Texture based feature extraction methods for content based medical image retrieval systems

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.

Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis

BACKGROUND

The aim of this study is to explore the utility of cardiac magnetic resonance (CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis (CS).

METHODS

Subjects were classified into active cardiac sarcoidosis (CSactive) and inactive cardiac sarcoidosis (CSinactive) based on PET-CMR imaging. CSactive was classified as featuring patchy [18F]fluorodeoxyglucose ([18F]FDG) uptake on PET and presence of late gadolinium enhancement (LGE) on CMR, while CSinactive was classified as featuring no [18F]FDG uptake in the presence of LGE on CMR. Among those screened, thirty CSactive and thirty-one CSinactive patients met these criteria. A total of 94 radiomic features were subsequently extracted using PyRadiomics. The values of individual features were compared between CSactive and CSinactive using the Mann-Whitney U test. Subsequently, machine learning (ML) approaches were tested. ML was applied to two sub-sets of radiomic features (signatures A and B) that were selected by logistic regression and PCA, respectively.

RESULTS

Univariate analysis of individual features showed no significant differences. Of all features, gray level co-occurrence matrix (GLCM) joint entropy had a good area under the curve (AUC) and accuracy with the smallest confidence interval, suggesting it may be a good target for further investigation. Some ML classifiers achieved reasonable discrimination between CSactive and CSinactive patients. With signature A, support vector machine and k-neighbors showed good performance with AUC (0.77 and 0.73) and accuracy (0.67 and 0.72), respectively. With signature B, decision tree demonstrated AUC and accuracy around 0.7; Conclusion: CMR radiomic analysis in CS provides promising results to distinguish patients with active and inactive disease.

Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management

Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images

BACKGROUND

Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.

METHODS

Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio of 7:3, and convolutional neural networks (CNN) models and random forest (RF) models were established. Survival analyses were performed for patients with solid adenocarcinomas.

RESULTS

Totally 720 solid pulmonary nodules were enrolled, 348 benign and 372 malignant. The CNN model with clinical features achieved the highest AUC [0.819, 95% confidence interval (CI): 0.760-0.877] with a sensitivity of 0.778, specificity of 0.788 and accuracy of 0.783. No significant differences were observed between the CNN and radiomics models. There were 295 solid adenocarcinomas in survival analysis. Different disease-free survival was observed between the low-risk and high-risk groups divided according to the radiomics Rad-score. However, the groups based on deep learning signatures showed similar survival. Cox regression analysis indicated that the radiomics Rad-score (hazard ratio: 5.08, 95% CI: 2.61-9.90) was an independent predictor of recurrence.

CONCLUSIONS

The radiomics and deep learning models can well predict the malignancy of solid pulmonary nodules. Radiomics signatures also demonstrate prognostic value in solid adenocarcinomas.

An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the P. falciparum stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments.

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.

Radiomics: the facts and the challenges of image analysis

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

In vivo automated quantification of thermally damaged human tissue using polarization sensitive optical coherence tomography

Over the decades numerous technologies have been performed for the quantification of skin injuries, but their poor sensitivity, specificity and accuracy limits their applications. Optical coherence tomography (OCT) can be potential tool for the identification but the modern high-speed OCT system acquires huge amount of data, which will be very time-consuming and tedious process for human interpretation. Our proposed method opens the possibility of fully automated quantitative analysis based on morphological features of thermally damaged tissue, which will become biomarker for the removal of non-viable skin. The proposed method is based on multi-level ensemble classifier by dissociating morphological features (A-line, B-scan, phase images) extracted from Polarization Sensitive Optical Coherence Tomography (PS-OCT) images. Our proposed classifier attained the average sensitivity, specificity and accuracy is 92.22%, 87.2% and 92.5%, respectively, in detecting the thermally damaged human skin. Moreover, we show that our classifier is one of the best possible classifier based on features extracted from PS-OCT images, which demonstrates the significance of PS-OCT data in detecting abnormality in human skin.

CD30 cell graphs of Hodgkin lymphoma are not scale-free--an image analysis approach

MOTIVATION

Hodgkin lymphoma (HL) is a type of B-cell lymphoma. To diagnose the subtypes, biopsies are taken and immunostained. The slides are scanned to produce high-resolution digital whole slide images (WSI). Pathologists manually inspect the spatial distribution of cells, but little is known on the statistical properties of cell distributions in WSIs. Such properties would give valuable information for the construction of theoretical models that describe the invasion of malignant cells in the lymph node and the intercellular interactions.

RESULTS

In this work, we define and discuss HL cell graphs. We identify CD30(+) cells in HL WSIs, bringing together the fields of digital imaging and network analysis. We define special graphs based on the positions of the immunostained cells. We present an automatic analysis of complete WSIs to determine significant morphological and immunohistochemical features of HL cells and their spatial distribution in the lymph node tissue under three different medical conditions: lymphadenitis (LA) and two types of HL. We analyze the vertex degree distributions of CD30 cell graphs and compare them to a null model. CD30 cell graphs show higher vertex degrees than expected by a random unit disk graph, suggesting clustering of the cells. We found that a gamma distribution is suitable to model the vertex degree distributions of CD30 cell graphs, meaning that they are not scale-free. Moreover, we compare the graphs for LA and two subtypes of HL. LA and classical HL showed different vertex degree distributions. The vertex degree distributions of the two HL subtypes NScHL and mixed cellularity HL (MXcHL) were similar.

AVAILABILITY AND IMPLEMENTATION

The CellProfiler pipeline used for cell detection is available at https://sourceforge.net/projects/cellgraphs/.

CONTACT

ina.koch@bioinformatik.uni-frankfurt.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.