Neighborhood Gray-tone Difference Matrix Research Articles

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging.

The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tone-difference matrix (NGTDM) and run-length matrix (RLM). Receiver-operating-characteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D* (AUC = 0.80) and f (AUC = 0.70) at t1. 3D statistical TA features might be useful as imaging-based markers for characterizing tumor aggressiveness and predicting chemotherapeutic response in patients with osteosarcoma.

Comparative analysis of texture feature extraction techniques for rice grain classification

Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET-A, SET-B and SET-C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co-occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K-nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET-B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

59. A RADIOMICS-BASED MACHINE LEARNING MODEL FOR DISTINGUISHING RADIATION NECROSIS FROM PROGRESSION OF BRAIN METASTASES TREATED WITH STEREOTACTIC RADIOSURGERY (SRS)

PURPOSEThis study aims to test whether MRI radiomic signatures can distinguish radiation necrosis (RN) from tumor progression (TP) in a multi-institution dataset using machine learning.METHODSBrain metastases treated with SRS were followed by serial MRI, and those showing evidence of RN or TP underwent pathologic confirmation. Radiomic features were extracted from T1 post-contrast (T1c) and T2 fluid attenuated inversion recovery (T2 FLAIR) MRI. High dimensional radiomic feature space was visualized in a two-dimensional space using t-distributed stochastic neighbor embedding (t-SNE). Cases from 2 institutions were combined and randomly assigned to training (2/3) and testing (1/3) cohorts. Backward elimination was used for feature selection, followed by random forest algorithm for predictive modeling.RESULTSA total of 135 individual lesions (37 RN and 98 TP) were included. The majority (72.6%) received single-fraction SRS to a median dose of 18Gy. Clear clustering of cases around the institutional origin was observed on t-SNE analysis. 21 T1c and 4 FLAIR features were excluded from subsequent modeling due to significant correlation with the institutional origin. Backward elimination yielded 6 T1c and 6 FLAIR features used for model construction. A random forest model based on the 6 FLAIR features (cluster shade, neighborhood gray tone difference matrix (NGTDM) coarseness, NGTDM texture strength, run length nonuniformity, run percentage, and short run high gray-level emphasis) achieved sensitivity of 76% and specificity of 70% on the training cohort (AUC 0.74, 95% CI 0.60–0.88), and sensitivity of 67% and specificity of 83% on the testing cohort (AUC 0.75, 95% CI 0.59–0.93). Addition of the T1c features resulted in overfitting of the training cohort (AUC 1.00), but did not improve model performance on the testing cohort (AUC 0.69, 95% CI 0.51–0.87).CONCLUSIONMRI radiomics based machine learning can distinguish RN from TP after brain SRS in a heterogeneous image dataset.

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer.

Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients.Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann–Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann–Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features.Results: Of 43 patients, 32 (74.4%) had low TIL levels and 11 (25.6%) had high TIL levels. The histological grade of the low TIL group was higher than that of the high TIL group (p = 0.043). The high TIL group had a more negative threshold Ki-67 level (<14%) than the low TIL group (p = 0.017). The six most important radiomics features [uniformity, variance, grayscale symbiosis matrix (GLCM) correlation, GLCM autocorrelation, gray level difference matrix (GLDM) low gray level emphasis, and neighborhood gray-tone difference matrix (NGTDM) contrast], representing qualitative mammographic image characteristics, were statistically different (p < 0.05) among the low and high TIL groups. Tumors in the high TIL group had a more non-uniform density and a smoother gradient of the tumor pattern than the low TIL group. The changes in Ki-67, age, epidermal growth factor receptor, radiomic characteristics, and Pearson correlation coefficient were statistically significant (p < 0.05).Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.

A multimodal liveness detection using statistical texture features and spatial analysis

Biometric authentication can establish a person’s identity from their exclusive features. In general, biometric authentication can vulnerable to spoofing attacks. Spoofing referred to presentation attack to mislead the biometric sensor. An anti-spoofing method is able to automatically differentiate between real biometric traits presented to the sensor and synthetically produced artifacts containing a biometric trait. There is a great need for a software-based liveness detection method that can classify the fake and real biometric traits. In this paper, we have proposed a liveness detection method using fingerprint and iris. In this method, statistical texture features and spatial analysis of the fingerprint pattern is utilized for fake or real classification. The approach is further improved by fusing iris modality with the fingerprint modality. The standard Haralick’s statistical features based on the gray level co-occurrence matrix (GLCM) and Neighborhood Gray-Tone Difference Matrix (NGTDM) are used to generate a feature vector from the fingerprint. Texture feature from iris is used to boost the performance of the proposed liveness detection method. For the fusion Dempster-Shafer (D-S) approach is used at the decision level. Experiments have been performed on ATVS dataset and LivDet2011 dataset. The results show the convincing and effective outcomes of the proposed method.

