Texture Analysis Parameters Research Articles

Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients.

Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients. Two hundred and twelve primary breast cancer patients underwent 18F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P-value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy. A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms. Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.

Difference in Laterality of the Dorsal Striatum in Schizoaffective Disorder.

Recent research has demonstrated that the dorsal striatum is directly associated with the integration of cognitive, sensory-motor, and motivational/emotional data. Disruptions in the corticostriatal circuit have been implicated in the pathophysiology of psychosis. The dorsal striatum was reported to show lateralized pathology in psychotic disorders. In this study, we aimed to analyze the laterality of the dorsal striatum with texture analysis of T2-weighted magnetic resonance imaging (MRI) images from schizoaffective disorder (SAD) patients. Twenty SAD patients, met the inclusion criteria and had available cranial MRI data were assigned as the patient group. Twenty healthy individuals were determined as the control group. Texture analysis values were obtained from striatum region of interests (ROI) generated from T2-weighted MRI images. Data are presented as mean and standard deviation. The suitability of the data for normal distribution was analyzed with the Kolmogorov-Smirnov test. Analysis of variance (ANOVA) test (Post Hoc TUKEY) was employed to compare the group data based on test findings. There was no significant difference between the groups in terms of gender and age. There were differences in the values of texture analysis parameters of both caudate and putamen nuclei in comparison to controls. We identified differences in the left dorsal striatum nuclei in SAD. The differences in the putamen were more and more pronounced than in the caudate. Texture analyses suggest that the left dorsal striatum nuclei may be different in SAD patients. Further studies are needed to determine the pathophysiology of SAD and how it may affect disease treatment.

Role of Textural Analysis Parameters Derived from FDG PET/CT in Diagnosing Cardiac Sarcoidosis

Abstract Introduction Texture and radiomic analysis characterize the lesion's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET/CT) images in differentiating patients with cardiac sarcoidosis (CS) from patients with physiologic myocardial uptake. Methods This is a retrospective, single-center study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients. These patients underwent FDG PET/CT for the diagnosis of CS. The non-CS group underwent 18F-FDG PET/CT for other oncological indications. The PET/CT images were then processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between the CS group and the non-CS group. Receiver operating characteristics (ROC) curves were used to identify cutoff values for textural features with a p-value &lt; 0.05 for statistical significance. These parameters were then passed through a principle component analysis algorithm. Five different machine learning classifiers were then tested on the derived parameters. Results A retrospective study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients, was done. Twelve textural analysis parameters were significant in differentiating between the CS group and the non-CS group. Cutoff values were calculated for these parameters according to the ROC curves. The parameters were Discretized_HISTO_Entropy, GLCM_Homogeneity, GLCM_Energy, GLRLM_LRE, GLRLM_LGRE, GLRLM_SRLGE, GLRLM_LRLGE, NGLDM_Coarseness, GLZLM_LZE, GLZLM_LGZE, GLZLM_SZLGE, and GLZLM_LZLGE. The gradient boosting classifier gave best results on these parameters with 85.71% accuracy and an F1 score of 0.86 (max 1.0) on both classes, indicating the classifier is performing well on both classes. Conclusion Textural analysis parameters could successfully differentiate between the CS and non-CS groups noninvasively. Larger multicenter studies are needed for better clinical prognostication of these parameters.

The value of PET/CT radiomic texture analysis of primary mass and mediastinal lymph node on survival in patients with non-small cell lung cancer

