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BACKGROUND AND PURPOSE: Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system.MATERIALS AND METHODS: MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T 1) was measured alongside the change in 1/T 1 (termed ΔR 1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems. RESULTS: Baseline T 1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR 1 significantly increased (pCONCLUSION: We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy.

We review the exciting potential (and challenges) of quantitative nailfold capillaroscopy, focusing on its role in systemic sclerosis. Quantifying abnormality, including automated analysis of nailfold images, overcomes the subjectivity of qualitative/descriptive image interpretation. First we consider the rationale for quantitative analysis, including the potential for precise discrimination between normal and abnormal capillaries and for reliable measurement of disease progression and treatment response. We discuss nailfold image acquisition and interpretation, and describe how early work on semi-quantitative and quantitative analysis paved the way for semi-automated and automated analysis. Measurement of red blood cell velocity is described briefly. Finally we give a personal view on ‘next steps’. From a clinical perspective, increased uptake of nailfold capillaroscopy by general rheumatologists could be achieved via low-cost hand-held devices with cloud-based automated analysis. From a research perspective, automated analysis could facilitate large-scale prospective studies using capillaroscopic parameters as possible biomarkers of systemic sclerosis-spectrum disorders.

Background/Aims Nailfold capillaroscopy is a key investigation in differentiating between primary and systemic sclerosis (SSc)-related Raynaud’s phenomenon (RP). However, interpreting capillaroscopy images (i.e. distinguishing ‘normal’ capillaries of primary RP [PRP] from abnormal capillaries of SSc) can be challenging. Our objectives were: (1) to develop a complete deep learning-based system to identify and classify capillary apices, and compute useful parameters such as width, shape score and vessel density; and (2) use the measured parameters to train and test a logistic regressor to distinguish between subjects with SSc and those with ‘normal’ capillary appearance. Methods 911 mosaic images were included for image-level feature extraction. 456 mosaic images (104 healthy control [HC], 83 PRP, 269 SSc) were used for training and the remaining 455 mosaic images (104 HC, 83 PRP and 268 SSc) were used for validation and testing the deep learning model. For subject-level classification 133 subjects (105 [79%] female, median age [range] 57 [19-80]) were used; 87 (25 HC, 14 PRP, and 48 SSc) were used for training and the remaining 46 (12 HC, 3 PRP, 31 SSc) for testing the logistic regressor. Images from patients with PRP and healthy subjects were grouped for all cases. The deep learning system uses a UNet architecture for capillary detection and vessel segmentation (density and shape score estimation). A ResNet-34 framework is used for capillary classification (distal vs. non-distal) and computing vessel widths. Area under Receiver Operating Characteristic (ROC) curves (AUC) measures the classification accuracy when assigning individuals to SSc and HC/PRP groups. Repeatability experiments were performed with different random seeds to validate the reproducibility of the results. Results For apex detection, the sensitivity and average false positive count from the Free-response Receiver Operating Characteristic (FROC) curve were 0.91 and 127.6, respectively. Apex classification accuracy (distal apices) was 93.8%, with an AUC of 0.92 and sensitivity of 0.9603. Overall AUC for subject level classification (HC/PRP vs SSc) for the current approach was 0.9739 ± 0.0207. Conclusion We have developed a completely automated nailfold capillary detection and analysis system based on deep learning. Apex detection, distal classification, and subsequent computation of distal apex measurements from nailfold capillaroscopy images are all performed with no human input. We have combined image-level features and have built a subject-level classifier model which successfully discriminates between SSc and HC/PRP with an improved AUC (i.e. improved sensitivity and specificity) compared to our previous work. We have performed the repeatability experiments with different random seeds and the performance of system is consistent with all the seeds. Disclosure P. Gurunath Bharathi: None. M. Berks: None. A. Herrick: None. G. Dinsdale: None. A. Murray: None. J. Manning: None. S. Wilkinson: None. C. Taylor: None.

