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BackgroundTo stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients.MethodsTwenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis.ResultsMedian age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (

Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Objective: Thyroid nodules are a common occurrence in adults. Although the majority of thyroid nodules are benign, a small percentage are cancerous. The combined phosphorylated histone H2AX (γ-H2AX) and p53-binding protein 1 (53BP1) assay was utilized to detect deoxyribonucleic acid (DNA) damage and DNA repair as biomarkers of the cellular stress response. Using the combined γ-H2AX and 53BP1 assay, we evaluated DNA damage, DNA repair capacity, and malignancy risk in peripheral blood mononuclear cells (PBMCs) of euthyroid individuals with nodular goiter. Materials and Methods: Peripheral blood samples were collected from 33 euthyroid patients with newly diagnosed nodular goiter and 30 healthy control participants. A fully automatic image analysis system was used for analyzing DNA damage (γ-H2AX), including DNA double-strand breaks (DSBs), and DNA repair (53BP1). Results: Euthyroid patients with nodular goiter exhibited a higher mean number of γ-H2AX foci per cell and a higher percentage of apoptotic cells compared to the control subjects (p=0.022 and p=0.005, respectively). Conclusion: This study found considerably higher DNA damage in euthyroid patients with nodular goiter than in control individuals. The increase in DNA damage occurs in the early stages of carcinogenesis. These patients were expected to exhibit compromised DNA repair along with enhanced DNA damage, increasing the risk of carcinogenesis. However, euthyroid patients with nodular goiter might be at a high risk of thyroid malignancy due to the high level of DNA damage. A long-term follow-up of these patients would provide better evidence of the relationship between DNA damage and the malignancy risk of thyroid. [ABSTRACT FROM AUTHOR]

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18, 752-771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-regionwise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object. [ABSTRACT FROM AUTHOR]

Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors. [ABSTRACT FROM AUTHOR]

Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology. [ABSTRACT FROM AUTHOR]

Glioblastoma is a grade IV pernicious neoplasm occurring in the supratentorial region of brain. As its causes are largely unknown, it is essential to understand its dynamics at the molecular level. This necessitates the identification of better diagnostic and prognostic molecular candidates. Blood‐based liquid biopsies are emerging as a novel tool for cancer biomarker discovery, guiding the treatment and improving its early detection based on their tumour origin. There exist previous studies focusing on the identification of tumour‐based biomarkers for glioblastoma. However, these biomarkers inadequately represent the underlying pathological state and incompletely illustrate the tumour because of non‐recursive nature of this approach to monitor the disease. Also, contrary to the tumour biopsies, liquid biopsies are non‐invasive and can be performed at any interval during the disease span to surveil the disease. Therefore, in this study, a unique dataset of blood‐based liquid biopsies obtained primarily from tumour‐educated blood platelets (TEP) is utilised. This RNA‐seq data from ArrayExpress is acquired comprising human cohort with 39 glioblastoma subjects and 43 healthy subjects. Canonical and machine learning approaches are applied for identification of the genomic biomarkers for glioblastoma and their crosstalks. In our study, 97 genes appeared enriched in 7 oncogenic pathways (RAF‐MAPK, P53, PRC2‐EZH2, YAP conserved, MEK‐MAPK, ErbB2 and STK33 signalling pathways) using GSEA, out of which 17 have been identified participating actively in crosstalks. Using PCA, 42 genes are found enriched in 7 pathways (cytoplasmic ribosomal proteins, translation factors, electron transport chain, ribosome, Huntington's disease, primary immunodeficiency pathways, and interferon type I signalling pathway) harbouring tumour when altered, out of which 25 actively participate in crosstalks. All the 14 pathways foster well‐known cancer hallmarks and the identified DEGs can serve as genomic biomarkers, not only for the diagnosis and prognosis of Glioblastoma but also in providing a molecular foothold for oncogenic decision making in order to fathom the disease dynamics. Moreover, SNP analysis for the identified DEGs is performed to investigate their roles in disease dynamics in an elaborated manner. These results suggest that TEPs are capable of providing disease insights just like tumour cells with an advantage of being extracted anytime during the course of disease in order to monitor it. [ABSTRACT FROM AUTHOR]

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumour heterogeneity through patterns of enhancement, texture, morphology and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of grey-level co-occurrence matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine learning approaches currently used in cancer radiomics with simulation studies.

