Magnetic Resonance Imaging Texture Research Articles

Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients

The purpose of the study is to evaluate the associations between intratumoral or peritumoral textural features derived from pretreatment magnetic resonance imaging (MRI) and recurrence-free survival (RFS) in triple-negative breast cancer (TNBC) patients. Forty-three patients with TNBC who underwent preoperative MRI between February 2008 and March 2014 were included. We performed two-dimensional texture analysis on the intratumoral or peritumoral region of interest (ROI) on axial of T2-weighted image (T2WI), dynamic contrast-enhanced (DCE)-MRI and DCE-MRI subtraction images. We also analyzed histopathological data. Cox proportional hazards models were used to investigate associations with survival outcomes. Twelve of the 43 patients (27.9%) had recurrence disease, at a median of 32.5months follow-up (1.4-61.5months). In univariate analysis, nine texture features in T2WI and DCE-MRI subtraction images were significantly associated with RFS. In multivariate analysis, intratumoral difference entropy in DCE-MRI subtraction images in the initial phase (hazard ratio 11.71; 95% confidence interval (CI) [1.41, 97.00]; p value 0.023) and, peritumoral difference variance in DCE-MRI subtraction images in the delayed phase (hazard ratio 9.60; 95% CI [1.98, 46.51]; p value 0.005), were both independently associated with RFS. Moreover, multivariate analysis revealed the presence of lymphovascular invasion as independently associated with RFS (hazard ratio 8.13; 95% CI [2.16, 30.30]; p value 0.002). At pretreatment MRI, an intratumoral and peritumoral quantitative approach using texture analysis has the potential to serve as a prognostic marker in patients with TNBC.

Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy.

To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy. A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test. Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation. Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.

Risk Stratification of Ductal Carcinoma In Situ and Texture Analysis of Contrast-Enhanced Breast Magnetic Resonance Imaging.

The aim of this study was to investigate whether texture analysis of contrast-enhanced T1 weighted images could predict risk of ductal carcinoma in situ (DCIS). The study included 185 DCIS lesions that were classified as either low risk or non-low risk using surgical pathology records. All magnetic resonance imaging texture analyses were performed using postprocessing software, and texture-derived parameters were extracted. The sphericity, compactness, and spherical disproportion were significantly different in the low-risk and non-low risk groups using the Van Nuys Prognostic Index (mean ± SD, 0.479 ± 0.189 vs 0.414 ± 0.176, 0.161 ± 0.159 vs 0.112 ± 0.134, and 2.569 ± 1.434 vs 2.934 ± 1.374, respectively; P < 0.05). In the univariate analyses, sphericity (odds ratio, 7.091; 95% confidence interval, 1.236-40.666; P = 0.028) and compactness (odds ratio, 9.267; 95% confidence interval, 1.125-76.360; P = 0.039) were significantly associated with a high probability of being low risk according to the Van Nuys Prognostic Index. Whole-lesion texture analysis may be helpful in identifying patients classified as having low-risk DCIS before surgery.

Magnetic resonance imaging texture analysis for the evaluation of viable ovarian tissue in patients with ovarian endometriosis: a retrospective case-control study.

BackgroundTexture analysis has been used as a method for quantifying image properties based on textural features. The aim of the present study was to evaluate the usefulness of magnetic resonance imaging (MRI) texture analysis for the evaluation of viable ovarian tissue on the perfusion map of ovarian endometriosis.MethodsTo generate a normalized perfusion map, subtracted T1-weighted imaging (T1WI), T1WI and contrast-enhanced T1W1 with sequences were performed using the same parameters in 25 patients with surgically confirmed ovarian endometriosis. Integrated density is defined as the sum of the values of the pixels in the image or selection. We investigated the parameters for texture analysis in ovarian endometriosis, including angular second moment (ASM), contrast, correlation, inverse difference moment (IDM), and entropy, which is equivalent to the product of area and mean gray value.ResultsThe perfusion ratio and integrated density of normal ovary were 0.52±0.05 and 238.72±136.21, respectively. Compared with the normal ovary, the affected ovary showed significant differences in total size (p<0.001), fractional area ratio (p<0.001), and perfusion ratio (p=0.010) but no significant differences in perfused tissue area (p=0.158) and integrated density (p=0.112). In comparison of parameters for texture analysis between the ovary with endometriosis and the contralateral normal ovary, ASM (p=0.004), contrast (p=0.002), IDM (p<0.001), and entropy (p=0.028) showed significant differences. A linear regression analysis revealed that fractional area had significant correlations with ASM (r2=0.211), IDM (r2=0.332), and entropy (r2=0.289).ConclusionMRI texture analysis could be useful for the evaluation of viable ovarian tissues in patients with ovarian endometriosis.

