Quantitative Evaluation for Differentiating Malignant and Benign Thyroid Nodules Using Histogram Analysis of Grayscale Sonograms.

OBJECTIVES: To evaluate the diagnostic value of histogram analysis using grayscale sonograms for differentiation of malignant and benign thyroid nodules. METHODS: From July 2013 through October 2013, 579 nodules in 563 patients who had undergone ultrasound-guided fine-needle aspiration were included. For the grayscale histogram analysis, pixel echogenicity values in regions of interest were measured as 0 to 255 (0, black; 255, white) with in-house software. Five parameters (mean, skewness, kurtosis, standard deviation, and entropy) were obtained for each thyroid nodule. With principal component analysis, an index was derived. Diagnostic performance rates for the 5 histogram parameters and the principal component analysis index were calculated. RESULTS: A total of 563 patients were included in the study (mean age ± SD, 50.3 ± 12.3 years;range, 15-79 years). Of the 579 nodules, 431 were benign, and 148 were malignant. Among the 5 parameters and the principal component analysis index, the standard deviation (75.546 ± 14.153 versus 62.761 ± 16.01; P < .001), kurtosis (3.898 ± 2.652 versus 6.251 ± 9.102; P < .001), entropy (0.16 ± 0.135 versus 0.239 ± 0.185; P < .001), and principal component analysis index (-0.386±0.774 versus 0.134 ± 0.889; P < .001) were significantly different between the malignant and benign nodules. With the calculated cutoff values, the areas under the curve were 0.681 (95% confidence interval, 0.643-0.721) for standard deviation, 0.661 (0.620-0.703) for principal component analysis index, 0.651 (0.607-0.691) for kurtosis, 0.638 (0.596-0.681) for entropy, and 0.606 (0.563-0.647) for skewness. The subjective analysis of grayscale sonograms by radiologists alone showed an area under the curve of 0.861 (0.833-0.888). CONCLUSIONS: Grayscale histogram analysis was feasible for differentiating malignant and benign thyroid nodules but did not show better diagnostic performance than subjective analysis performed by radiologists. Further technical advances will be needed to objectify interpretations of thyroid grayscale sonograms.

Evaluation of Underlying Lymphocytic Thyroiditis With Histogram Analysis Using Grayscale Ultrasound Images.

OBJECTIVES: The purpose of this study was to evaluate diagnostic performance of histogram analysis using grayscale ultrasound (US) images in the diagnosis of lymphocytic thyroiditis. METHODS: Three radiologists reviewed a total of 505 US images and classified the images according to the presence/existence of lymphocytic thyroiditis. After 2 months, each reviewer repeated the process with the same 505 images in a randomly mixed order. The intraobserver and interobserver variability was analyzed with a generalized κ value. Four histogram parameters (mean value, standard deviation, skewness, and kurtosis) were obtained, and an index was calculated from principal component analysis. Diagnostic performances were compared. RESULTS: Of 505 patients, 125 (24.8%) had lymphocytic thyroiditis, and 380 (75.2%) had normal thyroid parenchyma on pathologic analysis. The κ value for intraobserver variance ranged from -0.002 to 0.781, and the overall κ values for interobserver variance were 0.570 and 0.214 in the first and second tests, respectively. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the 3 reviewers versus the principal component analysis index were 28.0% to 83.2%, 43.7% to 82.6%, 53.5% to 79.0%, 24.6% to 56.2%, and 75.2% to 88.9% versus 58.4%, 72.4%, 68.9%, 41.0%, and 84.1%. CONCLUSIONS: Histogram analysis of grayscale US images provided confirmable and quantitative information about lymphocytic thyroiditis and was comparable with performers' assessments in diagnostic performance.

Genome-wide SNP identification and QTL mapping for black rot resistance in cabbage.

BACKGROUND: Black rot is a destructive bacterial disease causing large yield and quality losses in Brassica oleracea. To detect quantitative trait loci (QTL) for black rot resistance, we performed whole-genome resequencing of two cabbage parental lines and genome-wide SNP identification using the recently published B. oleracea genome sequences as reference. RESULTS: Approximately 11.5 Gb of sequencing data was produced from each parental line. Reference genome-guided mapping and SNP calling revealed 674,521 SNPs between the two cabbage lines, with an average of one SNP per 662.5 bp. Among 167 dCAPS markers derived from candidate SNPs, 117 (70.1%) were validated as bona fide SNPs showing polymorphism between the parental lines. We then improved the resolution of a previous genetic map by adding 103 markers including 87 SNP-based dCAPS markers. The new map composed of 368 markers and covers 1467.3 cM with an average interval of 3.88 cM between adjacent markers. We evaluated black rot resistance in the mapping population in three independent inoculation tests using F2:3 progenies and identified one major QTL and three minor QTLs. CONCLUSION: We report successful utilization of whole-genome resequencing for large-scale SNP identification and development of molecular markers for genetic map construction. In addition, we identified novel QTLs for black rot resistance. The high-density genetic map will promote QTL analysis for other important agricultural traits and marker-assisted breeding of B. oleracea.