Simple Summary: The two most common cross-sectional imaging modalities, computed tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in clinical oncology. The emergence of artificial intelligence (AI)-based tools in medical imaging has been motivated by the desire for greater efficiency and efficacy in clinical care. Although a growing number of new AI tools for narrow-specific tasks in imaging is highly encouraging, the effort to tackle the key challenges to implementation by the worldwide imaging community has yet to be appropriately addressed. In this review, we discuss a few challenges in using AI tools and offer some potential solutions with examples from lung CT and MRI of the abdomen, pelvis, and head and neck (HN) region. As we advance, AI tools may significantly enhance clinician workflows and clinical decision-making. Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients. [ABSTRACT FROM AUTHOR]

Before they become invasive, early cancer cells exhibit specific and characteristic changes that are routinely used by a histopathologist for diagnosis. Currently, these early abnormalities are only detectable ex vivo by histopathology or, non-invasively and in vivo, by optical modalities that have not been clinically implemented due to their complexity and their limited penetration in tissues. Optical coherence tomography (OCT) is a noninvasive medical imaging technology with increasing clinical applications in areas such as ophthalmology, cardiology, gastroenterology, etc. In addition to imaging the tissue micro-structure, OCT can also provide additional information, describing the constituents and state of the cellular components of the tissue. Estimates of the nuclear size, sub-cellular morphological variations, dispersion and index of refraction can be extracted from the OCT images and can serve as diagnostically useful biomarkers. Moreover, the development of fully automated algorithms for tissue segmentation and feature extraction and the application of machine learning, can further enhance the clinical potential of OCT. When fully exploited, OCT has the potential to lead to accurate and sensitive, image-derived, biomarkers for disease diagnosis and treatment monitoring of cancer. [ABSTRACT FROM AUTHOR]

A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values from surgically extracted tumor tissues are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored in some cases to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction ensemble learning and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain for learning and validation and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases. [ABSTRACT FROM AUTHOR]

To study if pre-treatment CT texture features in locally advanced squamous cell carcinoma of laryngo-pharynx can predict long-term local control and laryngectomy free survival (LFS). Image texture features of 60 patients treated with chemoradiation (CTRT) within an ethically approved study were studied on contrast-enhanced images using a texture analysis research software (TexRad, UK). A filtration-histogram technique was used where the filtration step extracted and enhanced features of different sizes and intensity variations corresponding to a particular spatial scale filter (SSF): SSF = 0 (without filtration), SSF = 2 mm (fine texture), SSF = 3–5 mm (medium texture) and SSF = 6 mm (coarse texture). Quantification by statistical and histogram technique comprised mean intensity, standard-deviation, entropy, mean positive pixels, skewness and kurtosis. The ability of texture analysis to predict LFS or local control was determined using Kaplan–Meier analysis and multivariate cox model. Median follow-up of patients was 24 months (95% CI:20–28). 39 (65%) patients were locally controlled at last follow-up. 10 (16%) had undergone salvage laryngectomy after CTRT. For both local control & LFS, threshold optimal cut-off values of texture features were analyzed. Medium filtered-texture feature that were associated with poorer laryngectomy free survival were entropy ≥4.54, (p = 0.006), kurtosis ≥4.18; p = 0.019, skewness ≤−0.59, p = 0.001, and standard deviation ≥43.18; p = 0.009). Inferior local control was associated with medium filtered features entropy ≥4.54; p 0.01 and skewness ≤ – 0.12; p = 0.02. Using fine filters, entropy ≥4.29 and kurtosis ≥−0.27 were also associated with inferior local control (p = 0.01 for both parameters). Multivariate analysis showed medium filter entropy as an independent predictor for LFS and local control (p < 0.001 & p = 0.001). Medium texture entropy is a predictor for inferior local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancer and this can complement clinico-radiological factors in predicting prognosticating these tumors. Texture features play an important role as a surrogate imaging biomarker for predicting local control and laryngectomy free survival in locally advanced laryngo-pharyngeal tumors treated with definitive chemoradiation. [ABSTRACT FROM AUTHOR]

The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field. [ABSTRACT FROM AUTHOR]

Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic “expression maps”, we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone. [ABSTRACT FROM AUTHOR]