Differential diagnosis of left ventricular hypertrophy with cardiac magnetic resonance imaging texture feature analysis

Abstract Funding Acknowledgements Type of funding sources: None. Background Hypertrophic cardiomyopathy (HCM), hypertensive cardiomyopathy (HTCM), and cardiac amyloidosis (CA) are characterized by left ventricular (LV) hypertrophy. CMR with late gadolinium enhancement is used in the differential diagnosis. Radiomics aims to extract features from medical images and quantify their patterns to produce quantitative biomarkers. Our aim was to develop radiomics-derived biomarkers capable of differentiating between HCM, HTCM, CA and healthy controls using CMR cine images. Methods We included 103 patients (42 HCM, 51 HTCM, 10 CA; 31 females, age 65.0 ± 12.8 years) and 50 healthy subjects (29 females, age 52.5 ± 18.0 years). The study was done on a 1.5 T CMR scanner (Sonata; Siemens, Erlangen, Germany) and all the subjects underwent a conventional protocol including cine sequences acquired in the 2-, 4- and 3-chamber views as well as in a short axis stack. The most basal slice of the short axis stack immediately distal to the LV outflow tract was used for texture feature analysis and regions of interest (ROI) were defined by segmenting the LV myocardium and dividing it into 6 segments. For image analysis a radiomic approach was used. Five different quantizations were obtained in order to find the optimal one and 43 texture features were extracted by quantization and segment. Feature selection (FS) methods were studied to see the optimal number of textures, and 4 predictive models were evaluated including k-Nearest Neighbour (KNN) and Support Vector Machine with a linear kernel (SVML) models. Performance of each model was evaluated with the area under the ROC curve (AUC). Results The AUCs of each of the models for the different texture features were analysed, from 1 to 43, according to the ranking obtained and for 5 different number of grey levels. The maximum average AUC, 1.0, was obtained with KNN model trained to classify CA and control subjects. More interestingly, 0.89 average AUC was achieved with SVML model and 1 single feature for HCM vs HTCM vs CA study. Table 1 shows the best average AUCs (mean ± SD) obtained for each study, number of subjects (N), number of grey levels used in each case, number of optimal features and the classification model. Conclusions Texture analysis allows for the correct differential diagnosis of LVH. Best results (AUC of 89%), were achieved using the SVML model in septal segments. Texture feature analysis applied to CMR cine sequences is a promising advance that might allow for the use of contrast-free protocols for the differential diagnosis of LVH.

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Background: Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer.Materials and Methods: MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance.Results: 7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively.Conclusions: Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.

768 The Role of MRI Texture Analysis in Predicting Outcomes in Locally Advanced Rectal Cancer After Chemoradiotherapy

Abstract Background Locally advanced rectal cancer (LARC) is treated by neoadjuvant chemoradiotherapy(NCRT) followed by surgery after restaging by magnetic resonance imaging(MRI). Texture analysis(TA) is a novel imaging biomarker that can assess heterogeneity in MRIs. This study hypothesizes that TA has the ability to predict the complete response(CR), survival, local recurrence, and distant metastasis. Method This is a retrospective analysis of all patients diagnosed with LARC who received NCRT and who have had MRI scans. Six parameters were systematically extracted from Textural histograms of post-treatment scans. Correlation between TA and CR was tested. These parameters were then examined to determine their ability in predicting local recurrence, distant metastases, and survival by means of Kaplan-Meier survival curves and log-rank tests. Results Four out of the six parameters extracted significantly identified CR. Utilising the same cut-off values across all parameters, three parameters significantly predicted local recurrence: Entropy(p = 0.033), mean of positive pixels(MPP)(p = 0.045), and Skewness(p = 0.018). Four parameters significantly predicted distant metastases: SD(p = 0.015), entropy(p = 0.017), MPP(p = 0.005), and skewness (p &amp;lt; 0.001). Four texture parameters significantly predicted survival: SD(p = 0.002), entropy(p = 0.001), MPP(p &amp;lt; 0.001), and skewness(p = 0.017). Conclusions MRI textural features are potentially significant imaging biomarkers in predicting survival, local recurrence, or liver metastases in LARC.

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

BackgroundThe purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast.MethodsThe preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI2min and DCE-T1WI7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency.ResultsFor ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADCMinimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI7min also showed significant differences between these two groups. Except for ADCMinimum, angular second moment of FS-T2WI (FS-T2WIASM), and the maximum gray values of DCE-T1WI7min (DCE-T1WI7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADCContrast) and entropy of FS-T2WI (FS-T2WIEntropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793–0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%.ConclusionTexture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

Value of MRI texture analysis for predicting new Gleason grade group.

