Extraction Of Quantitative Features Research Articles

Simultaneous [formula omitted] and [formula omitted] imaging in premixed turbulent flames for instantaneous flame structure visualization

Photofragmentation laser-induced fluorescence (PF-LIF) of methyl radical (CH3) and planar laser-induced fluorescence (PLIF) of formaldehyde (CH2O) were simultaneously carried out in premixed methane-air turbulent flames to visualize flame structure and extract quantitative features of CH3 and CH2O layers. A CH3 layer is located along the edge of a CH2O layer, and they partially overlap with each other. CH3 is closer to the reaction zone of a premixed flame compared with CH2O that is regarded as a typical marker of the preheating zone. The results show that the CH3 can be served as a flame marker for the transitional zone between the preheating zone and the reaction zone. The thicknesses of CH3 and CH2O layers and their distances were then quantitatively estimated to reduce the influence of layer merging and strong curvature. It was found that the broadening of CH2O layers against turbulence is more sensitive than that of CH3 layers. In stoichiometric and rich flames, layer broadening was clearly observed even with a slightly increased Karlovitz number.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.

Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.

Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows.

Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach.

Radiological tumor classification across imaging modality and histology.

Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.

Accurate and practical feature extraction from noisy holograms.

Quantitative phase imaging using holographic microscopy is a powerful and non-invasive imaging method, ideal for studying cells and quantifying their features such as size, thickness, and dry mass. However, biological materials scatter little light, and the resulting low signal-to-noise ratio in holograms complicates any downstream feature extraction and hence applications. More specifically, unwrapping phase maps from noisy holograms often fails or requires extensive computational resources. We present a strategy for overcoming the noise limitation: rather than a traditional phase-unwrapping method, we extract the continuous phase values from holograms by using a phase-generation technique based on conditional generative adversarial networks employing a Pix2Pix architecture. We demonstrate that a network trained on random surfaces can accurately generate phase maps for test objects such as dumbbells, spheres, and biconcave discoids. Furthermore, we show that even a rapidly trained network can generate faithful phase maps when trained on related objects. We are able to accurately extract both morphological and quantitative features from the noisy phase maps of human leukemia (HL-60) cells, where traditional phase unwrapping algorithms fail. We conclude that deep learning can decouple noise from signal, expanding potential applications to real-world systems that may be noisy.

Radiomic features in recurrent/metastatic platinum refractory head and neck squamous cell carcinoma treated with immunotherapy.

e18000 Background: Radiomics is the computerized extraction of quantitative features from medical images, beyond the level of detail accessible to an unaided human eye. Several studies on radiomic analysis have been carried out to identify predictive, prognostic and diagnostic biomarkers for diverse tumor types including HNSCC. Radiomic features proved to be effective in predicting outcomes in patients with locally advanced HNSCC. The aim of the present study was to dentify predictive and prognostic radiomic features in platinum-refractory HNSCC patients with recurrent and/or metastatic (R/M) disease treated with anti PD-1 monotherapy. Methods: We retrospectively reviewed the data of 38 patients treated with Nivolumab at our Institution between January 2018 and March 2020. Nasopharyngeal carcinomas were not eligible. CT radiomic textural features were extracted from regions of interests (ROIs) manually delineated around tumor volumes. Minimum Redundancy Maximum Relevance (mRMR) algorithm was the method of choice to rank radiomic features based on three outcomes: overall response rate (ORR); disease progression (PD) and overall survival (OS). Logistic regression was employed to build predictive/prognostic models. Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was chosen as the metric to assess model performances. Results: Data from 29 out of 38 patients were ultimately analyzed. Nine patients were excluded due to nasopharynx as primary tumor site and/or inadequate CT scan imaging. A total of 57 ROIs were extracted and analyzed. We obtained 9 models: 3 radiomic models, 3 clinical models and 3 combined models (radiomic plus clinical) for each outcome of interest (ORR, PD, OS). In the radiomic models 2 features were identified as predictor for ORR (AUC 0.69), 1 radiomic feature was predictive for PD (AUC 0.83) and 1 predictive for OS (AUC 0.72). In the clinical models, 3 clinical characteristics were identified for both ORR (AUC 0.91) and PD (AUC 0.73) and 2 clinical features were found to be predictive for OS (AUC 0.91). The combined model identified 3 features (2 radiomics and 1 clinical) predictive for ORR (AUC 0.76). No clinical characteristics were found in the combined model for PD. No radiomics features have been shown to be related to OS. Conclusions: This is an explorative study to test the power of radiomics and the potential value of combining radiomics and clinical data as a prognostic and predictive instrument. In this small sample size, the quantitative analysis of CT images seems to be an interesting tool which has to be further explored as predictor of outcome in a larger series of patients.[Table: see text]