Cancer is a generic term for a large group of diseases that can affect any part of the body. Lungs cancer is one of the most common and serious type of cancer. Lung cancer is the leading cause of cancer death worldwide [1]. In US alone, the estimated new lung cancer cases for 2017 are 222, 500 out of which 116, 990 are males and 105, 510 females. Estimated deaths are 155, 870 out of which 84, 590 are male and 71, 280 are female [2]. In India, the estimated cases of men and women in 2012 were 54000 & 17000 respectively [1]. Lungs cancer is a malignant lungs tumor characterized by uncontrolled cell growth in tissues of the lung. If it is left untreated, this growth can spread beyond the lungs by process of metastasis into nearby tissues or other parts of the body [3]. There are mainly two types of lungs cancer small-cell lungs carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC). NSCLC also categorized into three parts- Adenocarcinoma, Squamous cell carcinoma and Large cell carcinoma. The most common symptoms are coughing up blood, weight loss, shortness of breath, cough that does not go away, coughing up blood, fatigue, losing weight without trying, loss of appetite, shortness of breath and wheezing and chest pain. The vast majority (85%) of cases of lungs cancer are due to long -term tobacco smoking. About 10-15% of cases occur in people who have never smoked but it happened due to genetic factors, exposure to radon gas, asbestos, second hand smoke or other form of air pollution. Lungs cancer may be seen on chest radiographs and computed tomography but diagnosis confirmed by biopsy which is usually performed by bronchoscope or CT or by PET/CT guidance. F18-FDG PET/CT scanning is best for all types of cancers because of better sensitivity and specificity compared to anatomical imaging such as CT as it provides estimates of tumor glucose metabolism [4]. Standardized uptake value (SUV) is the semi-quantitative parameter which can be estimated from F18-FDG PET studies and routinely used for characterizing of the tumor and assessment of treatment response evaluation in these patients. The cut off value of SUV is 3.5 above which tumor is malignant otherwise benign [5]. However, there are many variables such as amount of activity injected, blood glucose level, time of injection and weight of patient, which can affect the estimation of SUV. Hence, majority of nuclear medicine physician rely more on their visual assessment and use it for reporting the F18-FDG PET/CT Scan. Now, the nuclear medicine community is looking for another reliable quantitative parameters extracted from the image that will be used in diagnosis and/or treatment response evaluation. Image processing algorithms have potential to assist in lesion (e.g. nodule) detection on PET/CT studies and to assess the stability or change in size of lesion on serial PET/CT studies. Comparison and evaluation of image processing techniques against each other require common data sets and standardized methods for evaluation. Investigators developing image processing algorithms need standardized databases with which to work. Therefore, there is a need for F18-FDG PET/CT image database as research resource for medical image processing. PET image texture analysis was proposed to characterize the heterogeneity of tumor F18-FDG uptake [6-9, 37]. F18-FDG uptake is not homogeneous across the tumor because of necrosis, cell proliferation, micro vessel density, and hypoxia [6, 10-12]. It has been shown that tumor heterogeneity can be associated with disease progression, response to therapy, and malignant behavior of the tumor [6, 13]. Texture analysis refers to a variety of mathematical methods that may be applied to describe the relationship between the grey level intensity of pixels or voxels and their position within an image. An advantage of measuring textural parameters is that it is a post processing technique that can be applied to data acquired during standard clinical imaging protocols thereby maximizing the information that can be derived from standard clinical images. A number of textural features can be derived that provide a measure of intralesional heterogeneity e.g. angular second moment, inverse difference moment, entropy, correlation etc. [6, 14, 26]. Texture analysis has been extensively used in CT images and has given promising results as a predictor of survival and in treatment response assessment in NSCLC and other carcinomas [15-18, 27], but it is still new and emerging field in PET/CT. Very few studies has been found in the literature in which texture analysis is used in F18-FDG PET/CT scans to predict patient outcome and treatment response in oncology[19-22, 34-36]. However, only one study has been found in literature on the prediction of treatment response of NSCLC using texture analysis in F18-FDG PET/CT scans [22]. Local Binary Pattern (LBP) is a method of texture analysis it is based on small area. It is based on the texture spectrum model and provides an additional statistical approach to texture analysis. In texture spectrum model a concept of texture unit is proposed. The texture unit is defined for each pixel value by the eight neighboring pixels values in a 3x3 matrix. Each neighboring pixel is compared to the central pixel and a texture unit value is assigned accordingly. Neighboring intensities with threshold lower values compared to the reference pixel are marked with 0, intensity values equal or greater than the reference pixel are marked with 1. The texture unit is read in starting from upper left corner of the newly calculated 3x3 matrix proceeding clockwise. The intent of this study is to construct a database of F18-FDG PET/ CT images of lung masses. [ABSTRACT FROM AUTHOR]