To explore the potential value of multiparametric magnetic resonance imaging (mpMRI) texture analysis (TA) to predict new Gleason Grade Group (GGG). Fifty-eight lesions of fifty patients who underwent mpMRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters were obtained by the postprocessing software, and each lesion was assigned to its corresponding GGG. TA parameters derived from T2WI and DWI were statistically analyzed in detail. Energy, inertia, and correlation derived from apparent diffusion coefficient (ADC) maps and T2WI had a statistically significant difference among the five groups. Kurtosis, energy, inertia, correlation on ADC maps and Energy, inertia on T2WI were moderately related to the GGG trend. ADC-energy and T2-energy were significant independent predictors of the GGG trend. ADC-energy, T2WI-energy, and T2WI-correlation had a statistically significant difference between GGG1 and GGG2-5. ADC-energy were significant independent predictors of the GGG1. ADC-energy, T2WI-energy, and T2WI-correlation showed satisfactory diagnostic efficiency of GGG1 (area under the curve (AUC) 84.6, 74.3, and 83.5%, respectively), and ADC-energy showed excellent sensitivity and specificity (88.9 and 95.1%, respectively). TA parameters ADC-energy and T2-energy played an important role in predicting GGG trend. Both ADC-energy and T2-correlation produced a high diagnostic power of GGG1, and ADC-energy was perfect predictors of GGG1. TA parameters were innovatively used to predict new GGG trend, and the predictive factors of GGG1 were screen out.

MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study

PurposeTo evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study.Materials and methodsSeparate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability.ResultsEighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%.ConclusionMost MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies.

T2 -weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis-based classification pipeline to symptomatic and asymptomatic cases.

Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population-based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T2 -weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin-echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow-up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4-L5 and L5-S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area-under-curve of 0.91. To conclude, textural features from T2 -weighted magnetic resonance images can be applied in low back pain classification.

MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer.

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.

Magnetic resonance imaging texture analysis of unilateral lateral pterygoid myospasm in patients with temporomandibular joint disorders: A pilot study

Background and Purpose: Lots of clinical observations have found that there is a close relationship between the pathological changes of lateral pterygoid muscle (LPM) and temporomandibular disc displacement. On medical images, unfortunately, these occult dysfunctions of LPM could scarcely be detected by naked eyes. As the presence of intrinsic properties of the human body, textural feature is capable to effectively discover the subtle functional changes of involved tissues. This study aimed to evaluate the lateral pterygoid myospasm in patients with temporomandibular joint disorders (TMD) applying magnetic resonance imaging (MRI) texture analysis. Patients and Methods: From December 2019 to October 2020, totally nine patients suffered from unilateral spasm of LPM (which is a subtype of TMD) were evaluated by MRI, who were consecutively recruited from Xinjiang Medical University Affiliated First Hospital. Gray-level co-occurrence matrix method was used to analyze the textural features of MRI T2-weighted images with the maximum area of LPM scanned at axial (closed-mouth) plane. Independent samples t-test was performed to compare the relevant parameters of bilateral LPMs (healthy- and affected-side of one individual self-controlled trial). Results: There were no statistically significant values between the two sides of angular second moment, inversed differential moment, and entropy (P &gt; 0.05). The contrast of the healthy-side (267.983 ± 45.952) was significantly higher than that of the affected-side (210.003 ± 29.613) (P &lt; 0.05); and the autocorrelation of the healthy-side (4.536 ± 0.819) was significantly lower than that of the affected-side (6.563 ± 1.653) (P &lt; 0.05). Conclusion: The texture contrast and autocorrelation have certain clinical meanings of diagnosis since they could identify the altered status of LPM, and could be considered as the efficient imaging biomarkers to assess LPM changes in patients with TMD.

