Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period.

OBJECTIVE: We investigated the utility of geometric features for future AAA growth prediction. BACKGROUND: Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. METHODS: Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients. RESULTS: There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases. CONCLUSIONS: Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

BACKGROUND: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications. OBJECTIVES: In this study, we investigate if the raw data acquired from a noncontrast CT image contains sufficient information to differentiate blood and other soft tissue components. A deep learning pipeline underpinned by generative adversarial networks was developed to simulate contrast enhanced CTA images using noncontrast CTs. METHODS AND RESULTS: Two generative models (cycle- and conditional) are trained with paired noncontrast and contrast enhanced CTs from seventy-five patients (total of 11,243 pairs of images) with abdominal aortic aneurysms in a 3-fold cross-validation approach with a training/testing split of 50:25 patients. Subsequently, models were evaluated on an independent validation cohort of 200 patients (total of 29,468 pairs of images). Both deep learning generative models are able to perform this image transformation task with the Cycle-generative adversarial network (GAN) model outperforming the Conditional-GAN model as measured by aneurysm lumen segmentation accuracy (Cycle-GAN: 86.1% ± 12.2% vs Con-GAN: 85.7% ± 10.4%) and thrombus spatial morphology classification accuracy (Cycle-GAN: 93.5% vs Con-GAN: 85.7%). CONCLUSION: This pipeline implements deep learning methods to generate CTAs from noncontrast images, without the need of contrast injection, that bear strong concordance to the ground truth and enable the assessment ofimportant clinical metrics. Our pipeline is poised to disrupt clinical pathways requiring intravenous contrast.

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

BACKGROUND: Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in >90% of cases. These deformations prevent the automatic extraction of vital clinical information by existing image reconstruction methods. AIM: In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood vessels in CT images acquired with or without the use of a contrast agent. METHODS AND RESULTS: Seventy-Five patients with paired noncontrast and contrast-enhanced CT images were randomly selected from an ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation. Data augmentation was implemented to diversify the training data set in a ratio of 10:1. The performance of our Attention-based U-Net in extracting both the inner (blood flow) lumen and the wall structure of the aortic aneurysm from CT angiograms was compared against a generic 3-D U-Net and displayed superior results. Implementation of this network within the aortic segmentation pipeline for both contrast and noncontrast CT images has allowed for accurate and efficient extraction of the morphological and pathological features of the entire aortic volume. CONCLUSIONS: This extraction method can be used to standardize aneurysmal disease management and sets the foundation for complex geometric and morphological analysis. Furthermore, this pipeline can be extended to other vascular pathologies.

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

The Impact of Early COVID-19 Pandemic on the Presentation and Management of Urinary Calculi Across the Globe: A Systematic Review.

Introduction: It is acknowledged that the COVID-19 pandemic has had a major impact on health care services around the globe with possible worse outcomes. It has resulted in stretch of resources with canceled or delayed procedures. Patients with urinary calculi have also suffered the negative impact. This systematic review aims to assess the impact of the early COVID-19 pandemic on the presentation and management of urinary calculi around the globe. Methods: We reviewed the impact of early COVID-19 on the clinically important aspects of stone disease using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We searched Medline, Embase, and Central databases using themes of COVID-19 OR Sars-Cov-2 OR pandemic OR coronavirus AND kidney stone, urinary calculi, urolithiasis, and similar allied terms. Inclusion criteria were studies with data on both pre- and COVID-19 period covering one or more of eight clinical domains. Results: Our search returned 231 studies, after removal of duplicates, of which 18 studies were included for analysis. The number of patients presenting to hospital declined by 21%-70% at the beginning of the pandemic, whereas majority of studies reported increased associated complications. There are mixed reports in terms of delay to presentation and use of conservative management. There was a consistent trend toward reduction in elective procedures with wide variations (shockwave lithotripsy 38%-98%, percutaneous nephrolithotomy 94%-100%, and ureteroscopy 8%-98%). There was a trend toward increased nephrostomy insertion with the onset of the pandemic. Conclusion: This review demonstrated the differences in the number of patients presenting to hospital, complication rates, and management of urinary calculi, including surgical interventions, with the onset of the COVID-19 pandemic. It offers baseline global information that would help understand the impact of early pandemic, variations in practices, and be useful for future comparisons.