This proposed work implements various classification approaches to classify the H&E-stained Follicular Lymphoma tissue sample. As part of the process, k-means clustering is used to isolate cytological components like the nucleus extracellular and cytoplasmic regions from images. Then feature space vector is constructed by combining global feature extraction techniques like LBP, LDP, GLCM, and other local feature-extraction techniques. To classify FL images into their respective grades, feature space vectors obtained from feature extraction algorithms are input into a multiclass SVM. Moreover, classification accuracies were explicitly tested with different classifiers like CNN and other pre-trained deep learning networks that can directly operate on raw images without any preprocessing to classify the FL images into their respective grades. The efficacy of different classifiers is presented. This preliminary work provides a proof of concept for incorporating automated FL tissue diagnostic systems into future pathology workflows to supplement the pathologists'. [ABSTRACT FROM AUTHOR]

Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging. [ABSTRACT FROM AUTHOR]

To accurately detect and track the thyroid nodules in a video is a crucial step in the thyroid screening for identification of benign and malignant nodules in computer-aided diagnosis (CAD) systems. Most existing methods just perform excellent on static frames selected manually from ultrasound videos. However, manual acquisition is labor-intensive work. To make the thyroid screening process in a more natural way with less labor operations, we develop a well-designed framework suitable for practical applications for thyroid nodule detection in ultrasound videos. Particularly, in order to make full use of the characteristics of thyroid videos, we propose a novel post-processing approach, called Cache-Track, which exploits the contextual relation among video frames to propagate the detection results into adjacent frames to refine the detection results. Additionally, our method can not only detect and count thyroid nodules, but also track and monitor surrounding tissues, which can greatly reduce the labor work and achieve computer-aided diagnosis. Experimental results show that our method performs better in balancing accuracy and speed. [ABSTRACT FROM AUTHOR]

Objective: Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. Methods: We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. Results: Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). Conclusion: The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. Significance: This study presents a new method to develop CAD schemes with significantly higher and robust performance. [ABSTRACT FROM AUTHOR]

Prostate Cancer (PCa) is one among the prominent causes of mortality in men, which can only be reduced by early diagnosis. Multi-parametric Magnetic Resonance Imaging (mp-MRI) is increasingly utilized by clinicians for performing diagnostics tasks because it possesses functional and morphological competencies. Although, manual segmentation of PCa on MRI is a tedious, operator-dependent and time consuming task. Therefore, Computer Aided Diagnosis (CAD) of PCa using mp-MRI images is highly desirable by employing computer-assisted segmentation approaches. In this paper, a method is proposed for segmentation of PCa based on level set with Particle Swarm Optimization (PSO) technique to address the limitations of existing techniques as PSO does not require any cost or objective function to be differentiable and it is easy to implement. The energy function is optimized with PSO based technique. The proposed approach is tested over three different mp-MRI modalities i.e., T2 weighted (T2w), Dynamic Contrast Enhanced (DCE) images and Apparent Diffusion Coefficient (ADC) Maps derived from Diffusion Weighted Images (DWI). The accuracy achieved by PSO based methodology is 7.6% greater than without PSO integration i.e., using Gradient descent with added computational overhead of 0.03 s. The experimental outcomes reveal that the proposed methodology shows better results in terms of considered evaluation metrics when compared with the existing techniques on the I2CVB dataset. The impact of the proposed methodology is that it has the ability for precise segmentation even with intensity inhomogeneity, which validates its applications in clinical treatments. Additionally, the proposed technique reduces the manual interference, which in turn minimizes the execution time. [ABSTRACT FROM AUTHOR]

Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%. [ABSTRACT FROM AUTHOR]

Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre‐processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre‐processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern‐based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern‐based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto‐encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K‐fold. [ABSTRACT FROM AUTHOR]

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer‐free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi‐level thresholding based on Rényi's two‐dimensional entropy with a cultural algorithm (2DReCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey‐level co‐occurrence matrix and block‐wise elliptical local binary pattern) are calculated based on pre‐processed grey‐scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [ABSTRACT FROM AUTHOR]

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases. [ABSTRACT FROM AUTHOR]

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer‐aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker‐based watershed segmentation. After segmentation, a two‐stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS‐DDSM) database and all the mammographic images that contain masses from the mini‐mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied. [ABSTRACT FROM AUTHOR]