Purpose: We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI). Methods: Our framework includes a hierarchy of nested models, from which physiological parameters are derived in 2 regimes, corresponding to the active transport and free diffusion of gadoxetate. We use simulations to show the sensitivity of model selection and parameter estimation to temporal resolution, time-series duration, and noise. We apply the framework in 8 healthy volunteers (time-series duration up to 24 minutes) and 10 patients with hepatocellular carcinoma (6 minutes). Results: The active transport regime is preferred in 98.6% of voxels in volunteers, 82.1% of patients’ non-tumorous liver, and 32.2% of tumor voxels. Interpatient variations correspond to known co-morbidities. Simulations suggest both datasets have sufficient temporal resolution and signal-to-noise ratio, while patient data would be improved by using a time-series duration of at least 12 minutes. Conclusions: In patient data, gadoxetate exhibits different kinetics: (a) between liver and tumor regions and (b) within regions due to liver disease and/or tumor heterogeneity. Our generalized framework selects a physiological interpretation at each voxel, without preselecting a model for each region or duplicating time-consuming optimizations for models with identical functional forms.

The autoimmune disease systemic sclerosis (SSc) causes microvascular changes that can be easily observed cutaneously at the finger nailfold. Optoacoustic imaging (OAI), a combination of optical and ultrasound imaging, specifically raster-scanning optoacoustic mesoscopy (RSOM), offers a non-invasive high-resolution 3D visualization of capillaries allowing for a better view of microvascular changes and an extraction of volumetric measures. In this study, nailfold capillaries of patients with SSc and healthy controls are imaged and compared with each other for the first time using OAI. The nailfolds of 23 patients with SSc and 19 controls were imaged using RSOM. The acquired images were qualitatively compared to images from state-of-the-art imaging tools for SSc, dermoscopy and high magnification capillaroscopy. The vascular volume in the nailfold capillaries were computed from the RSOM images. The vascular volumes differ significantly between both cohorts (0.216 ± 0.085 mm3 and 0.337 ± 0.110 mm3; p

Objectives Universal serial bus (USB) microscopy (capillaroscopy) could provide all rheumatologists with an easy-to-use, low-cost tool to examine the nailfold capillaries to facilitate early diagnosis of SSc. The objectives of this pilot study were to examine the feasibility of acquiring and analysing images using USB microscopy and to compare results to videocapillaroscopy. Methods Videocapillaroscopy and USB microscope images were obtained from the right and left ring fingers of 20 patients with SSc and 20 healthy control subjects. In addition to generating panoramic capillary mosaics from across the whole nailbed, custom software made fully automated measurements of vessel structure including capillary width and density. The area under the receiver operating characteristic curve (AZ) was used to measure separation between the SSc and healthy control groups. Results High quality images could be generated from the USB microscope, with reconstructed USB images comparing very favourably with those obtained using videocapillaroscopy. Using USB microscope images, the receiver operating characteristic curve AZ for group separation based on mean width was 0.81 (standard error 0.120) compared with 0.81 (standard error 0.095) for the (gold standard) videocapillaroscopy. The receiver operating characteristic curve AZ for group separation using capillary density was 0.48 (standard error 0.16) for USB microscope images, compared with 0.70 (standard error 0.10) for videocapillaroscopy. Conclusion In this pilot study, USB capillaroscopy was able to discriminate between patients with SSc and controls as well as videocapillaroscopy on the basis of capillary width. This finding, together with the high-quality images obtained, highlights the potential of USB capillaroscopy as a low-cost, easily accessible clinical and research tool.

