the use of PySyft in academic literature to date and discuss future use-cases and development plans. Most importantly, we introduce Duet: our tool for easier FL for scientists and data owners.

Recent advancements in laser-based transfer of molecules into the gas phase for subsequent analysis with mass spectrometry have allowed decoupling between vaporization (i.e., desorption) and ionization of analytes. This decoupling has allowed individual optimization of the “desorption” and “ionization” steps, giving birth to the field of ambient laser-based mass spectrometry. In these techniques, analytes are efficiently desorbed and ionized before transfer to internal ion optics of the mass analyzers operating under vacuum. Lasers have been instrumental in ambient mass spectrometry due to their multimodal capabilities of desorption and/or ionization. A survey of common laser-based mass spectrometry technologies for analysis under ambient conditions is provided.

Mass spectrometry imaging with desorption electrospray ionization mass spectrometry (DESI-MS) is used to characterize cancer from ex vivo slices of tissues. The process is time-consuming. The use of tissue smears for DESI-MS analysis has been proposed as it eliminates the time required to snap-freeze and section the tissue. To assess the utility of tissue smears for rapid cancer characterization, principal component analysis (PCA) was performed to evaluate the concordance between DESI-MS profiles of breast cancer from tissue slices and smears prepared on various surfaces. PCA suggested no statistical discrimination between DESI-MS profiles of tissue sections and tissue smears prepared on glass, polytetrafluoroethylene (PTFE), and porous PTFE. However, the abundances of cancer biomarker ions varied between sections and smears, with DESI-MS analysis of tissue sections yielding higher ion abundances of cancer biomarkers compared with smears. Coefficient of variance (CV) analysis suggests DESI-MS profiles from tissue smears are as reproducible as the ones from tissue sections. The limit of detection with smear samples from single pixel analysis is comparable to tissue sections that average the signal from a tissue area of 0.01 mm2. The smears prepared on the PTFE surface possessed a higher degree of homogeneity compared with the smears prepared on the glass surface. This allowed single MS scans (~1 s) from random positions across the surface of the smear to be used in rapid cancer typing with good reproducibility, providing pathologic information for cancer typing at speeds suitable for clinical utility.

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.HighlightsBrain age is estimated using a 3D CNN from 12,802 full T1-weighted images.Regions used to drive predictions are different for linearly and nonlinearly registered data.Linear registrations utilise a greater diversity of biologically meaningful areas.Correlations with IDPs and non-imaging variables are consistent with other publications.Excluding subjects with various health conditions had minimal impact on main correlations.

Targeted and localized mass spectrometry imaging allows faster characterization of cancer compared to conventional methods., While mass spectrometers can detect chemical signatures within milliseconds of data acquisition time, the non-targeted nature of mass spectrometry imaging (MSI) necessitates probing the entire surface of the sample to reveal molecular composition even if the information is only sought from a sample subsection. This leads to long analysis times. Here, we used polarimetry to identify, within a biological tissue, areas of polarimetric heterogeneity indicative of cancer. We were then able to target our MS analysis using polarimetry results to either the cancer region itself or to the cancer margin. A tandem of polarimetry and Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) enables fast (10 fold compared to non-targeted imaging), and accurate pathology assessment (cancer typification in less than 2 minutes compared to 30 minutes for histopathology) of ex vivo tissue slices, without additional sample preparation. This workflow reduces the overall analysis time of MSI as a research tool.

Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]-, m/z 835.53 [PI(34:1) - H]- and m/z 863.56 [PI(18:1)(18:0) - H]- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.

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.

Identification of necrosis in tumors is of prognostic value in treatment planning, as necrosis is associated with aggressive forms of cancer and unfavourable outcomes. To facilitate rapid detection of necrosis with Mass Spectrometry (MS), we report the lipid MS profile of necrotic breast cancer with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging validated with statistical analysis and correlating pathology. This MS profile is characterized by (1) the presence of the ion of m/z 572.48 [Cer(d34:1) + Cl]− which is a ceramide absent from the viable cancer subregions; (2) the absence of the ion of m/z 391.25 which is present in small abundance only in viable cancer subregions; and (3) a slight increase in the relative intensity of known breast cancer biomarker ions of m/z 281.25 [FA(18:1)-H]− and 303.23 [FA(20:4)-H]−. Necrosis is accompanied by alterations in the tissue optical depolarization rate, allowing tissue polarimetry to guide DESI-MS analysis for rapid MS profiling or targeted MS imaging. This workflow, in combination with the MS profile of necrosis, may permit rapid characterization of necrotic tumors from tissue slices. Further, necrosis-specific biomarker ions are detected in seconds with single MS scans of necrotic tumor tissue smears, which further accelerates the identification workflow by avoiding tissue sectioning and slide preparation.

