3D-printed iodine-ink CT phantom for radiomics feature extraction - advantages and challenges.

BACKGROUND: To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. PURPOSE: To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. METHODS: Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3 . This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. RESULTS: Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2 ). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g., for ω = 0.3942 mm-1 , |f(ω)|original  = 0.09 * |fmax |original and |f(ω)|phantom  = 0.12 * |fmax |phantom ). CONCLUSIONS: 3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses.

Breast cancer survival analysis agents for clinical decision support.

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.

Development and Piloting of a Community-Partnered Heart Failure Training Course for Home Health Care Workers.

BACKGROUND: Despite their unique contributions to heart failure (HF) care, home healthcare workers (HHWs) have unmet educational needs and many lack HF caregiving self-efficacy. To address this, we used a community-partnered approach to develop and pilot a HF training course for HHWs. METHODS: We partnered with the Training and Employment Fund, a benefit fund of the largest healthcare union in the United States, to develop a 2-hour virtual HF training course that met HHWs' job-specific needs. English and Spanish-speaking HHWs interested in HF training, with access to Zoom, were eligible. We used a mixed methods design with pre/postsurveys and semi-structured interviews to evaluate the course: (a) feasibility, (b) acceptability, and (c) effectiveness (change in knowledge [Dutch Heart Failure Knowledge Scale range 0-15] and caregiving self-efficacy [HF Caregiver Self-efficacy Scale range 0-100]). RESULTS: Of the 210 HHWs approached, 100 were eligible and agreed, and 70 enrolled. Of them, 53 (employed by 15 different home care agencies) participated. Posttraining data showed significant improvements (pretraining mean [SD] versus posttraining mean [SD]; P value) in HF knowledge (11.21 [1.90] versus 12.21 [1.85]; P=0.0000) and HF caregiving self-efficacy (75.21 [16.57] versus 82.29 [16.49]; P=0.0017); the greatest gains occurred among those with the lowest pre-training scores. Participants found the course engaging, technically feasible, and highly relevant to their scope of care. CONCLUSIONS: We developed and piloted the first HF training course for HHWs, which was feasible, acceptable, and improved their HF knowledge and caregiving self-efficacy. Our findings warrant scalability to the workforce at large with a train-the-trainer model.

Effect of fique fibers and its processing by-products on morphology, thermal and mechanical properties of epoxy based biocomposites.

This work examines the morphology, mechanical and thermal properties of biocomposites based on epoxy resin-EP and fique (Furcraea andina), a native crop of South America. The EP-fique biocomposites were prepared using fique powder-FP an industrial waste generated during fique processing, nonwoven fique fiber mats-NWF and unidirectional fique fiber mats-UF oriented at 0° and 90°. The addition of fique into EP matrix restricts EP macromolecule chains movement and enhance the thermal stability of EP. SEM images showed that fique form used (powder or fiber) and mat arrangement can generate changes in the biocomposites morphology. Mechanical characterization show that fique powder and fique fibers oriented at 90° acts as fillers for the epoxy matrix while the fique fibers oriented at 0° reinforce EP matrix increasing the tensile and flexural modulus up to 5700 and 1100% respectively and tensile and flexural strength up to 277% and 820% in comparison with neat EP. The obtained results can increase the interest in researching and developing products from fique Powders and other natural fibers processing byproducts thus reducing the abundance of waste in soil and landfills and environmental concerns and suggest that the EP-fique biocomposites are promising to be used in the automotive sector.

Assessing radiomics feature stability with simulated CT acquisitions.

Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the "radiomics" features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox ( www.astra-toolbox.com ). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features' stability and discriminative power.

Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors.

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient's high-risk markers, and support treatment decisions.

The Discriminative Power and Stability of Radiomics Features With Computed Tomography Variations: Task-Based Analysis in an Anthropomorphic 3D-Printed CT Phantom.

OBJECTIVES: The aims of this study were to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data. MATERIALS AND METHODS: A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. The stability and discriminative power of 86 radiomics features were assessed in measurements taken from 240 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing. Pairwise parameter group and pairwise tissue class comparisons were performed using Wilcoxon signed rank tests. RESULTS: In total, 19,264 feature stability tests and 8256 discriminative power tests were performed. The 8 CT parameter variation pairwise group comparisons had statistically significant differences on average in 78/86 radiomics features. On the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. The 86 radiomics features were ranked according to the cumulative sum of successful stability and discriminative power tests. CONCLUSIONS: The differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.