Background Nailfold capillaroscopy offers a non-invasive route to observation of systemic sclerosis (SSc)-related microvascular changes and is used routinely for inspection of the capillaries at the finger nailfold. The characteristic changes in capillary structure (increased capillary width, decreased capillary density and abnormal angiogenesis) are included in the 2013 classification criteria for SSc. Optoacoustic mesoscopy is a combination of optical and ultrasound imaging enabling a 3D perspective of capillaries at a similar resolution to commercial nailfold capillaroscopy systems. We have previously reported that quantitative measures of vascular volume and density extracted from optoacoustic images differ significantly between patients with SSc and healthy controls. The aim of this study was to determine whether an artificial neural network (deep machine learning) could correctly differentiate between images from patients with SSc and healthy controls. Methods Optoacoustic (3D, iThera, Germany) and ‘standard’ capillaroscopy images (2D, Optillia, Sweden) of the right and left ring finger nailfolds were acquired. Images were taken at the centre of the nailfold. Acquisition of the same capillaries was difficult in some cases. 2D, greyscale, maximum intensity projections were created from the 3D optoacoustic images. Capillaroscopy images were downsized to match the optoacoustic image resolution. For data augmentation purposes each image, from both the optoacoustic and capillaroscopy data sets, was sliced into multiple overlapping image sections of fixed size. Transfer learning was used to train the model on 'disease' classification (SSc vs control). The pre-trained neural networks learn general image features and subsequently, are fine-tuned on the image data to classify based on the previously learned features. Results Twenty four patients with SSc (19% female; median age 65 IQR [57-69]; duration of Raynaud’s phenomenon 18 [12-28] years; time since onset of first non-Raynaud’s feature 11 (5-18) years) and 19 controls (17% female; age 15 [39-55] years) took part in the study. Fifty random data splits were used to validate the model and showed an average classification accuracy of 0.81 ± 0.15, with an area under the ROC curve of 0.88 ± 0.13 for optoacoustic data. The classification specificity and sensitivity were 0.84 ± 0.22 and 0.77 ± 0.21 respectively for optoacoustic mesoscopy. Performing the same task on capillaroscopy images, achieved an average accuracy of 0.86 ± 0.12 (AUC: 0.92 ± 0.09). Conclusion Deep learning is able to achieve excellent differentiation between images from patients with SSc and controls for both optoacoustic and standard capillaroscopy. Limitations of the study include the relatively small participant numbers and direct comparison of the same capillaries not always being possible. Optoacoustic mesoscopy offers huge potential to increase our understanding of the microvasculature in SSc. Disclosures S. Nitkunanantharajah None. K. Haedicke Corporate appointments; KH is an employee of iThera. T.L. Moore None. J.B. Manning None. G. Dinsdale None. M. Berks None. D. Jüstel None. V. Ntziachristos None. C. Taylor None. M. Dickinson None. A. Herrick None. A.K. Murray None.

Immunotherapy has potential to improve outcome for cancer patients. The MRI biomarker apparent diffusion co-efficient (ADC) can map response to radiotherapy (RT) through its sensitivity to changes in tumor fluid and cellularity. The role of ADC in evaluating immunotherapy agents is unknown, despite investigators using ADC change as an exploratory endpoint in early phase clinical trials. Our study sought to evaluate and validate ADC changes induced by both RT and immunotherapy agents in syngeneic mouse tumor models. Three experiments were performed in BALB/c mice bearing CT26 colorectal cancers: (1) single 10Gy fraction RT versus control (sham); (2) TLR 7/8 agonist R848 versus control (saline); (3) anti-PD-L1 antibody versus control (saline). We acquired MRI data at 7T Bruker system at days 0, 3, 7, +/- 10 after therapy start, with tumors measuring between 250-300 mm3 at day 0. Median ADC and the inter-quartile range (IQR; a measure of tumor heterogeneity) were derived. Three further equivalent experiments were performed in BALB/c mice bearing triple negative 4T1 breast tumors. All CT26 and 4T1 tumors were bisected at cull for immunohistochemistry and FACS analysis. For CT26 model, RT-induced tumor growth inhibition (p In conclusion, our data are the first to evaluate ADC changes induced by RT and immunotherapy agents in syngeneic mouse models of cancer. RT, R848 and anti-PD-L1 induced different ADC responses, each with varied relationships to immune cells. This highlights the need for extensive validation before diffusion weighted MRI biomarkers can be used in clinical trials to monitor response to immunotherapies alone or in combination with RT. Citation Format: Grazyna Lipowska-Bhalla, Damien J. McHugh, Muhammad Babur, Isabel Peset Martin, Michael Berks, Ross A. Little, Susan Cheung, Yvonne Watson, Denis G. Alferez, Kaye J. Williams, Jamie Honeychurch, James P. O'Connor. Diffusion weighted MRI evaluation of response to immunotherapy and radiotherapy in CT26 and 4T1 syngeneic mouse models of cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2781.