Estimation of Bone Mineral Density in the Femoral Neck and Lumbar Spine using Texture Analysis of Chest and Pelvis Computed Tomography Hounsfield Unit.

OBJECTIVE: This study aimed to establish an academic basis for using a computed tomography (CT) model for predicting osteoporosis in the clinical setting by illustrating the effectiveness of morphometric texture analysis. We introduce texture analysis and quantitative approaches using CT Hounsfield units (HU) to screen osteoporosis. METHODS: From March 6th, 2013, to August 11th, 2020, a total of 4,333 cases (1,766 patients) were included in the study. After applying exclusion criteria concerning the patient status and scan interval between CT and DXA, we selected only 1,647 samples (736 patients) and analyzed both their CT and DXA bone mineral density (BMD) results. BMD was measured in the femoral neck and L1 spine body. A region of interest (ROI) was extracted from each patient's CT as the maximum trabecular area of the L1 spine body and femoral neck. A total of 45 texture features were extracted from every ROI using gray-level co-occurrence matrices. Machine-learning techniques, including linear regression (LR) and artificial neural network (ANN), were applied to predict BMD. RESULTS: We assigned samples to (1) Set 1 (857 lumbar spine samples in chest model, L1 spine DXA BMD), (2) Set 2 (392 lumbar spine samples in lumbar spine CT model, L1 spine DXA BMD), (3) Set 3 (1,249 lumbar spine samples in both chest and lumbar spine CT model, L1 spine DXA BMD), (4) Set 4 (398 femoral neck samples in hip and pelvis CT model, femoral neck DXA BMD), and (5) Set 5 (a total of 1,647 samples). When we applied LR, the correlation coefficients between estimated and reference values for Sets 1, 2, 3, and 4 were 0.783, 0.784, 0.757, and 0.652, respectively. For total samples (Set 5), LR and ANN provided correlation coefficients of 0.707 and 0.782, respectively. CONCLUSION: The modality using morphometric texture analysis with CT HU can be an additional diagnostic tool for osteoporosis and an alternative for DXA.

Prediction of bone mineral density in CT using deep learning with explainability.

Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score = - 1.0 ), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.

Radial k-t FOCUSS for high-resolution cardiac cine MRI.

A compressed sensing dynamic MR technique called k-t FOCUSS (k-t FOCal Underdetermined System Solver) has been recently proposed. It outperforms the conventional k-t BLAST/SENSE (Broad-use Linear Acquisition Speed-up Technique/SENSitivity Encoding) technique by exploiting the sparsity of x-f signals. This paper applies this idea to radial trajectories for high-resolution cardiac cine imaging. Radial trajectories are more suitable for high-resolution dynamic MRI than Cartesian trajectories since there is smaller tradeoff between spatial resolution and number of views if streaking artifacts due to limited views can be resolved. As shown for Cartesian trajectories, k-t FOCUSS algorithm efficiently removes artifacts while preserving high temporal resolution. k-t FOCUSS algorithm applied to radial trajectories is expected to enhance dynamic MRI quality. Rather than using an explicit gridding method, which transforms radial k-space sampling data to Cartesian grid prior to applying k-t FOCUSS algorithms, we use implicit gridding during FOCUSS iterations to prevent k-space sampling errors from being propagated. In addition, motion estimation and motion compensation after the first FOCUSS iteration were used to further sparsify the residual image. By applying an additional k-t FOCUSS step to the residual image, improved resolution was achieved. In vivo experimental results show that this new method can provide high spatiotemporal resolution even from a very limited radial data set.

How does open innovation lead competitive advantage? A dynamic capability view perspective.

The relationship between open innovation and company's competitive advantage, and organisational capabilities required remains to be explained. This study was conducted to answer the following questions. Does open innovation create organisation's competitive advantage? What types of capabilities are needed in the process of open innovation reaching competitive advantage, and what kind of relationship do they have? This study extends the scope of theoretical discussion about open innovation from the point of dynamic capability view. The results confirmed the statistical significance of the path linking open innovation to competitive advantage through product innovation. From the viewpoint of capabilities, transforming capability plays a role of significant prerequisite of sensing capability and seizing capability, having a direct or indirect significant effect on product innovation performance and competitive advantage sequentially. The results suggest that the linkages between the needed capabilities of organisation must be considered for performing open innovation to secure competitive advantage.

Does External Knowledge Sourcing Enhance Market Performance? Evidence from the Korean Manufacturing Industry.

