Radiographic Image Radiomics Feature Reproducibility: A Preliminary Study on the Impact of Field Size.

RATIONALE AND OBJECTIVES: Radiomics is an approach to quantifying diseases. Recently, several studies have indicated that radiomics features are vulnerable against imaging parameters. The aim of this study is to assess how radiomics features change with radiographic field sizes, positions in the field size, and mAs. MATERIALS AND METHODS: A large and small wood phantom and a cotton phantom were prepared and imaged in different field sizes, mAs, and placement in the radiographic field size. A region of interest was drawn on the image features, and twenty two features were extracted. Radiomics feature reproducibility was obtained based on coefficient of variation, Bland-Altman analysis, and intraclass correlation coefficient. Features with coefficient of variation ≤ 5%, intraclass correlation coefficient ≤ 90%, and 1% ≤ U/LRL ≤30% were introduced as robust features. U/LRL is upper/lower reproducibility limits in Bland-Altman. RESULTS: For all field sizes and all phantoms, features including Difference Variance, Inverse Different Moment, Fraction, Long Run Emphasis, Run Length Non Uniformity, and Short Run Emphasis were found as highly reproducible features. For change in the position of field size, Fraction was the most reproducible in all field sizes and all phantoms. On the mAs change, we found that feature, Short Run Emphasis field 15 × 15 for small wood phantom, and Correlation in all field sizes for Cotton are the most reproducible features. CONCLUSION: We demonstrated that radiomics features are strongly vulnerable against radiographic field size, positions in the radiation field, mAs, and phantom materials, and reproducibility analyses should be performed before each radiomics study. Moreover, these changing parameters should be considered, and their effects should be minimized in future radiomics studies.

Lung function parameters in patients with gastroesophageal reflux without respiratory symptoms: a case-control study.

AIM: This research aimed to evaluate the effect of gastroesophageal reflux disease (GERD) on pulmonary volumes, airflows, and airway resistance in the patients without respiratory symptoms and compare them with the healthy subjects. BACKGROUND: GERD is the return of gastric content into the esophagus and beyond. GERD may play an essential role in the extraesophageal diseases, including chest pain, asthma, laryngitis, chronic cough, and sinusitis. The relation between GERD and airway involvement in asthma and also bronchoconstrictor effects of GERD are well recognized, but its impact on lung parameters in the patients with GERD without respiratory symptoms is unclear. METHODS: In a case-control study, 78 GERD patients without pulmonary symptoms and 93 healthy subjects as control group were enrolled. The impulse oscillometry examined airway resistance. The body plethysmograph measured the pulmonary volumes and airflows. RESULTS: The mean age of GERD patients and the healthy subjects were 37.30±9.76 and 34.74±11.10, respectively. A total of 53.8% of patients and 67.7% of healthy subjects were male. The lung volumes measured by the body plethysmography were normal in both patients and healthy subjects. However, there was a significant difference between the groups in forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) (P=0.01) and maximal mid expiratory flow (MMEF) (P=0.008). Airway resistance at R5Hz was significantly higher in the case group than the control group (P=0.001). CONCLUSION: The results of the current study demonstrated that GERD patients have small airway disease even in the absence of respiratory symptoms.

Microaneurysm detection in fundus images using a two-step convolutional neural network.

BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the disease, distinguishing this complication within the fundus images facilitates early DR detection. In this paper, an automatic analysis of retinal images using convolutional neural network (CNN) is presented. METHODS: Our method incorporates a novel technique utilizing a two-stage process with two online datasets which results in accurate detection while solving the imbalance data problem and decreasing training time in comparison with previous studies. We have implemented our proposed CNNs using the Keras library. RESULTS: In order to evaluate our proposed method, an experiment was conducted on two standard publicly available datasets, i.e., Retinopathy Online Challenge dataset and E-Ophtha-MA dataset. Our results demonstrated a promising sensitivity value of about 0.8 for an average of >6 false positives per image, which is competitive with state of the art approaches. CONCLUSION: Our method indicates significant improvement in MA-detection using retinal fundus images for monitoring diabetic retinopathy.

Radiomic Feature Robustness and Reproducibility in Quantitative Bone Radiography: A Study on Radiologic Parameter Changes.

The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22%, 34%, and 45% of features were most robust (COV ≤ 5%) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100%) and 76% of which showed large variations (COV > 20%) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis.

Parallel point-multiplication architecture using combined group operations for high-speed cryptographic applications.

In this paper, we propose a novel parallel architecture for fast hardware implementation of elliptic curve point multiplication (ECPM), which is the key operation of an elliptic curve cryptography processor. The point multiplication over binary fields is synthesized on both FPGA and ASIC technology by designing fast elliptic curve group operations in Jacobian projective coordinates. A novel combined point doubling and point addition (PDPA) architecture is proposed for group operations to achieve high speed and low hardware requirements for ECPM. It has been implemented over the binary field which is recommended by the National Institute of Standards and Technology (NIST). The proposed ECPM supports two Koblitz and random curves for the key sizes 233 and 163 bits. For group operations, a finite-field arithmetic operation, e.g. multiplication, is designed on a polynomial basis. The delay of a 233-bit point multiplication is only 3.05 and 3.56 µs, in a Xilinx Virtex-7 FPGA, for Koblitz and random curves, respectively, and 0.81 µs in an ASIC 65-nm technology, which are the fastest hardware implementation results reported in the literature to date. In addition, a 163-bit point multiplication is also implemented in FPGA and ASIC for fair comparison which takes around 0.33 and 0.46 µs, respectively. The area-time product of the proposed point multiplication is very low compared to similar designs. The performance ([Formula: see text]) and Area × Time × Energy (ATE) product of the proposed design are far better than the most significant studies found in the literature.