A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a possible complication of pulmonary embolism (PE), with poor prognosis if left untreated. Surgical curative treatment is available, particularly in the early stages of the disease. However, most cases are not diagnosed until specific symptoms become evident. A small number of computed tomography (CT) findings, such as a widened pulmonary artery and mosaicism in the lung parenchyma, have been correlated with pulmonary hypertension (PH). Quantitative texture analysis in the CT scans of these patients could provide complementary sub-visual information of the vascular changes taking place in the lungs. For this task, a lung graph model was developed with texture descriptors from 37 CT scans with confirmed CTEPH diagnosis and 48 CT scans from PE patients who did not develop PH. The probability of presenting CTEPH, computed with the graph model, outperformed a convolutional neural network approach using 10 different train/test splits of the data set. An accuracy of 0.76 was obtained with the proposed texture analysis, and was then compared to the visual assessment of CT findings, manually identified by a team of three expert radiologists, commonly associated with pulmonary hypertension. This graph-based score combined with the information attained from the radiological findings resulted in a Cohen's kappa coefficient of 0.47 when differentiating patients with confirmed CTEPH from those with PE who did not develop the disease. The proposed texture quantification could be an objective measurement, complementary to the current analysis of radiologists for the early detection of CTEPH and thus improve patient outcome.

Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature.

BACKGROUND: The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists. OBJECTIVES: The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text. METHODS: An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features. RESULTS: The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data sources (proprietary and public datasets, e.g., scientific literature). The system was tested by pathologists, highlighting its capabilities and suggesting ways to improve it and make it more usable in clinical practice. CONCLUSIONS: The developed system can enhance the practice of pathologists by enabling them to use their experience and knowledge to control artificial intelligence tools for navigating repositories of images for clinical decision support and teaching, where the comparison with visually similar cases can help to avoid misinterpretations. The system is available as open source, allowing the scientific community to test, ideate and develop similar systems for research and clinical practice.

Kinetics of Gluconacetobacter diazotrophicus Growth Using Cane Molasses and Sucrose: Assessment of Kinetic Models / Cinética de crecimiento de gluconacetobacter diazotrophicus usando melaza de caña y sacarosa: evaluación de modelos cinéticos

RESUMEN Gluconacetobacter diazotrophicus es una bacteria endófita promotora del crecimiento vegetal utilizada como inoculante microbiano en diferentes cultivos agrícolas. El objetivo del presente trabajo fue aplicar diferentes modelos matemáticos para representar su crecimiento en un cultivo sumergido por lotes empleando un biorreactor de 3 L y usando melazas de caña y sacarosa como fuente de energía. Se obtuvo el perfil temporal de pH, biomasa celular y azúcares totales. Se compararon los modelos estudiados por calidad de ajuste y complejidad y se realizó un análisis de sensibilidad paramétrica. Se consideraron modelos de cuatro y cinco parámetros con expresiones que incluyen efectos de inhibición por sustrato y por biomasa. El modelo con mayor calidad de ajuste fue el de Herbert-Pirt-Contois con coeficientes de determinación para biomasa y sustrato de 0,888 y 0,425 respectivamente. Estos valores indican una mayor correspondencia de los datos experimentales de biomasa con los datos calculados por el modelo, en comparación con los resultados obtenidos para azúcares totales para los que esta correspondencia fue menor. Este modelo generó la mejor combinación de calidad de ajuste y complejidad según el criterio de información de Akaike. El estudio cinético desarrollado permitió observar un comportamiento bifásico en la etapa de crecimiento de la bacteria cuando se cultiva en melaza y un efecto de limitación de su crecimiento por la biomasa. Los resultados obtenidos proporcionan una descripción matemática útil para el diseño, escalamiento y operación de un futuro proceso de producción de un inoculante microbiano a base de la bacteria G. diazotrophicus.