The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset.

Radiomics–the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those–the “ComBatable” RFs–which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.

Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics

Glioma is one of the most common and deadly malignant brain tumors originating from glial cells. For personalized treatment, an accurate preoperative prognosis for glioma patients is highly desired. Recently, various machine learning-based approaches have been developed to predict the prognosis based on preoperative magnetic resonance imaging (MRI) radiomics, which extract quantitative features from radiographic images. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. This study investigates two machine learning-based prognosis prediction tasks using radiomic features extracted from preoperative multimodal MRI brain data: (i) prediction of tumor grade (higher-grade vs. lower-grade gliomas) from preoperative MRI scans and (ii) prediction of patient overall survival (OS) in higher-grade gliomas (<12 months vs. > 12 months) from preoperative MRI scans. Specifically, these two tasks utilize the conventional machine learning-based models built with various classifiers. Moreover, feature selection methods are applied to increase model performance and decrease computational costs. In the experiments, models are evaluated in terms of their predictive performance and stability using a bootstrap approach. Experimental results show that classifier choice and feature selection technique plays a significant role in model performance and stability for both tasks; a variability analysis indicates that classification method choice is the most dominant source of performance variation for both tasks.

AB082. SOH21AS180. A radiomic model to classify adrenal lesions on CT

Background: Medical imaging analysis has evolved to facilitate the development of AI-enhanced methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of adrenal pathology. Adrenal lesions can be classified as benign/malignant or originating in the cortex/medulla. Qualitative radiological features can contribute to diagnosis however histopathological assessment is required to definitively establish the type of lesion. There is a need for non-invasive diagnostic markers due to the impracticality of adrenal biopsy. The aim of this study was to develop and validate a radiomic classifier to non-invasively classify adrenal lesions on CT.

AB051. SOH21AS114. A radiogenomic model to classify response to neoadjuvant chemotherapy

Background: Medical imaging analysis has evolved to facilitate the development of AI-enhanced methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), response to NAC can be estimated pre-operatively based on the molecular subtype and biological characteristics of the tumour. However, there remains a lack of accurate predictive markers of response to NAC. The aim of this study was to develop and validate a radiogenomic classifier to predict the response to NAC pre-operatively in breast cancer.

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma.

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

Abstract PO-030: Radiomics for head and neck cancer prognostication: results from the RADCURE machine learning challenge

