CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity.

Breast cancer (BC) remains the most diagnosed cancer in women, accounting for 12% of new annual cancer cases in Europe and worldwide. Advances in surgery, radiotherapy and systemic treatment have resulted in improved clinical outcomes and increased survival rates in recent years. However, BC therapy-related cardiotoxicity, may severely impact short- and long-term quality of life and survival. This study presents the CARDIOCARE platform and its main components, which by integrating patient-specific data from different categories, data from patient-oriented eHealth applications and wearable devices, and by employing advanced data mining and machine learning approaches, provides the healthcare professionals with a valuable tool for effectively managing BC patients and preventing or alleviating treatment induced cardiotoxicity.Clinical Relevance- Through the adoption of CARDIOCARE platform healthcare professionals are able to stratify patients for their risk for cardiotoxicity and timely apply adequate interventions to prevent its onset.

Facial geometry and speech analysis for depression detection.

Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation.

The impact of spin coupling signal loss on fat content characterization in multi-echo acquisitions with different echo spacing.

PURPOSE: This study aimed to assess the effect of echo spacing in transverse magnetization (T2) signal decay of gel and fat (oil) samples. Additionally, we assess the feasibility of using spin coupling as a determinant of fat content. METHODS: Phantoms of known T2 values, as well as vegetable oil phantoms, were scanned at 1.5T scanner with a multi echo FSE sequence of variable echo spacing above and below the empirical threshold of 20ms for echo train signal modulation (6.7, 13.6, 26.8, and 40ms). T2 values were calculated from monoexponential fitting of the data. Relative signal loss between the four acquisitions of different echo spacing was calculated. RESULTS: Agreement in the T2 values of water gel phantom was observed in all acquisitions as opposed to fat phantom (oil) samples. Relative differences in signal intensity between two successive sequences of different echo spacing on composite fat/water regions of interest was found to be linearly correlated to fat fraction of the ROI. CONCLUSION: The sample specific degree of signal loss that was observed between different fat samples (vegetable oils) can be attributed to the composition of each sample in J coupled fat components. Hence, spin coupling may be used as a determinant of fat content.

Extraction of facial features as indicators of stress and anxiety.

Stress and anxiety heavily affect the human wellbeing and health. Under chronic stress, the human body and mind suffers by constantly mobilizing all of its resources for defense. Such a stress response can also be caused by anxiety. Moreover, excessive worrying and high anxiety can lead to depression and even suicidal thoughts. The typical tools for assessing these psycho-somatic states are questionnaires, but due to their shortcomings, by being subjective and prone to bias, new more robust methods based on facial expression analysis have emerged. Going beyond the typical detection of 6 basic emotions, this study aims to elaborate a set of facial features for the detection of stress and/or anxiety. It employs multiple methods that target each facial region individually. The features are selected and the classification performance is measured based on a dataset consisting 23 subjects. The results showed that with feature sets of 9 and 10 features an overall accuracy of 73% is reached.

Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm.

The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.

A novel method to detect heart beat rate using a mobile phone.

Heart Beat Rate calculation has traditionally been conducted using specialized hardware most commonly in the form of pulse oximeters or Electrocardiogram devices. Even though these methods offer high reliability, they require the users to have special sensor to measure their heart rate. In this paper we propose a system capable of estimating the heart beat rate using just a camera from a commercially available mobile phone. The advantage of this method is that the user does not need specialized hardware and s/he can take a measurement in virtually any place under almost any circumstances. Moreover the measurement provided can be used as a tool for health coaching applications or effective telecare services aimed in enhancing the user's well being.

Weighted filtered backprojection for quantitative fluorescence optical projection tomography.

Reconstructing images from a set of fluorescence optical projection tomography (OPT) projections is a relatively new problem. Several physical aspects of fluorescence OPT necessitate a different treatment of the inverse problem to that required for non-fluorescence tomography. Given a fluorophore within the depth of field of the imaging system, the power received by the optical system, and therefore the CCD detector, is related to the distance of the fluorophore from the objective entrance pupil. Additionally, due to the slight blurring of images of sources positioned off the focal plane, the CCD image of a fluorophore off the focal plane is lower in intensity than the CCD image of an identical fluorophore positioned on the focal plane. The filtered backprojection (FBP) algorithm does not take these effects into account and so cannot be expected to yield truly quantitative results. A full model of image formation is introduced which takes into account the effects of isotropic emission and defocus. The model is used to obtain a weighting function which is used in a variation of the FBP algorithm called weighted filtered backprojection (WFBP). This new algorithm is tested with simulated data and with experimental data from a phantom consisting of fluorescent microspheres embedded in an agarose gel.