Predicting the prognosis of hepatocellular carcinoma with the treatment of transcatheter arterial chemoembolization combined with microwave ablation using pretreatment MR imaging texture features

ObjectiveTo investigate the prognostic value of baseline magnetic resonance imaging (MRI) texture analysis of hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA).MethodsMRI was performed on 102 patients with HCC before receiving TACE combined with MWA in this retrospective study. The best 10 texture features were screened as a feature group for each MRI sequence by MaZda software using mutual information coefficient (MI), nonlinear discriminant analysis (NDA) and other methods. The optimal feature group with the lowest misdiagnosis rate was achieved on one MRI sequence between two groups dichotomized by 3-year survival, which was used to optimize the significant texture features with the optimal cutoff values. The Cox proportional hazards model was generated for the significant texture features and clinical variables to determine the independent predictors of overall survival (OS). The predictive performance of the model was further evaluated by the area under the ROC curve (AUC). Kaplan–Meier and log-rank tests were performed for disease-free survival (DFS) and Local recurrence-free survival (LRFS).ResultsThe optimal feature group with the lowest misdiagnosis rate of 8.82% was obtained on T2WI using MI combined with NDA feature analysis. For Cox proportional hazards regression models, the independent prognostic factors associated with OS were albumin (P = 0.047), BCLC stage (P = 0.001), Correlat(1,− 1)T2 (P = 0.01) and SumEntrp(3,0)T2 (P = 0.015), and the prediction efficiency of multivariate model is AUC = 0.876, 95%CI = 0.803–0.949. Kaplan–Meier analyses further demonstrated that BCLC (P < 0.001), Correlat(1,− 1)T2 (P = 0.023) and SumEntrp(3,0)T2 (P < 0.001) were associated with DFS, and BCLC (P = 0.007) related to LRFS.ConclusionsMR imaging texture features may be used to predict the prognosis of HCC treated with TACE combined with MWA.

MRI Texture Features are Associated with Vestibular Schwannoma Histology.

To determine if commonly used radiomics features have an association with histological findings in vestibular schwannomas (VS). Retrospective case-series. Patients were selected from an internal database of those who had a gadolinium-enhanced T1-weighted MRI scan captured prior to surgical resection of VS. Texture features from the presurgical magnetic resonance image (MRI) were extracted, and pathologists examined the resected tumors to assess for the presence of mucin, lymphocytes, necrosis, and hemosiderin and used a validated computational tool to determine cellularity. Sensitivity, specificity, and positive likelihood ratios were also computed for selected features using the Youden index to determine the optimal cut-off value. A total of 45 patients were included. We found significant associations between multiple MRI texture features and the presence of mucin, lymphocytes, hemosiderin, and cellularity. No significant associations between MRI texture features and necrosis were identified. We were able to identify significant positive likelihood ratios using Youden index cut-off values for mucin (2.3; 95% CI 1.2-4.3), hemosiderin (1.5; 95% CI 1.04-2.1), lymphocytes (3.8; 95% CI 1.2-11.7), and necrosis (1.5; 95% CI 1.1-2.2). MRI texture features are associated with underlying histology in VS. 3 Laryngoscope, 131:E2000-E2006, 2021.

Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.

Background Intracranial solitary fibrous tumor(SFT)/hemangiopericytoma (HPC) is an aggressive malignant tumor originating from the intracranial vasculature. Angiomatous meningioma (AM) is a benign tumor with a good prognosis. The imaging manifestations of the two are very similar. Thus, novel noninvasive diagnostic method is urgently needed in clinical practice. Texture analysis and model building through machine learning may have good prospects. Aim To evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM. Method A total of 97 patients with SFT/HPC and 95 with AM were included in this study. Patients from each group were randomly divided into the train (70%) and test (30%) sets. ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI using ITK-SNAP software. The segmented image was imported into the AK software for texture feature extraction, and the 3D ROI signal intensity histograms of T1WI, T2WI, and contrasted T1WI were automatically obtained along with all the parameters. Modeling was performed using the language R. Confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model. Results After Lasso dimension reduction, 5, 9, and 7 texture features were extracted from T1WI, T2WI, and contrasted T1WI, respectively; additional 8 texture features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.885 (sensitivity 76.1%, specificity 87.9%) for T1WI model, 0.918 (73.1%, 95.5%) for T2WI model, 0.815 (55.2%, 93.9%) for contrasted T1WI model, and 0.959 (92.5%, 84.8%) for the combined sequence model and were enough to correctly distinguish the two groups in 71.2%, 81.4%, 69.5%, and 83.1% of cases in test set, respectively. Conclusions The radiological model based on texture features could be used to differentiate SFT/HPC from AM.

Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer's disease.