Chronic obstructive pulmonary disease (COPD), is a major contributor to hospitalization and healthcare costs in North America. While the hallmark of COPD is airflow limitation, it is also associated with abnormalities of the cardiovascular system. Enlargement of the pulmonary artery (PA) is a morphological marker of pulmonary hypertension, and was previously shown to predict acute exacerbations using a one-dimensional diameter measurement of the main PA. We hypothesized that a three-dimensional (3D) quantification of PA size would be more sensitive than 1D methods and encompass morphological changes along the entire central pulmonary artery. Hence, we developed a 3D measurement of the main (MPA), left (LPA) and right (RPA) pulmonary arteries as well as total PA volume (TPAV) from thoracic CT images. This approach incorporates segmentation of pulmonary vessels in cross-section for the MPA, LPA and RPA to provide an estimate of their volumes. Three observers performed five repeated measurements for 15 ex-smokers with ≥10 pack-years, and randomly identified from a larger dataset of 199 patients. There was a strong agreement (r 2 =0.76) for PA volume and PA diameter measurements, which was used as a gold standard. Observer measurements were strongly correlated and coefficients of variation for observer 1 (MPA:2%, LPA:3%, RPA:2%, TPA:2%) were not significantly different from observer 2 and 3 results. In conclusion, we generated manual 3D pulmonary artery volume measurements from thoracic CT images that can be performed with high reproducibility. Future work will involve automation for implementation in clinical workflows.

Objectives: To develop and evaluate an automated registration and segmentation pipeline for regional lobar pulmonary structure-function measurements, using volume-matched thoracic CT and MRI in order to guide therapy. Methods: Ten subjects underwent pulmonary function tests and volume-matched 1H and 3He MRI and thoracic CT during a single 2-hr visit. CT was registered to 1H MRI using an affine method that incorporated block-matching and this was followed by a deformable step using free-form deformation. The resultant deformation field was used to deform the associated CT lobe mask that was generated using commercial software. 3He-1H image registration used the same two-step registration method and 3He ventilation was segmented using hierarchical k-means clustering. Whole lung and lobar 3He ventilation and ventilation defect percent (VDP) were generated by mapping ventilation defects to CT-defined whole lung and lobe volumes. Target CT-3He registration accuracy was evaluated using region- , surface distance- and volume-based metrics. Automated whole lung and lobar VDP was compared with semi-automated and manual results using paired t-tests. Results: The proposed pipeline yielded regional spatial agreement of 88.0±0.9% and surface distance error of 3.9±0.5 mm. Automated and manual whole lung and lobar ventilation and VDP were not significantly different and they were significantly correlated (r = 0.77, p < 0.0001). Conclusion: The proposed automated pipeline can be used to generate regional pulmonary structural-functional maps with high accuracy and robustness, providing an important tool for image-guided pulmonary interventions.

Necrosis is a form of cell death that is often associated with highly aggressive forms of cancer, is of prognostic value in treatment planning. Mass Spectrometry (MS) is a highly sensitive analytic platform capable of providing a molecular profile of cancer on the basis of mass to charge (m/z) ratio of tissue constituent molecules. MS analysis of ex vivo tissue slices from metastatic murine xenograft tumors from LM2-4 cell line with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) allowed direct comparisons with histology images to determine the molecular profile of necrotic tissues. The necrotic tissue is characterized by the presence of a ceramide absent from the viable cancer regions. The spatial distribution of this ion fully correlated to necrotic areas from pathology in additional independent tumor samples examined. The same ion was detected from in situ necrotic tissue using tissue aerosols generated by hand-held ablation probes coupled to evaporative ionization interface in only a few seconds of sampling. These developments further establish MS as a novel tool for rapid pathology that is highly complementary to current histology based methods widely used in characterization of cancer in both imaging mode (to provide spatial information on cancer border) and profiling mode (to provide information on cancer type and subtype); all based on unique molecular profile associated with each cancer type and subtype. Current efforts in creating cancer molecular profile libraries will facilitate translation. Citation Format: Arash Zarrine-Afsar, Bindesh Shrestha, Alessandra Tata, Michael Woolman, Manuela Ventura, Nicholas Bernards, Milan Ganguly, Howard Ginsberg, Jinzi Zheng, Emma Bluemke. Rapid detection of necrosis in breast cancer with ex vivo and in situ mass spectrometry analysis methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 741. doi:10.1158/1538-7445.AM2017-741

Correction for ‘Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration’ by Michael Woolman et al., Analyst, 2017, 142, 3250–3260.