Texture Analysis Parameters Research Articles

Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma.

The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC). Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated. In the comparison of the Radiomics values by stages, "minimum", "Long Run Low Gray-level Emphasis" values were higher in the stage 1-2 group, while "Energy", "Total energy", "Range", "Joint Average", "Sum Average", "Gray-Level Non-Uniformity", "Short-Run High Gray-level Emphasis ", "Run Length Non-Uniformity "and "High Gray-Level Run Emphasis "values were higher in the stage 3-4 group. Of these parameters, only "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity'' values were significantly lower in tumors with low Fuhrman grade (1-2) and low TNM stage (1-2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage. This study demonstrates that "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity "parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.

Delta-radiomics increases multicentre reproducibility: a phantom study

Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.

Texture analysis on preoperative contrast-enhanced magnetic resonance imaging identifies microvascular invasion in hepatocellular carcinoma

BackgroundRadiomic texture analysis quantifies tumor heterogeneity. The aim of this study is to determine if radiomics can predict biologic aggressiveness in HCC and identify tumors with MVI. MethodsSingle-center, retrospective review of HCC patients undergoing resection/ablation with curative intent from 2009 to 2017. DICOM images from preoperative MRIs were analyzed with texture analysis software. Texture analysis parameters extracted on T1, T2, hepatic arterial phase (HAP) and portal venous phase (PVP) images. Multivariate logistic regression analysis evaluated factors associated with MVI. ResultsMVI was present in 52.2% (n = 133) of HCCs. On multivariate analysis only T1 mean (OR = 0.97, 95%CI 0.95–0.99, p = 0.043) and PVP entropy (OR = 4.7, 95%CI 1.37–16.3, p = 0.014) were associated with tumor MVI. Area under ROC curve was 0.83 for this final model. Empirical optimal cutpoint for PVP tumor entropy and T1 tumor mean were 5.73 and 23.41, respectively. At these cutpoint values, sensitivity was 0.68 and 0.5, respectively and specificity was 0.64 and 0.86. When both criteria were met, the probability of MVI in the tumor was 87%. ConclusionTumor entropy and mean are both associated with MVI. Texture analysis on preoperative imaging correlates with microscopic features of HCC and can be used to predict patients with high-risk tumors.

Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab.

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.

Texture Analysis of High b-Value Diffusion-Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas.

Preoperative evaluation of the consistency of pituitary macroadenomas is important for neurosurgeons to prepare the surgical plan. To evaluate the diagnostic performance of texture analysis (TA) of diffusion-weighted imaging (DWI) at a standard b-value (b = 1000 s/mm2 ) and a high b-value (b = 2000 s/mm2 ) for their ability to assess the tumor consistency of pituitary macroadenomas. Retrospective. Fifty patients with histologically confirmed pituitary macroadenomas were classified as soft (n = 37) or hard (n = 13) types. Coronal T2 -weighted imaging (T2 WI), Readout Segmentation of Long Variable Echo-trains (RESOLVE) DWI at b = 1000 s/mm2 and b = 2000 s/mm2 were acquired with 3.0T MRI. The corresponding apparent diffusion coefficient (ADC) maps (ADC1000 and ADC2000 ) were registered to T2 WI. Regions of interest (ROIs) were manually drawn along the solid part of the tumor from the coregistered T2 WI-ADC images. The texture parameters from T2 WI, ADC1000 , and ADC2000 were acquired. The texture parameters were compared between the two types by using unpaired Student's t-test. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to assess their diagnostic performance. Significant differences in TA parameters of ADC1000 and ADC2000 were observed between soft and hard types (P &lt; 0.05 for all), whereas the TA of T2 WI resulted in no significant difference (P &gt; 0.05 for all). TA of ADC2000 provided a superior diagnostic performance compared with that of ADC1000 (P = 0.038). A combination of mean value and entropy of ADC2000 yielded an AUC, a sensitivity, and a specificity of 0.911, 78.4% and 92.3%, respectively. TA of ADC values were useful for assessing the tumor consistency of pituitary macroadenomas. ADC2000 may facilitate better type discrimination. 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1507-1513.