The relationship between cardiac dosimetry and tumour quadrant location in left sided whole breast and chest wall adjuvant radiotherapy.

BACKGROUND: Radiotherapy after breast surgery decreases locoregional recurrence and improves survival. This is not without risks from radiation exposure and could have implications in clinical practice. Our study investigates the correlation between tumour location and radiation dose to the heart. METHODS: Left-sided breast cancer patients who had radiotherapy at Aberdeen Royal Infirmary in 2010 were identified. Tumour location was established from notes and imaging. Radiotherapy planning scans were reviewed, and cardiac doses calculated. The mean cardiac dose, maximum dose and volume of the heart in the field, along with V5-V40, were determined. RESULTS: 40 patients had mastectomies and 118 breast conserving surgery. The median percentage of the heart in the field and the Interquartile Range was 0.59% (0.03-1.74) for all patients, with the highest for lower inner quadrant (LIQ) tumours 1.20% (0.29-2.40), followed by mastectomy 0.94% (0.02-1.82). The mean heart dose showed a higher median for mastectomies 1.59 Gy (1.00-1.94), followed by LIQ tumours 1.58 Gy (1.31-2.28), with an overall median of 1.42 Gy (1.13-1.95). The median percentage of the heart in the field, the mean cardiac dose and V5-V30 did not reach statistical significance, however, V40 and the maximum dose did. CONCLUSIONS: The benefits of radiotherapy after breast cancer surgery are established, but with potential harm from cardiac exposure. Our cohort showed higher radiation exposure to the heart in patients with LIQ tumours and mastectomies but reached significance only for V40 and maximum dose. This highlights tumour location as a potentially important risk factor for cardiac exposure with breast radiotherapy.

Acute Severe Hyponatremia Following Use of "Detox Tea".

A previously healthy 51-year-old woman presented to the ED with a one-week history of malaise, myalgia, unsteadiness, and an intermittent mild headache. Physical examination revealed neurological signs including a broad-based gait and bilateral low amplitude tremor. Laboratory testing of electrolytes revealed acute severe hyponatremia (115 mmol/L). Further, history-taking revealed increased urinary frequency following regular consumption of an over-the-counter detoxification tea product. The patient made a good recovery following admission to the intensive care unit. We identify similar cases in the literature and explore potential causal mechanisms. This case highlights the importance of enquiring about the use of supplementary health products when taking a history, and specifically identifies a growing number of reports of acute severe hyponatremia following the use of "detox teas".

Non-Traumatic Carotid Artery Dissection Following an Episode of Orbital Cellulitis: A Case Report.

Carotid artery dissection (CAD) is a haemorrhage into the arterial wall disrupting the intimal layers of the vessel. We present a case of a 16-year-old male with a non-traumatic spontaneous CAD. The patient presented with Horner's syndrome following an episode of orbital cellulitis secondary to sinusitis requiring sinus drainage surgery. Subsequent magnetic resonance imaging (MRI) revealed a CAD. The patient was treated with antiplatelet medication.

Frank Netter: A Man of Art and Science.

Medical illustrations are highly used by medical professionals for various reasons. They have facilitated learning for many centuries and allowed a detailed analysis of subjects. Complex information on anatomy, histology, physiology, and even surgical procedures can be transformed by illustrations into a clear and accurate source that words could never fully describe. The idea that art can be used to enhance the study of science has been emphasized by many medical illustrators. One person who will always be remembered for his artwork in the modern era is Frank H. Netter. The New York native, born in 1906, is one of the era's most well-known medical illustrators. Almost all medical professionals, at one point or another, have come across one of his numerous artistic sketches during their career. Although best known for his Atlas of Human Anatomy, some of Netter's other projects include the CIBA Collection of Medical Illustrations, the "Transparent Women," and the Clinical Symposia series. Medicine lost an educator and an artist in 1991; however, his collection of illustrations remains as his lasting gift to the field.