scholarly journals Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margherita Mottola ◽  
Stephan Ursprung ◽  
Leonardo Rundo ◽  
Lorena Escudero Sanchez ◽  
Tobias Klatte ◽  
...  
Keyword(s):  
Quantitative Imaging ◽  
Texture Features ◽  
Voxel Size ◽  
Morphological Evaluation ◽  
Resampling Procedure ◽  
Automatic Methods ◽  
Visual Assessments ◽  
2D And 3D ◽  
Slice Spacing ◽  
Original Information

AbstractComputed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.

scholarly journals Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma

2019 ◽  
Vol 18 ◽  
pp. 153601211988316 ◽  
Author(s):  
Guangjie Yang ◽  
Aidi Gong ◽  
Pei Nie ◽  
Lei Yan ◽  
Wenjie Miao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Texture Features ◽  
Regions Of Interest ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
2D And 3D

Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration ( P > .05). There was no significant difference in AUC between the 2 models ( P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

2020 ◽  
Author(s):  
Marta Ligero ◽  
Olivia Jordi-Ollero ◽  
Kinga Bernatowicz ◽  
Alonso Garcia-Ruiz ◽  
Eric Delgado-Muñoz ◽  
...  
Keyword(s):  
Liver Metastases ◽  
Large Scale ◽  
Batch Effect ◽  
Slice Thickness ◽  
Convolution Kernel ◽  
Voxel Size ◽  
Large Scale Data ◽  
Before And After ◽  
Slice Spacing ◽  
Scale Data

Abstract Objective To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications. Methods CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction. Results Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness–kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20). Conclusion CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy. Key Points • The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

scholarly journals Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6199
Author(s):  
Chidozie N. Ogbonnaya ◽  
Xinyu Zhang ◽  
Basim S. O. Alsaedi ◽  
Norman Pratt ◽  
Yilong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
Spatial Relationship ◽  
Region Of Interest ◽  
Texture Features ◽  
Gray Level ◽  
Discriminative Ability ◽  
Significant Difference ◽  
Clinically Significant ◽  
Occurrence Matrix

Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

scholarly journals Comparing Two Different Automatic Methods to Measure Femoral Neck-Shaft Angle Based on PointNet++ Network

2021 ◽  
Author(s):  
Zhe Li ◽  
Jiayu Yang ◽  
Xinghua Li ◽  
Kunzheng Wang ◽  
Jungang Han ◽  
...  
Keyword(s):  
Femoral Neck ◽  
Femoral Shaft ◽  
Femoral Neck Axis ◽  
Neck Shaft Angle ◽  
3D Space ◽  
Significant Difference ◽  
Shaft Angle ◽  
Automatic Methods ◽  
3D Methods ◽  
2D And 3D

Abstract Bacnground: Accurate measurement of the femoral neck-shaft angle (NSA) is of great significance for diagnosing hip joint diseases and preoperative planning of total hip arthroplasty. However, the fitting lines of the femoral neck and femoral shaft did not always intersect in 3D space. Thus, it is unclear whether there is a difference between 2D and 3D methods for measuring NSA. Methods: The femoral point cloud datasets from 310 subjects were segmented into three regions, including the femoral head, femoral neck, and femoral shaft using PointNet++. We created a projection plane to simulate the hip anteroposterior radiograph and fitted the femoral neck axis and femoral shaft axis to complete the 2D measurement, while we directly fitted the two axes in space to complete the 3D measurement. Also, we conducted the manual measurement of the NSA. We verified the accuracy of the segmentation and compared the results of the two automatic and manual methods. Results: The Dice coefficient of femoral segmentation reached 0.9746, and MIoU of that was 0.9165. No significant difference was found between any two of the three methods. While comparing the 2D and 3D methods, the average accuracy was 98.00%, and the average error was 2.58°. Conclusion: This paper proposed two accurate and automatic methods to measure the NSA based on a 2D plane and a 3D model respectively. Although the femoral neck and femoral shaft axes did not intersect in 3D space, the NSAs obtained by 2D and 3D methods were basically consistent.