Firms continuously search for external knowledge that can contribute to product innovation, which may ultimately increase market performance. The relationship between external knowledge sourcing and market performance is not well-documented. The extant literature primarily examines the causal relationship between external knowledge sources and product innovation performance or to identify factors which moderates the relationship between external knowledge sourcing and product innovation. Non-technological innovations, such as organization and marketing innovations, intervene in the process of external knowledge sourcing to product innovation to market performance but has not been extensively examined. This study addresses two research questions: does external knowledge sourcing lead to market performance and how does external knowledge sourcing interact with a firm's different innovation activities to enhance market performance. This study proposes a comprehensive model to capture the causal mechanism from external knowledge sourcing to market performance. The research model was tested using survey data from manufacturing firms in South Korea and the results demonstrate a strong statistical relationship in the path of external knowledge sourcing (EKS) to product innovation performance (PIP) to market performance (MP). Organizational innovation is an antecedent to EKS while marketing innovation is a consequence of EKS, which significantly influences PIP and MP. The results imply that any potential EKS effort should also consider organizational innovations which may ultimately enhance market performance. Theoretical and practical implications are discussed as well as concluding remarks.

Histogram and gray level co-occurrence matrix on gray-scale ultrasound images for diagnosing lymphocytic thyroiditis.

The objective of the study was to evaluate whether texture analysis using histogram and gray level co-occurrence matrix (GLCM) parameters can help clinicians diagnose lymphocytic thyroiditis (LT) and differentiate LT according to pathologic grade. The background thyroid pathology of 441 patients was classified into no evidence of LT, chronic LT (CLT), and Hashimoto's thyroiditis (HT). Histogram and GLCM parameters were extracted from the regions of interest on ultrasound. The diagnostic performances of the parameters for diagnosing and differentiating LT were calculated. Of the histogram and GLCM parameters, the mean on histogram had the highest Az (0.63) and VUS (0.303). As the degrees of LT increased, the mean decreased and the standard deviation and entropy increased. The mean on histogram from gray-scale ultrasound showed the best diagnostic performance as a single parameter in differentiating LT according to pathologic grade as well as in diagnosing LT.

Application of RAPD and SCAR markers for purity testing of F1 hybrid seed in chili pepper (Capsicum annuum).

A simpler and better method for purity testing of hybrid pepper seed was developed. The simplest method for extracting genomic DNA, the NaOH method, was chosen. Two RAPD markers identifying male and female parents were also developed, and the PCR products of male- and female-specific RAPD markers were cloned and sequenced. From these sequences, new longer primers were constructed for conversion into SCAR markers. In blind tests the RAPD and SCAR markers were able to reliably detect contaminating exotic seeds. These PCR-based markers are therefore directly applicable for purity testing by seed companies. In addition, the PCR products of the SCAR markers could be identified by direct staining methods such as ethidium bromide and pellet painting without electrophoresis.

Ex vivo estimation of photoacoustic imaging for detecting thyroid microcalcifications.

BACKGROUND: The aim of this study was to evaluate the diagnostic utility of PAI at detecting thyroid microcalcifications at 700 nm laser wavelengths. METHODS: This study included 36 resected samples in 18 patients. To evaluate the PA manifestation of microcalcifications in PAI, gray level histogram and co-occurrence matrix (COM) texture parameters were extracted from the 3 fixed ROI US and PA images, respectively, per sample. We compared the textural parameters obtained from specimen PAIs between samples with punctate microcalcifications on specimen radiography and those without microcalcifications. RESULTS: On specimen US, the mean value (2748.4 ± 862.5) of samples with microcalcifications on specimen radiography was higher than that (1961.9 ± 780.2) of those without microcalcifications (P = 0.007). However, there were no significant differences in textural parameters obtained from specimen PAIs between samples with punctate microcalcifications on specimen radiography and those without when applying both the mean value of the three slices of thyroid specimens and the value of the thyroid specimen slice which had the highest value of the mean values in specimen US. CONCLUSION: PAI did not show significant PA contrast on thyroid microcalcifications indicating that the experimental setup and protocols should be enhanced, e.g., method of complete blood rejection from ex vivo specimens, the multi-wavelength spectroscopic PA imaging method which can solely extract the PA signal from microcalcifications even with high spectral interferences, or PA imaging with narrower slice thickness using 2-dimensional array transducer, etc.

Real-time elastography in the evaluation of diffuse thyroid disease: a study based on elastography histogram parameters.

The purpose of this study was to evaluate the diagnostic performance of quantitative histogram parameters using real-time tissue elastography (RTE) in the diagnosis of patients with diffuse thyroid disease. One hundred and sixteen patients (mean age, 43.7 ± 10.97 y) who had undergone pre-operative staging ultrasonography and RTE were included. For each patient, 11 parameters were obtained from RTE images, from which the "elastic index" was calculated. Diagnostic performance of the elastic index and that of the 11 parameters on RTE were calculated and compared. Of the 116 patients, 31 had diffuse thyroid disease and 85 had normal thyroid parenchyma. Area under the receiver operating characteristic curve (A(z)) of MEAN (average relative value) elasticity was high (0.737), without significant differences from other elasticity values. Diagnostic performance of the elastic index was higher than the MEAN, A(z) = 0.753, without significance (p = 0.802). In conclusion, RTE using the elastic index was found to have good diagnostic performance and may be useful in the diagnosis and management of patients with diffuse thyroid disease.