Prioritization of Sub-Watersheds for Soil and Water Conservation in Parts of Narmada River through Morphometric Analysis Using Remote Sensing and GIS

ABSTRACT GIS-based prioritization of sub-watersheds of Hoshangabad and Budhni industrial area, Madhya Pradesh, India were carried out to describe the importance of morphometric parameters in the field of water and soil conservation. In the study, four subwatersheds were prioritized through geomorphologic analysis for soil and water conservation. Topographic maps 55 F/9, 55 F/10 and 55 F/13 on scale 1:50,000 were used to evaluate the drainage characteristics of watershed. SRTM DEM data has been processed for slope analysis and delineation of sub-watersheds. LISS-III, IRS data was processed for land use/ land cover analysis. Soil map has been generated by processing of NBSS &amp; LUP soil map. The various morphometric parameters evaluated in each sub-watershed includes drainage network, drainage geometry and texture analysis and relief parameters in ArcGIS. Each sub-watershed has been prioritized by assigning ranks using compound parameter. After prioritization, land use, soil type and slope classes of each subwatershed were integrated to propose suitable soil and water conservation structures at appropriate places. It has been suggested that the proposed soil and water conservation structures must be executed on priority basis to reduce the adverse effect on the land and environment. The study shows that classification and prioritization of sub-watersheds are very relevant, supportive and useful in the watershed, where there is high diversity in agricultural practices, soil texture and land cover. Thus, priority wise execution of the proposed soil and water conservation structures will not only reduce the soil erosion but also increase the surface and groundwater availability in the area. Therefore, prioritization of these sub-watersheds is found very helpful for soil conservation and management of groundwater in the watershed.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PurposeGlioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. MethodIn a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. ResultsAll five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. ConclusionsComputational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Texture analysis versus conventional MRI prognostic factors in predicting tumor response to neoadjuvant chemotherapy in patients with locally advanced cancer of the uterine cervix.

To determine the performance of texture analysis and conventional MRI parameters in predicting tumoral response to neoadjuvant chemotherapy and to assess whether a relationship exists between texture tissue heterogeneity and histological type of uterine cervix cancer. Twenty-eight patients with local advanced cervical cancer (FIGO IB2-IIIB), underwent MRI before chemotherapy. Texture analysis parameters were quantified on T2-weighted sequences, as well as the maximum diameter expressed in mm. ADC values were obtained on the ADC map. Statistical analysis included unpaired t test and ROC curve. No statistical correlation was found between conventional parameters and response to NACT. Mean and skewness showed a strong correlation with the histological type: Adenocarcinomas presented higher mean and skewness values (69.8 ± 10.5 and 0.55 ± 0.19) in comparison with squamous cell carcinomas. Using a cutoff value ≥ 29 for mean it was possible to differentiate the two histological types with a sensitivity of 100% and a specificity of 81%. Kurtosis showed a positive correlation with tumor response to NACT resulting higher in responders (v.m. 5.7 ± 1.1) in comparison with non-responders (2.3 ± 0.5). The optimal Kurtosis cutoff value for the identification of non-responders tumors was ≤ 3.7 with a sensitivity of 92% and a specificity of 75%. Texture analysis applied to T2-weighted images of uterine cervical cancer exceeded the role of conventional prognostic factors in predicting tumoral response; moreover, they showed a potential role to differentiate histological tumor types.

Texture analysis parameters derived from T1-and T2-weighted magnetic resonance images can reflect Ki67 index in soft tissue sarcoma

Background and objectivesTexture analysis derived from morphological magnetic resonance (MR) images might be associated with histopathology in tumors. The present study sought to elucidate possible associations between texture features derived from T1-and T2-weighted images with proliferation index Ki67 in soft tissue sarcomas. MethodsOverall, 29 patients (n = 13, 44.8% female) with a median age of 52 years were included into this retrospective study. Several soft tissue sarcomas were investigated. Texture analysis was performed on pre-contrast T1-weighted and T2-weighted images using the free available Mazda software. ResultsThe best correlation coefficients with Ki67 index were identified for the following parameters: T1-weighted images “45dgr_RLNonUni (p = 0.50, P = 0.006), T2-weighted images “S (4,0)SumAverg” (p = −0.45, P = 0.02). A ROC analysis was performed for Ki67-index with a threshold of 10%. The highest area under the curve (AUC) was found for the parameter “T1_WavEnHL_s-7” with an AUC of 0.90. For the threshold of Ki67 = 20% the highest AUC was identified for the parameter „T2_S (1,1)Entropy” with an AUC of 0.77. ConclusionSeveral texture features derived from T1-and T2-weighted images correlated with proliferation index Ki67 and might be used as valuable novel biomarkers in soft tissue sarcomas.

