•Ni nanotubes with different length were grown into the pores of PET templates by electrodeposition method.•Parameters of Ni nanotubes with different length were comprehensively studied.•Correlation ...between morphology, structure and magnetic properties were discussed.•Influence of the growth processes on texture and magnetic anisotropy were demonstrated.
Metallic nanostructures such as nanorods, nanowires, and nanotubes are of great interest from fundamental and applied perspectives. In this work, we studied one-dimensional nickel nanostructures with variable lengths approximately 2–12 μm and wall thickness approximately 70–120 nm grown electrochemically in the pores of PET templates. The synthesis technique and the results of sample characterization including morphological features, texture, crystal structure, and magnetic properties are presented.
Background
Accurate glioma grading plays an important role in patient treatment.
Purpose
To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with ...support vector machine (SVM).
Study Type
Retrospective.
Population
In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007.
Field Strength/Sequence
3.0T MRI/ T1WI, T2 fluid‐attenuated inversion recovery, contrast enhanced T1, arterial spinal labeling, diffusion‐weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2), and dynamic contrast‐enhanced.
Assessment
Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray‐level co‐occurrence matrix (GLCM), gray‐level run‐length matrix (GLRLM), and gray‐level size‐zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF‐SVM) combined with attribute selection using SVM‐recursive feature elimination (SVM‐RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10‐fold crossvalidation. The model performance was further tested using the remaining 20% data.
Statistical Tests
Ten‐fold crossvalidation was used to validate the model performance.
Results
Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM‐RFE was able to reduce attribute redundancy as well as improve RBF‐SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray‐level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM‐RFE to provide comparable classifying efficacy.
Data Conclusion
When using image textures‐based SVM classification of gliomas, the GLSZM model in combination with gray‐level 64 and attribute selection may be an optimized solution.
Level of Evidence: 2
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2019;49:1263–1274.
Purpose
To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma.
Materials and methods
Following IRB ...approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data.
Results
One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 (
p
< 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 (
p
< 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively.
Conclusion
Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.
To assess whether intratumoral heterogeneity measured by (18)F-FDG PET texture analysis has potential as a prognostic imaging biomarker in patients with pancreatic ductal adenocarcinoma (PDAC).
We ...evaluated a cohort of 137 patients with newly diagnosed PDAC who underwent pretreatment (18)F-FDG PET/CT from January 2008 to December 2010. First-order (histogram indices) and higher-order (grey-level run length, difference, size zone matrices) textural features of primary tumours were extracted by PET texture analysis. Conventional PET parameters including metabolic tumour volume (MTV), total lesion glycolysis (TLG), and standardized uptake value (SUV) were also measured. To assess and compare the predictive performance of imaging biomarkers, time-dependent receiver operating characteristic (ROC) curves for censored survival data and areas under the ROC curve (AUC) at 2 years after diagnosis were used. Associations between imaging biomarkers and overall survival were assessed using Cox proportional hazards regression models.
The best imaging biomarker for overall survival prediction was first-order entropy (AUC = 0.720), followed by TLG (AUC = 0.697), MTV (AUC = 0.692), and maximum SUV (AUC = 0.625). After adjusting for age, sex, clinical stage, tumour size and serum CA19-9 level, multivariable Cox analysis demonstrated that higher entropy (hazard ratio, HR, 5.59; P = 0.028) was independently associated with worse survival, whereas TLG (HR 0.98; P = 0.875) was not an independent prognostic factor.
Intratumoral heterogeneity of (18)F-FDG uptake measured by PET texture analysis is an independent predictor of survival along with tumour stage and serum CA19-9 level in patients with PDAC. In addition, first-order entropy as a measure of intratumoral metabolic heterogeneity is a better quantitative imaging biomarker of prognosis than conventional PET parameters.
Purpose
The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate ...the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC.
Methods
By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort.
Results
The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively.
Conclusion
Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.
The constrained groove pressing (CGP) is one of severe plastic deformation (SPD) methods for producing ultra-fine grained sheet metal. The present research work is concerned with CGP process of 2024 ...Al alloy, with a high strength to weight ratio, at 300 °C, and for three cycles. With this regard, several tensile tests were performed to study the mechanical properties of various samples in different directions. By computing the Lankford anisotropy coefficient, normal and planar anisotropy were determined for specimens produced via up to three CGP passes. Moreover, pole figures (PF) and inverse pole figures (IPF) of various samples were prepared for texture analyses and finding the causes of variations in their UTS, elongation and anisotropy parameters. On the basis of texture analysis, it was found that 1-pass CGP could result in the highest normal anisotropy which means the lowest risk of fracture in a subsequent metal forming process such as deep drawing. The highest texture intensity was also owned by the 1-passed specimen. On the other hand, the 2-passed workpiece demonstrated the lowest absolute planar anisotropy value which could be beneficial for an additional forming operation.
•Greatest UTS in various directions in the plane of sheet was observed for the 1-pass CGPed sample.•1-pass CGPed specimen can result in highest normal anisotropy rave, based on texture analysis.•The sheets produced by the first CGP cycle represented a greater formability.•2-passed workpiece showed the lowest anisotropy, beneficial for the subsequent forming operation.
The wavelet transform as an important multiresolution analysis tool has already been commonly applied to texture analysis and classification. Nevertheless, it ignores the structural information while ...capturing the spectral information of the texture image at different scales. In this paper, we propose a texture analysis and classification approach with the linear regression model based on the wavelet transform. This method is motivated by the observation that there exists a distinctive correlation between the sample images, belonging to the same kind of texture, at different frequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlation varies from texture to texture. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Therefore, our method considers not only the frequency regions but also the correlation between these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-structured wavelet transform (TSWT) do not consider the correlation between different frequency regions. Experiments show that our method significantly improves the texture classification rate in comparison with the multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derived from these.
Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in ...renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.
Hyperspectral imaging in the visible and near-infrared (400–1000
nm) regions was tested for nondestructive determination of moisture content (MC), total soluble solids (TSS), and acidity (expressed ...as pH) in strawberry. The spectral data were analyzed using the partial least squares (PLS) analysis, a multivariate calibration technique. The correlation coefficients (
r) with the whole spectral range (400–1000
nm) for predicting MC, TSS, and pH were 0.90, 0.80, and 0.87 with SEC of 6.085, 0.233, and 0.105 and SEP of 3.874, 0.184, and 0.129, respectively. Optimal wavelengths were selected using
β-coefficients from PLS models. Multiple linear regression (MLR) models were established using only the optimal wavelengths to predict the quality attributes. The correlation coefficients (
r) for predicting MC, TSS, and pH using MLR models were 0.87, 0.80, and 0.92 with SEC of 6.72, 0.220, and 0.084 and SEP of 5.786, 0.211, and 0.091, respectively. Moreover, for classifying strawberry based on ripeness stage, a texture analysis was conducted on the images based on grey-level co-occurrence matrix (GLCM). The higher classification accuracy of 89.61% was achieved using the GLCM parameters at horizontal direction at angle of 0°.
Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts ...using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation on pelvic computed tomography and magnetic resonance images.
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