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning

Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity include clinical examination, visual inspection, and patient-reported symptoms. Physiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging. Quantitative thermal imaging markers were identified and used in supervised machine learning to develop a predictive model for radiation dermatitis. Ninety patients treated for adjuvant whole-breast RT (4250 cGy/fx = 16) were recruited for the study. Thermal images of the treated breast were taken at 4 intervals: before RT, then weekly at fx = 5, fx = 10, and fx= 15. Parametric thermograms were analyzed and yielded 26 thermal-based features that included surface temperature (°C) and texture parameters obtained from (1) gray-level co-occurrence matrix, (2) gray-level run-length matrix, and (3) neighborhood gray-tone difference matrix. Skin toxicity was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events (CTCAE) guidelines (Ver.5). Binary group classes were labeled according to a CTCAE cut-off score of ≥2, and thermal features obtained at fx = 5 were used for supervised machine learning to predict skin toxicity. The data set was partitioned for model training, independent testing, and validation. Fifteen patients (∼17% of the whole data set) were randomly selected as an unseen test data set, and 75 patients (∼83% of the whole data set) were used for training and validation of the model. A random forest classifier with leave-1-patient-out cross-validation was employed for modeling single and hybrid parameters. The model performance was reported using receiver operating characteristic analysis on patients from an independent test set. Thirty-seven patients presented with adverse skin effects, denoted by a CTCAE score ≥2, and had significantly higher local increases in skin temperature, reaching 36.06°C at fx = 10 (P = .029). However, machine-learning models demonstrated early thermal signals associated with skin toxicity after the fifth RT fraction. The cross-validated model showed high prediction accuracy on the independent test data (test accuracy = 0.87) at fx = 5 for predicting skin toxicity at the end of RT. Early thermal markers after 5 fractions of RT are predictive of radiation-induced skin toxicity in breast RT.

Adenomatous Hyperplasia Of Thyroid Nodule Classification: Texture Feature Analysis Methodoloy Of Ultrasound Images

This paper, explores to extract textural feature from ultrasound Adenomatous Hyperplasia thyroid nodule. The extracted texture feature will help the clinician to improve diagnostic accuracy. The Fine Needle Aspiration (FNA) and Histopathology report is the conventional diagnostic methodology. It is an invasive technique and patients are subjected to painful process. Non-invasive, non-contact and low cost imaging tool is essential to increase the clinical diagnostic accuracy. Ultrasound imaging is a potential non-invasive modality to capture Adenomatous Hyperplasia thyroid nodule. Adenomatous Hyperplasia thyroid nodule is the common inflammation in thyroid gland abnormality. The texture features, using Law’s Texture Energy Measures (Law’s TEM), Neighborhood Gray Tone Difference Matrix (NGTDM) and Statistical Feature Matrix (SFM) are extracted from normal and adenomatous hyperplasia of thyroid nodules. The normal and abnormal images are demarcated using T-test analysis. The extracted feature shows significant difference between normal and abnormal images with p value less than 0.001(p &lt; 0.001). Hence the Law’s Texture Energy based feature may be used to identify the pathology in the thyroid ultrasound images.

Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer.

Background The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature. Purpose To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer. Material and Methods Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV &gt; 5% were normalized by the mean feature variation across the group. Results Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV &gt; 5%), 11, 23, and 40 features demonstrated 5%&lt; CV ≤10%, 10%&lt; CV ≤20%, and CV &gt; 20%, respectively. A subset of features in each category (CV &gt; 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix. Conclusions Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV &lt;5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.

Using neighborhood gray tone difference matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules

ObjectiveLung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone difference matrix (NGTDM) texture features.MethodsRetrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features.ResultsIn the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively.ConclusionCompared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.

Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.

Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUVmean and SUVmax were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.