Abstract Recently, there has been significant interest in using rich, multi-modal data routinely collected in the clinic, including imaging, demographic and clinical information for prognostic factor discovery in cancer. In particular, the emerging field of radiomics makes use of computational tools to extract quantitative features from radiological images, with the aim of capturing the morphological and biological characteristics of tumors. Previous studies have demonstrated the potential of computed tomography (CT) imaging features as independent prognostic factors for overall survival in multiple types of cancer, including head and neck (HNC). However, poor reproducibility and lack of large, rigorous validation studies have hindered widespread clinical use of radiomics so far. We conducted a HNC survival prediction challenge with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors. Using a large, retrospective cohort of 2552 patients, we assessed prognostic performance of 12 different approaches developed by several research groups at University Health Network in Toronto, making use of engineered radiomics, deep learning, clinical information and combinations of those. To allow for unbiased comparison between different approaches, all participants had access to a public training dataset of 1802 patients, while 750 were held out for evaluation. The best challenge submission used a deep multi-task learning framework on clinical data and tumour volume, achieving area under the ROC curve (AUROC) of 0.812 [95% CI 0.763–0.858] for 2-year survival prediction and concordance (C) index for lifetime risk prediction of 0.795 [0.751–0.838], outperforming the best clinical-only model (AUROC=0.800 [0.749–0.848], C=0.708 [0.661–0.754]), best radiomics-only model (AUROC=0.766 [0.719–0.811], C=0.748 [0.704–0.790]), as well as the best model combining deep radiomics with clinical features (AUROC=0.786 [0.733–0.836], C=0.774 [0.726–0.820]). We also used a ‘wisdom of the crowds’ ensemble approach to combine the predictions of all challenge submissions to determine whether The ensemble achieved stronger performance than any individual model (AUROC=0.823 [0.778–0.864], C=0.810 [0.772–0.845]) indicating that there might be complementary information between the different data modalities. Our rigorous challenge framework allowed us to evaluate a diverse collection of prognostic models in a large multi-modal dataset, demonstrating the value of machine learning in HNC prognostication, as well as the advantages of simple imaging features over several hand-engineered and deep radiomics approaches. Furthemore, our ensemble approach achieves excellent performance for both 2-year and lifetime risk prediction, establishing new state-of-the-art in HNC prognostic modelling. Citation Format: Michal Kazmierski, Mattea Welch, Benjamin Haibe-Kains. Radiomics for head and neck cancer prognostication: results from the RADCURE machine learning challenge [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-030.

Feature-based PET/MRI radiomics in patients with brain tumors.

Radiomics allows the extraction of quantitative features from medical images such as CT, MRI, or PET, thereby providing additional, potentially relevant diagnostic information for clinical decision-making. Because the computation of these features is performed highly automated on medical images acquired during routine follow-up, radiomics offers this information at low cost. Further, the radiomics features can be used alone or combined with other clinical or histomolecular parameters to generate predictive or prognostic mathematical models. These models can then be applied for various important diagnostic indications in neuro-oncology, for example, to noninvasively predict relevant biomarkers in glioma patients, to differentiate between treatment-related changes and local brain tumor relapse, or to predict treatment response. In recent years, amino acid PET has become an important diagnostic tool in patients with brain tumors. Therefore, the number of studies in patients with brain tumors investigating the potential of PET radiomics or combined PET/MRI radiomics is steadily increasing. This review summarizes current research regarding feature-based PET as well as combined PET/MRI radiomics in neuro-oncology.

How does Radiomics actually work? - Review.

Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades, applications of artificial intelligence are increasingly attracting scientific interest as they could substantially expand the possibility of objective quantification and diagnostic or prognostic use of image information.In this context, the term "radiomics" describes the extraction of quantitative features from imaging data such as those obtained from computed tomography or magnetic resonance imaging examinations. These features are associated with predictive goals such as diagnosis or prognosis using machine learning models. It is believed that the integrative assessment of the feature patterns thus obtained, in combination with clinical, molecular and genetic data, can enable a more accurate characterization of the pathophysiology of diseases and more precise prediction of therapy response and outcome.This review describes the classical radiomics approach and discusses the existing very large variability of approaches. Finally, it outlines the research directions in which the interdisciplinary field of radiology and computer science is moving, characterized by increasingly close collaborations and the need for new educational concepts. The aim is to provide a basis for responsible and comprehensible handling of the data and analytical methods used. KEY POINTS:: · Radiomics is playing an increasingly important role in imaging research.. · Radiomics has great potential to meet the requirements ofprecision medicine.. · Radiomics analysis is still subject to great variability.. · There is a need for quality-assured application of radiomics in medicine.. CITATION FORMAT: · Attenberger UI, Langs G, . How does Radiomics actually work? - Review. Fortschr Röntgenstr 2021; 193: 652 - 657.

Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis.