scholarly journals CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues

2019 ◽  
Author(s):  
Caleb R Stoltzfus ◽  
Jakub Filipek ◽  
Benjamin H Gern ◽  
Brandy E Olin ◽  
Joseph M Leal ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Quantitative Imaging ◽  
Myeloid Cell ◽  
Tissue Level ◽  
Innate Immune ◽  
Lymphoid Tissues ◽  
Tissue Architecture ◽  
Tissue Organization ◽  
Cell Organization ◽  
2D And 3D

ABSTRACTRecently developed approaches for highly-multiplexed 2-dimensional (2D) and 3D imaging have revealed complex patterns of cellular positioning and cell-cell interactions with important roles in both cellular and tissue level physiology. However, robust and accessible tools to quantitatively study cellular patterning and tissue architecture are currently lacking. Here, we developed a spatial analysis toolbox, Histo-Cytometric Multidimensional Analysis Pipeline (CytoMAP), which incorporates neural network based data clustering, positional correlation, dimensionality reduction, and 2D/3D region reconstruction to identify localized cellular networks and reveal fundamental features of tissue organization. We apply CytoMAP to study the microanatomy of innate immune subsets in murine lymph nodes (LNs) and reveal mutually exclusive segregation of migratory dendritic cells (DCs), regionalized compartmentalization of SIRPa− dermal DCs, as well as preferential association of resident DCs with select LN vasculature. These studies provide new insights into the organization of myeloid cells in LNs, and demonstrate that CytoMAP is a comprehensive analytics toolbox for revealing fundamental features of tissue organization in quantitative imaging datasets.

Quantitative imaging and correction for cascade gamma radiation of76Br with 2D and 3D PET

2002 ◽  
Vol 47 (19) ◽  
pp. 3519-3534 ◽  
Author(s):  
Mark Lubberink ◽  
Harald Schneider ◽  
Mats Bergstr m ◽  
Hans Lundqvist
Keyword(s):  
Gamma Radiation ◽  
Quantitative Imaging ◽  
2D And 3D ◽  
3D Pet

scholarly journals Towards Texture Accurate Slice Interpolation of Medical Images Using PixelMiner

2021 ◽  
Author(s):  
William Roger ◽  
Philippe Lambin ◽  
Simon Keek ◽  
Manon Beuque ◽  
Sergey Primakov ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Quantitative Imaging ◽  
Texture Features ◽  
Qualitative Assessment ◽  
Imaging Features ◽  
Edge Preservation ◽  
Human Trial ◽  
Analysis Models ◽  
Deep Learning Model ◽  
Lung Ct

Abstract Quantitative analysis models are used for various medical imaging tasks such as registration, classification, object detection, and segmentation which all benefit from high-quality imaging. We propose PixelMiner, a convolution-based deep-learning model which interpolates computed tomography (CT) imaging slices while better preserving quantitative imaging features than standard interpolation methods. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy using a novel architecture. PixelMiner was trained on a large dataset consisting of 7092 lung CT scans and validated using external lung CT datasets. We demonstrated the model's effectiveness by using edge preservation ratio (EPR), compared texture features commonly used in radiomics, and performed a qualitative assessment by human trial. PixelMiner had the highest EPR 82% (p<.01) of the time compared to the closest competing method. It had the lowest texture error, using a normalized root mean squared error (NRMSE) of 0.11 (p<.01), with the highest reproducibility of concordance correlation coefficient (CCC) ≥ 0.85 (p<.01). PixelMiner was chosen 72% of the time by human evaluation (p<.01). PixelMiner was not only demonstrated quantitatively to have improved structural and textural constructions but also shown to be preferable qualitatively.

scholarly journals Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach

2021 ◽  
Vol 11 ◽  
Author(s):  
Antonino Guerrisi ◽  
Michelangelo Russillo ◽  
Emiliano Loi ◽  
Balaji Ganeshan ◽  
Sara Ungania ◽  
...  
Keyword(s):  
Pilot Study ◽  
Metastatic Melanoma ◽  
Texture Analysis ◽  
Quantitative Imaging ◽  
Texture Feature ◽  
Texture Features ◽  
Progression Free Survival ◽  
Imaging Biomarkers ◽  
Contrast Enhanced Ct ◽  
Medium Texture

In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient’s therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.

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