Background : Mammographic density is an important risk factor for breast cancer. Recent research demonstrated that percentage density assessed visually using Visual Analogue Scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognise relevant image features not yet captured by automated methods. Method : We have built convolutional neural networks (CNN) to predict VAS scores from full-field digital mammograms. The CNNs are trained using whole-image mammograms, each labelled with the average VAS score of two independent readers. They learn a mapping between mammographic appearance and VAS score so that at test time, they can predict VAS score for an unseen image. Networks were trained using 67520 mammographic images from 16968 women, and tested on a large dataset of 73128 images and case-control sets of contralateral mammograms of screen detected cancers and prior images of women with cancers detected subsequently, matched to controls on age, menopausal status, parity, HRT and BMI. Results : Pearson's correlation coefficient between readers' and predicted VAS in the large dataset was 0.79 per mammogram and 0.83 per woman (averaging over all views). In the case-control sets, odds ratios of cancer in the highest vs lowest quintile of percentage density were 3.07 (95%CI: 1.97 - 4.77) for the screen detected cancers and 3.52 (2.22 - 5.58) for the priors, with matched concordance indices of 0.59 (0.55 - 0.64) and 0.61 (0.58 - 0.65) respectively. Conclusion : Our fully automated method demonstrated encouraging results which compare well with existing methods, including VAS.

Objectives: Nailfold capillaroscopy is being increasingly used by rheumatologists in the diagnosis of SSc. However, assessment of all nailfolds can be time-consuming in a busy outpatient clinic. Our aim was to answer the question as to how many (and which) fingers a clinician should routinely assess to capture accurately the true state.Methods: A total of 2994 assessments (by an international panel of expert observers) of 1600 images from 173 participants (101 with SSc, 22 with primary RP and 50 healthy controls) were included in this analysis. Seven single-finger or finger combinations (derived from the middle and ring fingers) were then tested for sensitivity for the presence of two markers of capillary abnormality [presence of giant capillaries and an SSc grade (early, active or late)] compared with assessment of all eight fingers.Results: For the eight-finger gold standard, sensitivity against the diagnostic criteria was 74.6% (53.0% for the presence of giants alone and 73.1% for image grade alone). Examining only one finger gave low sensitivity (ranging from right middle 31.7% to left ring 46.6%). Examining both ring fingers gave a sensitivity of 59.8%, whereas examining the four-finger combination of both ring and both middle fingers gave a sensitivity of 66.7%.Conclusion: During routine capillaroscopic examination, ideally all eight nailbeds (excluding thumbs) should be examined, otherwise some abnormalities will be missed. Examining only four fingers reduces capillaroscopy sensitivity.

OBJECTIVES: Our aim was to assess the reliability of nailfold capillary assessment in terms of image evaluability, image severity grade ('normal', 'early', 'active', 'late'), capillary density, capillary (apex) width, and presence of giant capillaries, and also to gain further insight into differences in these parameters between patients with systemic sclerosis (SSc), patients with primary Raynaud's phenomenon (PRP) and healthy control subjects.METHODS: Videocapillaroscopy images (magnification 300×) were acquired from all 10 digits from 173 participants: 101 patients with SSc, 22 with PRP and 50 healthy controls. Ten capillaroscopy experts from 7 European centres evaluated the images. Custom image mark-up software allowed extraction of the following outcome measures: overall grade ('normal', 'early', 'active', 'late', 'non-specific', or 'ungradeable'), capillary density (vessels/mm), mean vessel apical width, and presence of giant capillaries.RESULTS: Observers analysed a median of 129 images each. Evaluability (i.e. the availability of measures) varied across outcome measures (e.g. 73.0% for density and 46.2% for overall grade in patients with SSc). Intra-observer reliability for evaluability was consistently higher than inter- (e.g. for density, intra-class correlation coefficient [ICC] was 0.71 within and 0.14 between observers). Conditional on evaluability, both intra- and inter-observer reliability were high for grade (ICC 0.93 and 0.78 respectively), density (0.91 and 0.64) and width (0.91 and 0.85).CONCLUSIONS: Evaluability is one of the major challenges in assessing nailfold capillaries. However, when images are evaluable, the high intra- and inter-reliabilities suggest that overall image grade, capillary density and apex width have potential as outcome measures in longitudinal studies.