Multi-view breast thermogram analysis by fusing texture features

ABSTRACTBreast cancer is the prevalent cancer type in women and thermography aids in premature detection by utilising ultra-sensitive infrared cameras. In this work, normal and abnormal thermograms are differentiated by extracting and fusing texture features from frontal and lateral views. Multi-view thermograms are pre-processed using anisotropic diffusion. The Region of Interest from axilla to lower breast boundary is extracted through level-set segmentation without re-initialisation. Texture features such as grey-level co-occurrence matrix, grey-level run length matrix, grey-level size zone matrix and neighbourhood grey tone difference matrix that quantitatively describe local or regional texture properties are extracted for 32-normal and 31-abnormal subjects chosen from DMR database. Using t-test, the reduced feature set is determined for frontal, right-lateral and left-lateral thermograms independently from the extracted texture features. Significant features are obtained by performing kernel principal component analysis on the reduced feature set. Feature fusion is performed on obtained significant features from frontal and lateral views to obtain a composite feature vector that is fed to least square-support vector machine employing optimised hyper-parameters to classify subjects as normal and abnormal. Experimental results indicate that fusion of texture features from frontal and lateral thermograms achieved 96% accuracy, 100% sensitivity and 92% specificity.

308. Evaluation of the stability and discriminative power of PET radiomic features in phantom studies

Purpose PET radiomics is a promising and emerging field with large potential for tumor phenotype characterization. Radiomic Metrics (RM) analysis, nonetheless, is not yet standardized and the impact of the whole processing chain on RM hast not yet been clearly understood. In this work we characterized the impact of the following major issues on RM: (i) four different discretization methods, (ii) acquisition duration, (iii) reconstruction algorithm and iv) image filtering. The influence of the region volume and Standardized-Uptake-Value (SUV) on the RM was also measured. Methods and materials The CGITA software v1.4 was used to estimate 42 RM, from an uniform phantom and a purpose built “heterogeneous” one. Statistical effect size indicators were used to determine for each RM: (i) the impact of each nuisance factor, and (ii) the discriminating power. Results The discretization procedure performed at the beginning of RM quantification is critical with respect to the dependency of RM values on lesion volume and SUV. The method based on relative range had overall better proprieties than those based on bins of fixed width in units of SUV. Based on the robustness with respect to all factors, we categorized RM based on the influence of the imaging workflow and on the ability to better discriminate heterogeneous PET patterns. The families of RM less robust resulted to be the Gray Level Run Length Matrix (GLRLM) and the Neighborhood gray tone difference matrix (NGTD) ones while the Gray Level Co-occurrence Matrix (GLCM) group as well as the SUV first order RM were the most suitable. Conclusion From the experiments performed in this work, processing pipeline recommendations were provided, together with a list of 20 sufficiently robust and discriminative RMs for PET radiomic analysis. This work was supported by AIRC IG18965.

Effect of tube current on computed tomography radiomic features

Variability in the x-ray tube current used in computed tomography may affect quantitative features extracted from the images. To investigate these effects, we scanned the Credence Cartridge Radiomics phantom 12 times, varying the tube current from 25 to 300 mA∙s while keeping the other acquisition parameters constant. For each of the scans, we extracted 48 radiomic features from the categories of intensity histogram (n = 10), gray-level run length matrix (n = 11), gray-level co-occurrence matrix (n = 22), and neighborhood gray tone difference matrix (n = 5). To gauge the size of the tube current effects, we scaled the features by the coefficient of variation of the corresponding features extracted from images of non-small cell lung cancer tumors. Variations in the tube current had more effect on features extracted from homogeneous materials (acrylic, sycamore wood) than from materials with more tissue-like textures (cork, rubber particles). Thirty-eight of the 48 features extracted from acrylic were affected by current reductions compared with only 2 of the 48 features extracted from rubber particles. These results indicate that variable x-ray tube current is unlikely to have a large effect on radiomic features extracted from computed tomography images of textured objects such as tumors.

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Breast cancer is the leading cancer in women worldwide. Early detection can reduce the mortality rate of breast cancer. Breast thermography is a non-invasive and simple imaging technique used for early detection of breast cancer. Feature extraction and selection of appropriate features play a major role in computer-aided detection of breast cancer using breast thermograms. In this article, texture features are extracted from automatically segmented breast thermograms by computing neighbourhood grey tone difference matrix (NGTDM) and run length matrix (RLM). Significance of these features in differentiating the abnormal breast from the normal breast is found by statistical test. NGTDM extracted coarseness, busyness, complexity, strength and RLM extracted long run emphasis and run percentage are found to be significant by statistical test. Extracted features are computationally less expensive and attained an average accuracy of 80%, sensitivity of 94% and specificity of 71.4% using back propagation neural network classifier.