In the last decades, significant advances have been made in the preventive approaches to cardiovascular disease. Even so, coronary artery disease remains one of the main causes of morbidity and mortality worldwide. Invasive imaging modalities, such as intravascular ultrasound or optical coherence tomography, have played a key role in the comprehension of the pathological processes underlying myocardial infarction and cerebrovascular disease. These imaging techniques have contributed greatly to the identification and phenotyping of the culprit lesion, the so-called vulnerable plaque. Coronary computed tomographic angiography (CCTA) has emerged in more recent years as the non-invasive modality of choice in the study of coronary atherosclerosis, showing in many studies a diagnostic yield comparable to invasive approaches. Moreover, being able to describe extra-luminal characteristics of the affected vessel, CCTA has greatly contributed towards shifting the attention of researchers from the mere quantification of luminal stenosis to the identification of adverse plaque features, which appear to have a stronger prognostic value. However, the identification of some of the hallmarks of vulnerable plaques is qualitative in nature and, therefore, subject to some degree of inter-reader variability. Moreover, CCTA is still unable to identify some fine markers of plaque vulnerability which can be detected by invasive techniques, such as neovascularization and plaque erosion, among others. Nonetheless, radiological images can be viewed as vast 3-D datasets which, via the use of recent technology, allow for the extraction of numerous quantitative features that may be used to accurately phenotype a given lesion. Radiomics is the process of extrapolating innumerable parameters from a given region of interest, with the goal of establishing correlations between quantitative variables and clinical data. These datasets can then be manipulated to create predictive models via the use of automated algorithms in a process called machine learning. As a result of these approaches, radiological images may offer information regarding the characterization of a plaque which can go much beyond the boundaries of what can be qualitatively asserted by the human eye, contributing to expanding the knowledge of the disease and ultimately assist clinical decisions. Thus far, radiomics has found its more consistent area of application in the field of oncology; to present date, the amount of clinical data regarding coronary artery disease is still relatively small, partly due to the technical difficulties associated with the implementation of such techniques to the study of a small and geometrically complex lesion such as the coronary plaque. The present review, after a summary of the imaging modalities most commonly used nowadays in the study of coronary plaques, will provide a perspective on the application of radiomic analysis to coronary artery disease.

Using deep learning to extract novel and quantitative imaging features from perfluoropropane contrast, sulphur hexafluoride contrast and non-contrast stress echocardiography images

Abstract Background Stress echocardiography has become established as the most widely applied non-invasive imaging test for diagnosis of coronary artery disease within the UK. However, stress echocardiography has been substantially qualitative, rather than quantitative, based on visual wall motion assessment. For the first time, we have identified and validated quantitative descriptors of cardiac geometry and motion, extracted from ultrasound images acquired using contrast agents in an automated way. Purpose To establish whether these novel imaging features can be generated in an automated, quantifiable and reproducible way from images acquired with perfluoropropane contrast, as well as investigating how these extracted measures compare to those extracted from sulphur hexafluoride contrast and non-contrast studies. Methods 100 patients who received perfluoropropane contrast during their stress echocardiogram were recruited. Their stress echocardiography images were processed through a deep learning algorithm. Novel feature values were recorded and a subset of 10 studies were repeated. The automated measures of global longitudinal strain (GLS) and ejection fraction (EF) extracted from these images were compared to values previously extracted from sulphur hexafluoride contrast and non-contrast images using the same software. Results A full set of 31 novel imaging features were successfully extracted from 79 studies acquired using the perfluoropropane contrast agent with a dropout rate of 14% (n=92, 8 incomplete image sets). Repeated analysis in a subset of 10 perfluoropropane cases demonstrated excellent reproducibility of the extracted feature values (R2=1). Automated values of GLS and EF, at both rest (GLS = −16.4±4.8%, EF = 63±13%) and stress stages (GLS = −17.7±5.8%, EF = 68±11%), were extracted from 83 perfluoropropane studies, with a dropout rate of 16% (n=99, fewer incomplete sets as short axis view not required). The ranges of GLS and EF measures extracted from the perfluoropropane images were comparable to the other contrast studies (n=222) (Rest GLS = −16.8±5.8%, Rest EF = 63±10%; Stress GLS = −19.1±6.7%, Stress EF = 71±9%) and non-contrast studies (n=86) (Rest GLS = −15.7±5.3%, Rest EF = 57±10%; Stress GLS = −17.3±6.4%, Stress EF = 61±14%). Conclusions Novel features and clinically relevant measures were extracted from images acquired using perfluoropropane contrast for the first time in a fully automated and reproducible way using a deep learning algorithm. The analysis failure rate and generated measures are comparable to those extracted from images using other commonly used sulphur hexafluoride contrast agents and non-contrast stress echocardiography studies. These findings demonstrate that deep learning algorithms can be used for automated quantitative analysis of stress echocardiograms acquired using various contrast agents and in non-contrast studies to improve stress echocardiography practice. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Lantheus Medical Imaging, Inc.