Background Nailfold capillaroscopic parameters hold increasing promise as outcome measures for clinical trials in systemic sclerosis (SSc). Their inclusion as outcomes would often naturally require capillaroscopy images to be captured at several time points during any one study. Our objective was to assess repeatability of image acquisition (which has been little studied), as well as of measurement. Method 41 patients (26 with SSc, 15 with primary Raynaud's phenomenon) and 10 healthy controls returned for repeat high-magnification (300 ×) videocapillaroscopy mosaic imaging of 10 digits one week after initial imaging (as part of a larger study of reliability). Images were assessed in a random order by an expert blinded observer and 4 outcome measures extracted: (1) overall image grade and then (where possible) distal vessel locations were marked, allowing (2) vessel density (across the whole nailfold) to be calculated (3) apex width measurement and (4) giant vessel count. Intra-rater, intra-visit and intra-rater inter-visit (baseline vs. 1 week) reliability were examined in 475 and 392 images respectively. A linear, mixed-effects model was used to estimate variance components, from which intra-class correlation coefficients (ICCs) were determined. Results Intra-visit and inter-visit reliability estimates (ICCs) were (respectively): overall image grade, 0.97 and 0.90; vessel density, 0.92 and 0.65; mean vessel width, 0.91 and 0.79; presence of giant capillary, 0.68 and 0.56. These estimates were conditional on each parameter being measurable. Conclusion Within-operator image analysis and acquisition are reproducible. Quantitative nailfold capillaroscopy, at least with a single observer, provides reliable outcome measures for clinical studies including randomised controlled trials.

Nailfold capillaroscopy (NC) allows non-invasive imaging of systemic sclerosis (SSc) related microvascular disease. We have developed a state-of-the-art NC system that enables fast, panoramic imaging of the whole nailfold at high-magnification, and incorporates novel software to make fully automated estimates of capillary structure and blood flow velocity. We present the first results of a study in which 50 patients with SSc, 12 with primary Raynauds phenomenon (PRP) and 50 healthy controls (HC) were imaged using the new system, and show that a combined model of capillary measurements strongly separates SSc from HC/PRP (ROC \(A_z\)=0.93). Including capillary flow improves model performance, suggesting flow provides complementary information to capillary structure for diagnosing SSc.

Nailfold capillaroscopy is an established qualitative technique in the assessment of patients displaying Raynaud’s phenomenon. We describe a fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach. On an unseen set of 455 images, the system detects and locates individual capillaries as well as human experts, and makes measurements of vessel morphology that reveal statistically significant differences between patients with (relatively benign) primary Raynaud’s phenomenon, and those with potentially life-threatening systemic sclerosis.

The effects of systemic sclerosis (SSc) - a disease of the connective tissue causing blood flow problems that can require amputation of the fingers - can be observed indirectly by imaging the capillaries at the nailfold, though taking quantitative measures such as blood flow to diagnose the disease and monitor its progression is not easy. Optical flow algorithms may be applied, though without ground truth (i.e. known blood flow) it is hard to evaluate their accuracy. We propose an image model that generates realistic capillaroscopy videos with known flow, and use this model to quantify the effect of flow rate, cell density and contrast (among others) on estimated flow. This resource will help researchers to design systems that are robust under real-world conditions.

Increased mammographic density is a well-established risk factor for breast cancer; much of the evidence is based on semi-automated or visual assessment of analogue mammograms, although volumetric measures from digitally acquired mammograms are now being reported. It is also possible to quantify volumes of fat and gland from digitised analogue mammograms, given suitable calibration data. We have measured breast density in cancer cases and controls which had previous analogue screening mammograms in the PROCAS (Predicting Risk Of Cancer At Screening) study.