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Primary Rectal Cancer: Repeatability of Global and Local-Regional MR Imaging Texture Features.

PurposeTo assess the day-to-day repeatability of global and local-regional magnetic resonance (MR) imaging texture features derived from primary rectal cancer.Materials and MethodsAfter ethical approval and patient informed consent were obtained, two pretreatment T2-weighted axial MR imaging studies performed prospectively with the same imaging unit on 2 consecutive days in 14 patients with rectal cancer (11 men [mean age, 61.7 years], three women [mean age, 70.0 years]) were analyzed to extract (a) global first-order statistical histogram and model-based fractal features reflecting the whole-tumor voxel intensity histogram distribution and repeating patterns, respectively, without spatial information and (b) local-regional second-order and high-order statistical texture features reflecting the intensity and spatial interrelationships between adjacent in-plane or multiplanar voxels or regions, respectively. Repeatability was assessed for 46 texture features, and mean difference, 95% limits of agreement, within-subject coefficient of variation (wCV), and repeatability coefficient (r) were recorded.ResultsRepeatability was better for global parameters than for most local-regional parameters. In particular, histogram mean, median, and entropy, fractal dimension mean and standard deviation, and second-order entropy, homogeneity, difference entropy, and inverse difference moment demonstrated good repeatability, with narrow limits of agreement and wCVs of 10% or lower. Repeatability was poorest for the following high-order gray-level run-length (GLRL) gray-level zone size matrix (GLZSM) and neighborhood gray-tone difference matrix (NGTDM) parameters: GLRL intensity variability, GLZSM short-zone emphasis, GLZSM intensity nonuniformity, GLZSM intensity variability, GLZSM size zone variability, and NGTDM complexity, demonstrating wider agreement limits and wCVs of 50% or greater.ConclusionMR imaging repeatability is better for global texture parameters than for local-regional texture parameters, indicating that global texture parameters should be sufficiently robust for clinical practice.Online supplemental material is available for this article.

Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer

Background: Radiomics features have been used in a variety of studies to predict patient outcomes or aid in the diagnosis of non-small cell lung cancer. However, no guidelines exist for the best way to calculate these features to maximize their prognostic potential. The purpose of the current study was to evaluate how different image pre-processing techniques may impact both the volume dependence and prognostic potential of the features in univariate analyses. Methods: Radiomics features from the histogram, co-occurrence matrix, neighborhood gray-tone difference matrix and run-length matrix were calculated from a set of computed tomography (CT) images of 107 non-small cell lung cancer tumors with volumes ranging from 5 to 567 cm3. Features were calculated from the images with no pre-processing, 8 bit depth resampling, Butterworth smoothing, or both 8 bit depth resampling and Butterworth smoothing. To determine which features were correlated with volume, we calculated the Spearman rank correlation coefficient (rs) for each feature and preprocessing combination. For features that had very high volume correlations (rs >0.95) regardless of which preprocessing algorithm was used, we normalized the algorithm for volume and recalculated the volume correlation. To determine whether the preprocessing technique affected the usefulness of the feature, we fitted univariate Cox proportional hazards models for all four preprocessing techniques for each feature and calculated the P value. Additionally, univariate cox models were recalculated using leave-one-out cross validation to generate risk predictions for each patient. As a result, each patient had a predicted outcome for each model in which the patient was not involved in the model building. The prediction accuracy was assessed using Harrell’s concordance index (c-index). Finally, the ability of each feature to improve model fit was examined using the P value of the log-likelihood ratio between a model built using volume only and a model built using volume and one radiomics feature. The Benjamini-Hochberg procedure was used for multiplicity correction. Results: Five features were entirely volume dependent (busyness, coarseness, grey-level non-uniformity, run-length non-uniformity, and energy) and new algorithms were proposed for these features. Both the correlation with volume and the prognostic value of individual features changed substantially with different preprocessing techniques. In general, preprocessed features that were at least slightly correlated with volume (rs >0.5) were more likely to be significant in the univariate analysis. Additionally, Butterworth smoothing, used either alone or in conjunction with 8 bit depth resampling, most often yielded features that were significant in univariate analysis. Conclusions: Preprocessing can have a strong impact on the volume dependence of a feature, and its significance in univariate models. To create standardized features useful for multivariate modeling, it will be important to balance the usefulness of features with their volume dependence.