Physicochemical evaluation and Antioxidant Capacity of Cow milk yogurts Containing Different Levels of Panax ginseng Extract

The present study aimed to evaluate the physicochemical parameters of cow milk yogurt containing Panex ginseng extract at different levels (0, 5, 10, and 15%). Effect of storage period on water holding capacity, acidity, texture analysis, pH, inhibition, totally sold and sensory parameters. The physicochemical and texture characteristic of yogurt prepared from cow milk was performed. The titratable acidity and pH, inhibition indices were evaluated during 21 days of refrigerated storage. the pH of yogurt was decreased on the first day of storage from (4.895 ) to (4.695), while the inhibition percentage increased with increasing of P. ginseng extract to cow milk were (48.5±5.1) to (62.1±4.6% ). Although higher acidification was observed in the first day of storage due to P. ginseng extract addition, While moisture (%) and springing as texture increased and viscosity decreased during storage after 21 days. Changes in pH of yogurt samples during storage period were not remarkable at 14 days of storage, Therefore, the addition of P. ginseng extract to cow milk yogurt could be a good option to improve the functionality of this food matrix for dairy companies wishing to enter the functional food market.

Correlation of CT imaging features and CT texture analysis parameters with pathologic risk stratification in gastric stromal tumors

To analyze the correlation of the CT imaging features and CT texture analysis (CTTA) parameters with risk stratifications of the gastric stromal tumors (GSTs). Methods: CT (plain scans with portal phase contrast enhanced scans) images from 98 GSTs patients before surgery were collected. CT features of the tumors were retrospectively analyzed and pathological risk stratifications were determined. Feature parameters of CTTA at plain and portal phase were obtained by using a MaZda software. The correlations of CT imaging features and CTTA parameters with the risk stratifications were analyzed. Results: CT imaging features including tumor size, growth pattern, shape, margin, the relationship between mass and adjacent organs, enhancement pattern, necrosis or cystic change, and the abnormal tumor vessels were associated with the risk stratifications (P<0.05). For CTTA, entropy was associated with the risk stratifications (P<0.05). Conclusion: Certain CT imaging features and CTTA parameters are associated with risk classifications in GSTs.

Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer

Background and purposeBaseline contrast-enhanced computed tomography (CT)-derived texture analysis in locally advanced rectal cancer could help offer the best personalized treatment. The purpose of this study was to determine the value of baseline-CT texture analysis in the prediction of downstaging in patients with locally advanced rectal cancer. Patients and methodsWe retrospectively included all consecutive patients treated with neoadjuvant chemoradiation therapy (CRT) followed by surgery for locally advanced rectal cancer. Tumor texture analysis was performed on the baseline pre-CRT contrast-enhanced CT examination. Based on the selected model of downstaging with a penalized logistic regression in a training set, a radiomics score (Radscore) was calculated as a linear combination of selected features. A multivariable prognostic model that included Radscore and clinical factors was created. ResultsOf the 121 patients included in the study, 109 patients (90%) had T3-T4 cancer and 99 (82%) had N+ cancer. A downstaging response was observed in 96 patients (79%). In the training set (79 patients), the best model (ELASTIC-NET method) reduced the 36 texture features to a combination of 6 features. The multivariate analysis retained the Radscore (odds ratio [OR] = 13.25; 95% confidence interval [95% CI], 4.06–71.64; p < 0.001) and age (OR = 1.10/1 year; 1.03–1.20; p = 0.008) as independent factors. In the test set, the area under the curve was estimated to be 0.70 (95% CI, 0.48–0.92). ConclusionThis study presents a prognostic score for downstaging, from initial computed tomography-derived texture analysis in locally advanced rectal cancer, which may lead to a more personalized treatment for each patient.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance.

PurposeThe purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance.Patients and methodsPNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.ResultsThere were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989.ConclusionOur data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD).