Building a European biomedical grid on cancer: the ACGT Integrated Project.

This paper presents the needs and requirements that led to the formation of the ACGT (Advancing Clinico Genomic Trials) integrated project, its vision and methodological approaches of the project. The ultimate objective of the ACGT project is the development of a European biomedical grid for cancer research, based on the principles of open access and open source, enhanced by a set of interoperable tools and services which will facilitate the seamless and secure access to and analysis of multi-level clinico-genomic data, enriched with high-performing knowledge discovery operations and services. By doing so, it is expected that the influence of genetic variation in oncogenesis will be revealed, the molecular classification of cancer and the development of individualised therapies will be promoted, and finally the in-silico tumour growth and therapy response will be realistically and reliably modelled. Its main design decisions and results at its current stage of development are presented.

Improved microarray spot segmentation by combining two information channels.

High-throughput gene expression is an important aspect of modern post-genomic research. Microarray technology is the driving force of this revolution, a technology that allows the simultaneous monitoring of expression for thousands of genes. The need for accurate and reproducible research has driven the development of robust analysis frameworks for maximizing the information content of biological data. In microarray imaging technologies, several non-linearities in the experimental process render the measured expression values prone to variability and often, to poor reproducibility. Accurate segmentation of the true signal is a very important task, not least because a single value per spot needs to be derived for further knowledge discovery analysis. In this paper, we present a fully automatic segmentation method for improving the spot segmentation result. The method doesn't make any assumptions concerning the number of classes present in each image spot, and it isn't driven only by the most intense features, since it takes into account the underlying "hybridization ground truth" derived from both information channels of the spotted arrays. Our method is compared to widely used, state-of-the-art segmentation methods in microarray image analysis in a study of a metabolic disorder in yeast, where replicates of reporters are present. Initial results indicate that our method yields more reproducible log ratio measurements across replicates.

Automatic labelling and BI-RADS characterisation of mammogram densities.

Intelligent management of medical data is an important field of research in clinical information and decision support systems. Such systems are finding increasing use in the management of patients known to have, or suspected of having, breast cancer. Different types of breast-tissue patterns convey semantic information which is reported by the radiologist when reading mammograms. In this paper, a novel method is presented for the automatic labelling and characterisation of mammographic densities. The presented method is first concerned with the identification of the prominent structures in each mammogram. Subsequently, "dense tissue" is labelled in a mammogram data set, and BI-RADS classification is performed based on a 2D pdf that is contracted from a "ground truth" data set as well as a shape analysis framework. The presented method can be used in large-scale epidemiological studies which involve mammographic measurements of tissue-pattern, especially since breast-tissue density has been linked to an increased risk of breast cancer.

Fusion of contrast-enhanced breast MR and mammographic imaging data.

Increasing use is being made of Gd-DTPA contrast-enhanced MRI (CE-MRI) for breast cancer assessment since it provides three-dimensional (3D) functional information via pharmacokinetic interaction between contrast agent and tumour vascularity, and because it is applicable to women of all ages as well as patients with post-operative scarring. CE-MRI is complementary to conventional X-ray mammography, since it is a relatively low-resolution functional counterpart of a comparatively high-resolution 2D structural representation. However, despite the additional information provided by MRI, mammography is still an extremely important diagnostic imaging modality, particularly for several common conditions such as ductal carcinoma in situ (DCIS) where it has been shown that there is a strong correlation between microcalcification clusters and malignancy. Pathological indicators such as calcifications and fine spiculations are not visible in CE-MRI and therefore there is clinical and diagnostic value in fusing the high-resolution structural information available from mammography with the functional data acquired from MRI. This article is a clinical overview of the results of a technique to transform the coordinates of regions of interest (ROIs) from the 2D mammograms to the spatial reference frame of the contrast-enhanced MRI volume. An evaluation of the fusion framework is demonstrated with a series of clinical cases and a total of 14 patient examples.