Texture analysis of magnetic resonance imaging (MRI) brain scans have been proposed as a promising tool in the early diagnosis of Alzheimer's disease (AD), but its biological correlates remain unknown. In this study, we examined the relationship between MRI texture features and AD pathology. The study included 150 participants who had a 3.0T T1-weighted image, amyloid-β positron emission tomography (PET), and tau PET within 3months of each other. In each of six brain regions (hippocampus, precuneus, and entorhinal, middle temporal, posterior cingulate and superior frontal cortices), linear regression analyses adjusting for age and sex was performed to examine the effects of regional amyloid-β and tau burden on regional texture features. We also compared neuroimaging measures based on pathological severity using ANOVA. In all regions, tau burden (p<0.05), but not amyloid-β burden, were associated with a certain texture feature that varied with the region's cytoarchitecture. Specifically, autocorrelation and cluster shade were associated with tau burden in allocortical and periallocortical regions, whereas entropy and contrast were associated with tau burden in neocortical regions. Mean signal intensity of each region did not show any associations with AD pathology. The values of the region-specific textures also varied across groups of varying pathological severity. Our results suggest that textures of T1-weighted MRI reflect changes in the brain that are associated with regional tau burden and the local cytoarchitecture. This study provides insight into how MRI texture can be used for detection of microstructural changes in AD.

Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival.

Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis. A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8+ T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively. Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophenotyping had a better prognosis than the non-inflamed one. The 5-year survival rates of the two groups were 48.5% and 25.3%, respectively (p < 0.05). The only wavelet-HLH_firstorder_Median feature was associated with OS and used to build the OS predictive model with a C-index of 0.70 (95% CI, 0.57, 0.82), which could well stratify ICC patients into high- and low-risk groups. The 1-, 3-, and 5-year survival probabilities of the stratified groups were 62.5%, 30.0%, and 24.2%, and 89.5%, 62.2%, and 42.1%, respectively (p < 0.05). The MRI texture signature could serve as a potential predictive biomarker for the IP and OS of ICC patients. • The MRI texture signature, including three wavelets and one 3D feature, showed significant associations with immunophenotyping of ICC, and all have favorable ability to discriminate immunophenotyping; a combination of the above features performed best with an AUC of 0.919. • The only wavelet-HLH_firstorder_Median feature was associated with the OS of ICC and used to build the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.

Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages.

To explore the capability of algorithms to build multivariate models integrating morphological and texture features derived from preoperative T2-weighted magnetic resonance (MR) images of gastric cancer (GC) to evaluate tumor- (T), node- (N), and metastasis- (M) stages. A total of 80 patients at our hospital who underwent abdominal MR imaging and were diagnosed with GC from December 2011 to November 2016 were retrospectively included. Texture features were calculated using T2-weighted images with a manual region of interest. Morphological characteristics were also evaluated. Classifiers and regression analyses were used to build multivariate models. Receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic efficacy. There were 8, 10, and 3 texture parameters that showed significant differences in GCs at different overall (I-II vs. III-IV), T (1-2 vs. 3-4), and N (- vs. +) stages (all p < 0.05), respectively. Mild thickening was more common in stages I-II, T1-2, and N- GCs (all p < 0.05). An irregular outer contour was more commonly observed in stages III-IV (p = 0.001) and T3-4 (p = 0.001) GCs. T3-4 and N+ GCs tended to be thickening type lesions (p = 0.005 and 0.032, respectively). The multivariate models using the naive bayes algorithm showed the highest diagnostic efficacy in predicting T and N stages (area under the ROC curves [AUC] = 0.900 and 0.863, respectively), and the model based on regression analysis had the best predictive performance in overall staging (AUC = 0.839). Multivariate models combining morphological characteristics with texture parameters based on machine learning algorithms were able to improve diagnostic efficacy in predicting the overall, T, and N stages of GCs.

Texture Analysis of Hepatocellular Carcinoma on Magnetic Resonance Imaging: Assessment for Performance in Predicting Histopathologic Grade.

The aim of the study was to evaluate the performance of texture analysis for discriminating the histopathological grade of hepatocellular carcinoma (HCC) on magnetic resonance imaging. Preoperative magnetic resonance imaging data from 101 patients with HCC, including T2-weighted imaging, arterial phase, and apparent diffusion coefficient mapping, were analyzed using texture analysis software (TexRAD). Differences among the histological groups were analyzed using the Mann-Whitney U test. The performance of texture features was evaluated using receiver operating characteristic analysis. Entropy was the most significantly relevant texture feature for distinguishing each histological grade group of HCC (P < 0.05). In ROC analysis, entropy with spatial scale filter 3 (area under curve the receiver operating characteristic curve [AUC], 0.778), mean with coarse filter (spatial scale filter 5; AUC, 0.670), and skewness without filtration (AUC, 0.760) had the highest AUC value on T2-weighted imaging, arterial phase, and apparent diffusion coefficient maps, respectively. Magnetic resonance imaging texture analysis demonstrated potential for predicting the histopathological grade of HCCs.