To investigate the performance of MR-based texture analysis (TA) for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). Fifty-four adult patients (33 females, 21 males, mean age 49.8 ± 13.5years) with biopsy-proven NAFLD were enrolled and underwent MR imaging on a 1.5 T system. TA parameters were extracted on axial noncontrast 3D-GRE T1W images (slice thickness = 4.6mm) using a commercially available research software (TexRAD). Receiver operating curves (ROC), areas under the ROC (AUROC) and 95% confidence intervals (CI) were calculated to assess the accuracy of each TA parameter for the diagnosis of significant (F ≥ 2) and advanced fibrosis (F ≥ 3). The correlation between TA and histopathological features of nonalcoholic steatohepatitis (NASH) was tested calculating the Spearman's rank correlation coefficient (ρ). Thirty-seven (68%) subjects had significant fibrosis and 20 (37%) had advanced fibrosis. The TA parameters with the best performance were standard deviation (SD) and entropy, respectively, with AUROC 0.755 (95% CI 0.619-0.862, p ≤ 0.0002) and 0.769 (95% CI 0.634-0.873, p < 0.0001) for significant fibrosis and AUROC 0.746 (95% CI 0.609-0.854, p ≤ 0.0004) and 0.754 (95% CI 0.618-0.861, p ≤ 0.0002) for advanced fibrosis. SD and entropy demonstrated a moderate correlation with the degree of fibrosis (ρ = 0.457 and 0.480; p < 0.01). No significant correlation was found between TA parameters and other histopathological features of NASH. Entropy and SD extracted on T1-weighted MR images have fair accuracy for the diagnosis of significant and advanced hepatic fibrosis in patients with NAFLD.

Radiogenomics in Clear Cell Renal Cell Carcinoma: Correlations Between Advanced CT Imaging (Texture Analysis) and MicroRNAs Expression.

Purpose:A relevant challenge for the improvement of clear cell renal cell carcinoma management could derive from the identification of novel molecular biomarkers that could greatly improve the diagnosis, prognosis, and treatment choice of these neoplasms. In this study, we investigate whether quantitative parameters obtained from computed tomography texture analysis may correlate with the expression of selected oncogenic microRNAs.Methods:In a retrospective single-center study, multiphasic computed tomography examination (with arterial, portal, and urographic phases) was performed on 20 patients with clear cell renal cell carcinoma and computed tomography texture analysis parameters such as entropy, kurtosis, skewness, mean, and standard deviation of pixel distribution were measured using multiple filter settings. These quantitative data were correlated with the expression of selected microRNAs (miR-21-5p, miR-210-3p, miR-185-5p, miR-221-3p, miR-145-5p). Both the evaluations (microRNAs and computed tomography texture analysis) were performed on matched tumor and normal corticomedullar tissues of the same patients cohort.Results:In this pilot study, we evidenced that computed tomography texture analysis has robust parameters (eg, entropy, mean, standard deviation) to distinguish normal from pathological tissues. Moreover, a higher coefficient of determination between entropy and miR-21-5p expression was evidenced in tumor versus normal tissue. Interestingly, entropy and miR-21-5p show promising correlation in clear cell renal cell carcinoma opening to a radiogenomic strategy to improve clear cell renal cell carcinoma management.Conclusion:In this pilot study, a promising correlation between microRNAs and computed tomography texture analysis has been found in clear cell renal cell carcinoma. A clear cell renal cell carcinoma can benefit from noninvasive evaluation of texture parameters in adjunction to biopsy results. In particular, a promising correlation between entropy and miR-21-5p was found.

Value of Texture Analysis on Gadoxetic Acid-Enhanced MRI for Differentiating Hepatocellular Adenoma From Focal Nodular Hyperplasia.

The objective of our study was to assess the diagnostic performance of texture analysis (TA) on gadoxetic acid-enhanced MR images for differentiation of hepatocellular adenoma (HCA) from focal nodular hyperplasia (FNH). This study included 40 patients (39 women and one man) with 51 HCAs and 28 patients (27 women and one man) with 32 FNH lesions. All lesions were histologically proven with preoperative MRI performed with gadoxetic acid. Two readers reviewed all the imaging sequences to assess the qualitative MRI characteristics. The T2-weighted fast spin-echo, hepatic arterial phase (HAP), and hepatobiliary phase (HBP) sequences were used for TA. Textural features were extracted using commercially available software (TexRAD). The differences in distributions of TA parameters of FNHs and HCAs were assessed using the Mann-Whitney U test. Area under the ROC curve (AUROC) values were calculated for statistically significant features. A logistic regression analysis was conducted to explore the added value of TA. A p value < 0.002 was considered statistically significant after Bonferroni correction for multiple comparisons. Multiple TA parameters showed a statistically different distribution in HCA and FNH including skewness on T2-weighted imaging, skewness on HAP imaging, skewness on HBP imaging, and entropy on HBP imaging (p < 0.001). Skewness on HBP imaging showed the largest AUROC (0.869; 95% CI, 0.777-0.933). A skewness value on HBP imaging of greater than -0.06 had a sensitivity of 72.5% and a specificity of 90.6% for the diagnosis of HCA. Six of 51 (11.8%) HCAs lacked hypointensity on HBP imaging. A binary logistic regression analysis including hypointensity on HBP imaging and the statistically significant TA parameters yielded an AUROC of 0.979 for the diagnosis of HCA and correctly predicted 96.4% of the lesions. TA may be of added value for the diagnosis of atypical HCA presenting without hypointensity on HBP imaging.