ABSTRACT Gluconacetobacter diazotrophicus is a plant-growth promoting endophytic bacterium used as a microbial inoculant for different crops. The objective of this work was to apply different mathematical models to represent its growth in a batch submerged culture employing a 3-L bioreactor and using sugarcane molasses and sucrose as energy sources. The time profile of pH, cell biomass, and total sugars was obtained. Models studied were compared considering their fit quality and complexity, and a parametric sensitivity analysis was performed. Four- and five-parameter models with expressions involving substrate and biomass inhibition effects were considered. The Herbert-Pirt-Contois model achieved the highest fit quality with determination coefficients of 0.888 and 0.425 for biomass and substrate, respectively. These values indicate a higher correspondence between the experimental data of biomass concentration and the data calculated by the model, compared to results obtained for total sugars for which this correspondence was lower. This model reached the best combination considering the fit quality and complexity according to the Akaike's information criterion. The kinetic study performed enabled to observe a bi-phasic behavior in the growth stage of the bacterium when grown on molasses, and a growth limitation effect due to biomass concentration. The outcomes obtained provide a mathematical description useful for design, scale-up, and operation of a future process for the production of a microbial inoculant based on G. diazotrophicus.

Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks.

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.

Processing Diabetes Mellitus Composite Events in MAGPIE.

The focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. In the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system's scalability is evaluated by comparing it with a centralized approach. The evaluation concerning the detection of temporal patterns highlights the system's ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.

Live ECG readings using Google Glass in emergency situations.

Most sudden cardiac problems require rapid treatment to preserve life. In this regard, electrocardiograms (ECG) shown on vital parameter monitoring systems help medical staff to detect problems. In some situations, such monitoring systems may display information in a less than convenient way for medical staff. For example, vital parameters are displayed on large screens outside the field of view of a surgeon during cardiac surgery. This may lead to losing time and to mistakes when problems occur during cardiac operations. In this paper we present a novel approach to display vital parameters such as the second derivative of the ECG rhythm and heart rate close to the field of view of a surgeon using Google Glass. As a preliminary assessment, we run an experimental study to verify the possibility for medical staff to identify abnormal ECG rhythms from Google Glass. This study compares 6 ECG rhythms readings from a 13.3 inch laptop screen and from the prism of Google Glass. Seven medical residents in internal medicine participated in the study. The preliminary results show that there is no difference between identifying these 6 ECG rhythms from the laptop screen versus Google Glass. Both allow close to perfect identification of the 6 common ECG rhythms. This shows the potential of connected glasses such as Google Glass to be useful in selected medical applications.

Benefits of texture analysis of dual energy CT for Computer-Aided pulmonary embolism detection.

Pulmonary embolism is an avoidable cause of death if treated immediately but delays in diagnosis and treatment lead to an increased risk. Computer-assisted image analysis of both unenhanced and contrast-enhanced computed tomography (CT) have proven useful for diagnosis of pulmonary embolism. Dual energy CT provides additional information over the standard single energy scan by generating four-dimensional (4D) data, in our case with 11 energy levels in 3D. In this paper a 4D texture analysis method capable of detecting pulmonary embolism in dual energy CT is presented. The method uses wavelet-based visual words together with an automatic geodesic-based region of interest detection algorithm to characterize the texture properties of each lung lobe. Results show an increase in performance with respect to the single energy CT analysis, as well as an accuracy gain compared to preliminary work on a small dataset.

Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons.

Epilepsy is a disorder of the brain that can lead to acute crisis and temporary loss of brain functions. Surgery is used to remove focal lesions that remain resistant to treatment. An accurate localization of epileptogenic lesions has a strong influence on the outcome of epilepsy surgery. Magnetic resonance imaging (MRI) is clinically used for lesion detection and treatment planning, mainly through simple visual analysis. However, visual inspection in MRI can be highly subjective and subtle 3D structural abnormalities are not always entirely removed during surgery. In this paper, we introduce a lesion abnormality score based on computerized comparison of the 3D texture properties between brain hemispheres in T1 MRI. Overlapping cubic texture blocks extracted from user-defined 3D regions of interest (ROI) are expressed in terms of energies of 3D steerable Riesz wavelets. The abnormality score is defined as the Hausdorff distance between the ROI and its corresponding contralateral region in the brain, both expressed as ensembles of blocks in the feature space. A classification based on the proposed score allowed an accuracy of 85% with 10 control subjects and 8 patients with epileptogenic lesions. The approach therefore constitutes a valuable tool for the objective pre-surgical evaluation of patients undergoing epilepsy surgery.