Texture based classification of the severity of mitral regurgitation

Clinically, the severity of valvular regurgitation is assessed by manual tracing of the regurgitant jet in the respective chambers. This work presents a computer-aided diagnostic (CAD) system for the assessment of the severity of mitral regurgitation (MR) based on image processing that does not require the intervention of the radiologist or clinician. Eight different texture feature sets from the regurgitant area (selected through an arbitrary criterion) have been used in the present approach. First order statistics have been used initially, however, observing their limitations, the other texture features such as spatial gray level difference matrix, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws’ textures energy measure, fractal dimension texture analysis and Fourier power spectrum have additionally been used. For the classification task a supervised classifier i.e., support vector machine has been used in the present approach. The classification accuracy has been improved significantly by using these texture features in combination, in comparison to when fed individually as input to the classifier. The classification accuracy of 95.65±1.09, 95.65±1.09 and 95.36±1.13 has been obtained in apical two chamber, apical four chamber and parasternal long axis views, respectively. Therefore, the results of this paper indicate that the proposed CAD system may effectively assist the radiologists in establishing (confirming) the MR stages, namely, mild, moderate and severe.

Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT.

We aimed to explore the ability of textural heterogeneity indices determined by (18)F-FDG PET/CT for grading the malignancy of thymic epithelial tumors (TETs). We retrospectively enrolled 47 patients with pathologically proven TETs who underwent pre-treatment (18)F-FDG PET/CT. TETs were classified by pathological results into three subgroups with increasing grades of malignancy: low-risk thymoma (LRT; WHO classification A, AB and B1), high-risk thymoma (B2 and B3), and thymic carcinoma (TC). Using (18)F-FDG PET/CT, we obtained conventional imaging indices including SUVmax and 20 intratumoral heterogeneity indices: i.e., four local-scale indices derived from the neighborhood gray-tone difference matrix (NGTDM), eight regional-scale indices from the gray-level run-length matrix (GLRLM), and eight regional-scale indices from the gray-level size zone matrix (GLSZM). Area under the receiver operating characteristic curve (AUC) was used to demonstrate the abilities of the imaging indices for differentiating subgroups. Multivariable logistic regression analysis was performed to show the independent significance of the textural indices. Combined criteria using optimal cutoff values of the SUVmax and a best-performing heterogeneity index were applied to investigate whether they improved differentiation between the subgroups. Most of the GLRLM and GLSZM indices and the SUVmax showed good or fair discrimination (AUC >0.7) with best performance for some of the GLRLM indices and the SUVmax, whereas the NGTDM indices showed relatively inferior performance. The discriminative ability of some of the GLSZM indices was independent from that of SUVmax in multivariate analysis. Combined use of the SUVmax and a GLSZM index improved positive predictive values for LRT and TC. Texture analysis of (18)F-FDG PET/CT scans has the potential to differentiate between TET tumor grades; regional-scale indices from GLRLM and GLSZM perform better than local-scale indices from the NGTDM. The SUVmax and heterogeneity indices may have complementary value in differentiating TET subgroups.

Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors

PurposeTo assess the uncertainty of quantitative imaging features extracted from contrast-enhanced computed tomography (CT) scans of lung cancer patients in terms of the dependency on the time after contrast injection and the feature reproducibility between scans. MethodsEight patients underwent contrast-enhanced CT scans of lung tumors on two sessions 2–7 days apart. Each session included 6 CT scans of the same anatomy taken every 15s, starting 50s after contrast injection. Image features based on intensity histogram, co-occurrence matrix, neighborhood gray-tone difference matrix, run-length matrix, and geometric shape were extracted from the tumor for each scan. Spearman's correlation was used to examine the dependency of features on the time after contrast injection, with values over 0.50 considered time-dependent. Concordance correlation coefficients were calculated to examine the reproducibility of each feature between times of scans after contrast injection and between scanning sessions, with values greater than 0.90 considered reproducible. ResultsThe features were found to have little dependency on the time between the contrast injection and the CT scan. Most features were reproducible between times of scans after contrast injection and between scanning sessions. Some features were more reproducible when they were extracted from a CT scan performed at a longer time after contrast injection. ConclusionThe quantitative imaging features tested here are mostly reproducible and show little dependency on the time after contrast injection.