305. Can magnetic-resonance-imaging volumetric texture analysis predict treatment outcome in rectal cancer patients undergoing neoadjuvant chemo-radiation?

Purpose This work aims to evaluate the possibility to use volumetric Texture analysis (TA) of T2 images and ADC maps to predict the clinical output in patients affected by advanced rectal cancer (LARC). Methods For this work were considered patients with non-mucinous extraperitoneal rectum adenocarcinoma with different degree of differentiation treated with neoadjuvant chemo-radiotherapy (C-RT) and radical surgery (TME). The Mandard score permitted to establish the tumour regression grade (TRG). Enrolled patients were treated with C-RT (45 Gy + 9 Gy 1,8 Gy/day and oxaliplatin and capecitabine) and subjected to pelvic MRI (T2 and DWI) examinations (1.5-T system, Signa Excite HD, GE) made at baseline and 40 ± 20 days after the end of C-RT. Two blinded readers manually segmented tumour volumes and used a homemade Image macro to evaluate TA parameters. The application of a Laplacian of Gaussian bandpass filter between 0.5 and 2.5 permits to highlight different spatial scales. Using SPSS software Pearson correlation, Cox regression, ROC and Kaplan Meier curve were evaluated considering TA parameters (TAp), TRG and metastasis development. Were correlated with early progression of disease (ePD) omitting the variable with the lowest Pearson correlation coefficient to avoid a model overfitting. With k-fold and ROC analysis was possible to validate our performance model. Results This study included 23 patients (average age 65,9 ± 11,7 years): 9 were TRG1 and 14 TRG2. No statistically significant correlation was found considering TAp calculated from T2 images. From ADC images emerged a significant correlation between tumour stage (p = 0.016) and metastasis development (p = 0.013). Moreover, ROC analysis showed how this parameter is predictive for complete treatment response (specificity 86% and accuracy 83%). Conclusion This work shows how TAp could predict the outcome of patients to implement a personalised treatment.

304. Role of texture analysis (TA) as prognostic factor in patients with glioblastoma diagnosis subject to radio-chemotherapic treatment concerning post-operating tomozolomide

