3D modelling of angiogenesis and vascular tumour growth

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute

Estimating the furrow infiltration characteristic from a single advance point

Management and control of surface irrigation, in particular furrow irrigation, is limited by spatio-temporal soil infiltration variability as well as the high cost and time associated with collecting intensive field data for estimation of the infiltration characteristics. Recent work has proposed scaling the commonly used infiltration function by using a model infiltration curve and a single advance point for every other furrow in an irrigation event. Scaling factors were calculated for a series of furrows at two sites and at four points down the length of the field (0.25 L, 0.5 L, 0.75 L and L). Differences in the value of the scaling factor with distance were found to be a function of the shape of the advance curves. It is concluded that use of points early in the advance results in a substantial loss of accuracy and should be avoided. The scaling factor was also strongly correlated with the furrow-wetted perimeter suggesting that the scaling is an appropriate way of both predicting and accommodating the effect of the hydraulic variability

Country differences in the diagnosis and management of coronary heart disease : a comparison between the US, the UK and Germany

Background The way patients with coronary heart disease (CHD) are treated is partly determined by non-medical factors. There is a solid body of evidence that patient and physician characteristics influence doctors' management decisions. Relatively little is known about the role of structural issues in the decision making process. This study focuses on the question whether doctors' diagnostic and therapeutic decisions are influenced by the health care system in which they take place. This non-medical determinant of medical decision-making was investigated in an international research project in the US, the UK and Germany. Methods Videotaped patients within an experimental study design were used. Experienced actors played the role of patients with symptoms of CHD. Several alternative versions were taped featuring the same script with patients of different sex, age and social status. The videotapes were shown to 384 randomly selected primary care physicians in the three countries under study. The sample was stratified on gender and duration of professional experience. Physicians were asked how they would diagnose and manage the patient after watching the video vignette using a questionnaire with standardised and open-ended questions. Results Results show only small differences in decision making between British and American physicians in essential aspects of care. About 90% of the UK and US doctors identified CHD as one of the possible diagnoses. Further similarities were found in test ordering and lifestyle advice. Some differences between the US and UK were found in the certainty of the diagnoses, prescribed medications and referral behaviour. There are numerous significant differences between Germany and the other two countries. German physicians would ask fewer questions, they would order fewer tests, prescribe fewer medications and give less lifestyle advice. Conclusion Although all physicians in the three countries under study were presented exactly the same patient, some disparities in the diagnostic and patient management decisions were evident. Since other possible influences on doctors treatment decisions are controlled within the experimental design, characteristics of the health care system seem to be a crucial factor within the decision making process

Patient information leaflets (PILs) for UK randomised controlled trials : a feasibility study exploring whether they contain information to support decision making about trial participation

Peer reviewedPublisher PD

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Predicting the safety and efficacy of butter therapy to raise tumour pHe: an integrative modelling study

Background: Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts.\ud \ud Methods: We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies.\ud \ud Results: The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1–7.2.\ud \ud Conclusion: Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1–7.2 is most promising

Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks

Objective: To test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Background: Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction nor generalisation of independently varying, active and passive states. We use deep learning to investigate the generalizable content of 2D US muscle images. Method: US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle were recorded from 32 healthy participants (7 female, ages: 27.5, 19-65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, driftfree, components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous, independent variation of passive (joint angle) and active (electromyography) inputs. Results: For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography, and joint moment were estimated to accuracy 55±8%, 57±11%, and 46±9% respectively. Significance: With 2D US imaging, deep neural networks can encode in generalizable form, the activitylength-tension state relationship of these muscles. Observation only, low power, 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalised muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing

A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma

We proposed a pooling-based radiomics feature selection method and showed how it would be applied to the clinical question of predicting one-year survival in 130 patients treated for glioma by radiotherapy. The method combines filter, wrapper and embedded selection in a comprehensive process to identify useful features and build them into a potentially predictive signature. The results showed that non-invasive CT radiomics were able to moderately predict overall survival and predict WHO tumour grade. This study reveals an associative inter-relationship between WHO tumour grade, CT-based radiomics and survival, that could be clinically relevant