On the application of multi-parametric optical phenotyping of bacterial colonies for multipurpose microbiological diagnostics

The development of new diagnostics techniques and modalities is critical for early detection of microbial contamination. In this study, the novel integrated system for multi-parametric optical phenotyping and characterization of bacterial colonies, is presented. The system combines Mach-Zehnder interferometer with a spectral imaging system for capturing multispectral diffraction patterns and multispectral two-dimensional transmission maps of bacterial colonies, along with the simultaneous interferometric profilometry. The herein presented investigation was carried out on five representative bacteria species and nearly 3000 registered multispectral optical signatures. The interferograms were analyzed by four-step phase shift algorithm to reconstruct the colony profile to enable the obtaining of the comparable optical signatures. The dedicated image processing algorithms were used for extraction of quantitative features of these signatures. The random forest algorithm was applied for selection of the most predictive set of features, which were used in classification model based on Support-Vector Machine. Obtained results have shown that the use of multiple multispectral optical signatures provide a multi-parametric bacteria identification at an exceptionally high accuracy (99.4-100%), significantly better than in case of classification based on each of these signatures (multispectral diffraction patterns, two-dimensional transmission coefficient maps), separately. Obtained results revealed that analysis of multispectral signatures can also be applied for characterisation of physical, physicochemical and chemical properties of the bacterial colonies in the presence of the antimicrobial factors. Therefore, the proposed label-free, non-destructive optical technique has perspectives to be exploited in the multipurpose diagnostics and it can be used as a pre-screening tool in microbiological laboratories.

Diagnosis of Diabetic Retinopathy Utilizing Computer-Aided Diagnosis System

Purpose: Diabetes is considered one of most diseases spread among people; blindness is considered the most resulted effect. Diabetes can damage the retinal blood vessels and cause severe problems to the eyes, which may end with sight loss. Such medical condition is known as "diabetic retinopathy" (DR). In such a diagnosis, the retinal microvascular go through several stages of change threat. In the early stages of the DR, detecting the formation that happened to the retinal blood vessels helps prevent the disease's dangerous effects. Therefore, producing a method to diagnose the disease in the early stages is helpful. So, this work aimed to develop a system of detecting and classifying the retina formation, trying to avoid relevant effects.Methodology: The current method depends on vascular edges map extracted from images of retina captured by a fundus camera. Such a map been utilized to extract quantitative texture features. The system tested two independent groups of the region of interest in normal and abnormal images. The two sets were extracted from ground truth images of the 89 fundus images. Fundus images were annotated images from the Standard Diabetic Retinopathy Database (DIARETDB1).Findings: The system provided an accuracy of 71% with a sensitivity of 75%.Recommendation: The current work may open an opportunity for improvement for future work. Other methods may be reached to raise the accuracy and the sensitivity of the system. Besides, the current system may be tested on a larger sample size to study such effects. Finally, the ease of the current method makes it faster in adoption in the appropriate diagnosis especially in the early stages.

1251P Exploring quantitative biomarkers from different tumour volumes for radiomics in lung cancer

Recent advances in medical imaging technology allow the use of advanced image analysis methods by the extraction of quantitative features from medical images to characterize tumor biology in order to provide informations that may be useful to guide therapies and predict survival. In our previous experience, a radiomics signature mixing semantic and image-based features was able to predict the reduction of the target volume during chemoradiation in LA-NSCLC. The aim of this study is to investigate which 3D ROI contain more quantitative information that can be exploit for a radiomic approach in lung cancer.