Purpose The purpose of this retrospective study was to evaluate the prognostic-predictive role of texture analysis parameters (TA) measured on preoperative MRI and their correlation with known clinical and molecular factors in radiotherapy-treated glioblastoma patients. Methods This study regarded patients affected to glioblastoma and treated with concomitant postoperative radiotherapy with before surgery MRI exams (T1, FLAIR and DWI). TA GLCM parameters were obtained with LIFEx software and subsequently selected according to reproducibility through the intra-class correlation coefficient (ICC). A cut-off correlated with overall survival (OS) and free disease survival (PFS) was identified using X-Tile analysis software considering parameters like MGMT methylation and EGFR expression (i.e. Table). Results This study included 73 patients and took to the definition of three classes of risk related to the TA considering clinical outcomes. Multivariate OS analysis showed that the significantly related survival parameters were KPS and TA risk classes (p < 0.001). The AUC curve of 0.80 (DS: 0.062 95% CI: 0.684–0926). In the group with MGMT non-methylated - EGFR high expression (prognostically worse), the risk class influences statistically significant OS with a median survival rate of 4 months vs 10 months vs 12 (p: 0.028). In the MGMT group, low-expression EGFR (prognostically favourable class) patients were found, for the three classes, an OS with a median survival rate of 4, 9 and 68.8 months (Fig). Conclusion This study shows that there is a correlation between TA GLCM parameters extracted from RM sequences by building a score based on quantifiable TA parameters that seem to improve prognostic stratification of patients significantly. The results of this work require external validation, possibly prospective, that can improve the robustness of the results obtained. The purpose of this retrospective study was to evaluate the prognostic-predictive role of texture analysis parameters (TA) measured on preoperative MRI and their correlation with known clinical and molecular factors in radiotherapy-treated glioblastoma patients. This study regarded patients affected to glioblastoma and treated with concomitant postoperative radiotherapy with before surgery MRI exams (T1, FLAIR and DWI). TA GLCM parameters were obtained with LIFEx software and subsequently selected according to reproducibility through the intra-class correlation coefficient (ICC). A cut-off correlated with overall survival (OS) and free disease survival (PFS) was identified using X-Tile analysis software considering parameters like MGMT methylation and EGFR expression (i.e. Table). This study included 73 patients and took to the definition of three classes of risk related to the TA considering clinical outcomes. Multivariate OS analysis showed that the significantly related survival parameters were KPS and TA risk classes (p < 0.001). The AUC curve of 0.80 (DS: 0.062 95% CI: 0.684–0926). In the group with MGMT non-methylated - EGFR high expression (prognostically worse), the risk class influences statistically significant OS with a median survival rate of 4 months vs 10 months vs 12 (p: 0.028). In the MGMT group, low-expression EGFR (prognostically favourable class) patients were found, for the three classes, an OS with a median survival rate of 4, 9 and 68.8 months (Fig). This study shows that there is a correlation between TA GLCM parameters extracted from RM sequences by building a score based on quantifiable TA parameters that seem to improve prognostic stratification of patients significantly. The results of this work require external validation, possibly prospective, that can improve the robustness of the results obtained.

Effects of CT FOV displacement and acquisition parameters variation on texture analysis features

Texture analysis (TA) applied to CT imaging is an intensely studied topic and many studies suggested TA potential value in imaging characterisation for diagnostic purposes in different fields. However, often authors do not consider the reproducibility and the robustness versus variations in acquisition parameters; in this work, we wanted to explore the robustness of the TA features extracted from CT images.We scanned a commercial phantom (CIRS model 062M) containing plugs with nine different tissue equivalent electron densities using two different CT scanners of the same vendor and changing tube current (100 and 200 mA without modulation) and peak voltage (80 and 140 kVp). After the segmentation, we extracted TA features with LifeX and data were then statistically analysed using the generic estimate equations (GEE) method.Our results suggest that only seven out of 37 TA features extracted are not affected by variation in acquisition parameters considered in this study: GLRLM lgre, GLRLM srlge, GLRLM lrgle, GLZLM lze, GLZLM lgze, GLZLM szlge, GLZLM lzlge. Definitively, we highlighted the importance of a careful study of the dependence of TA parameters on acquisition modalities and analysis before their application in clinical studies.

Applying CT texture analysis to determine the prognostic value of subsolid nodules detected during low-dose CT screening

To analyse subsolid nodules (SSNs) detected during low-dose (LD) computed tomography (CT) screening and investigated whether CT texture analysis parameters can predict the malignancy and growth trends of GGNs. In this retrospective study, 89 SSNs were detected in 86 LDCT screening participants, including 42 pure ground-glass nodules (GGNs) and 47 part-solid GGNs. In these participants, 28 SSNs were diagnosed as lung cancer at histopathology, and 61 SSNs from participants who underwent at least two LDCT imaging studies. All nodules were divided into three groups: cancer group, growth group, and non-growth group. The nodule size, volume, attenuation, volume doubling time (VDT), and texture parameters (mean value, uniformity, entropy and energy) were assessed, respectively. The entropy of the cancer group was significantly higher than that of the growth and non-growth groups (pure GGNs: p=0.009, 0.001; part-solid GGNs: p=0.012, 0.004). The energy of the cancer group was significantly lower than that of the other groups (pure GGNs: p=0.043, 0.021; part-solid GGNs: p=0.001, 0.002). A good positive correlation was found between uniformity and VDT (p=0.022). Different CT texture parameters show good predictive value for SSNs detected at LDCT screening: the entropy and energy differences between malignant pulmonary nodules and others could be a helpful quantitative index to predict the malignancy of SSNs. Uniformity could be used to predict the growth probability of pure GGNs at baseline to pay more attention to these nodules. Moreover, the follow-up and treatment plan could be more targeted.