ObjectiveThis study was designed to determine the potential prognostic value of radiomic texture analysis and metabolic-volumetric parameters obtained from positron emission tomography (PET) in primary mass and metastatic hilar/mediastinal lymph nodes in stage 2–3 non-small cell lung cancer (NSCLC). MethodsImages of patients diagnosed with stage 2–3 NSCLC who underwent 18F-FDG PET/CT imaging for staging up to 4 weeks before the start of treatment were evaluated using LIFEx software. Volume of interest (VOI) was generated from the primary tumor and metastatic lymph node separately, and volumetric and textural features were obtained from these VOIs. The relationship between the parameters obtained from PET of primary mass and the metastatic hilar/mediastinal lymph nodes with overall survival (OS) and progression-free survival (PFS) was analyzed. ResultsWhen radiomic features, gender and stage obtained from lymph nodes were evaluated by Cox regression analysis; GLCM_correlation (p: 0.033, HR: 4,559, 1.660–12.521, 95% CI), gender and stage were determined as prognostic factors predicting OS. In predicting PFS; stage, smoking and lymph node MTV (p: 0.033, HR: 1.008, 1.001–1.016, 95% CI) were determined as prognostic factors. However, the radiomic feature of the primary tumor could not show a significant relationship with either OS or PFS. ConclusionsIn a retrospective cohort of NSCLC patients with Stage 2 and 3 disease, volumetric and radiomic texture characteristics obtained from metastatic lymph nodes were associated with PFS and OS. Tumor heterogeneity, defined by radiomic texture features of 18 F-FDG PET/CT images, may provide complementary prognostic value in NSCLC.

Multidimensional Outcome Parameters in a Cress Seedling-CuCl2 Crystallization Assay to Corroborate Specific Effects of Stannum metallicum 30x Compared to Lactose 30x.

Previously we developed a test system which yielded highly significant evidence for specific effects of a Stannum metallicum 30x preparation in a multi-center replication trial. This test system is based on cress seed germination in homeopathic or control samples, CuCl2 crystallization of the cress extract, and subsequent digital textural image analysis of the resulting crystallization patterns. The current study aimed to investigate whether three novel outcome parameters could further corroborate and possibly characterize the specific effects of Stannum metallicum 30x. To this end, (1) cress seedling length, (2) a second texture analysis parameter, entropy and (3) the local connected fractal dimension (LCFD) of crystallization patterns as a measure of complexity were considered. The stability of the experimental setup was monitored throughout the entire investigation with systematic negative control (SNC) experiments. Cress length and entropy revealed a time-modulated potency treatment effect, in the absence of a significant main treatment effect. This indicated that the effect of the potency treatment varied significantly across the different experimental days. LCFD yielded a highly significant potency treatment effect. In addition, a significant interaction of treatment with experimental day seems to indicate a modulation of this effect. No significant effects were observed in any of the evaluations of the SNC experiments, indicative of a stable experimental setup and a reliable and specific treatment effect. Neither significant nor strong correlations were found between the four parameters, indicating that they reflect different effects of Stannum metallicum 30x on the organism treated. This quadruple characterization of the biological effects of Stannum metallicum 30x provides an unprecedented opportunity for basic homeopathy research into, among others, the presumed specificity of homeopathic preparations.

MRI Texture Analysis of Inguinal Lymph Node Metastasis in Vulvar Cancer - Associations With Histopathology.

Texture analysis is a quantitative imaging technique that provides novel biomarkers beyond conventional image reading. This study aimed to investigate the correlation between texture parameters and histopathological features of lymph nodes in patients with vulvar cancer. Overall, nine female patients (mean age 70.1±13.4 years, range=39-87 years) were included in the analysis. All patients had squamous cell carcinomas and underwent upfront surgery with inguinal lymph node resection. Immunohistochemical assessment was performed using several markers of the epithelial-mesenchymal transition. The presurgical magnetic resonance imaging (MRI) was analyzed with the MaZda package. In discrimination analysis, several parameters derived from T1-weighted images showed statistically significant differences between non-metastatic and metastatic lymph nodes. The highest statistical significance was reached by the texture feature "S(0,3)InvDfMom" (p=0.016). In correlation analysis, significant associations were found between MRI texture parameters derived from both T1-weighted and T2-weighted images and the investigated histopathological features. Notably, S(0,3)InvDfMom derived from T1-weighted images highly correlated with the Vimentin-score (r=0.908, p=0.001). Several associations between MRI texture analysis and immunohistochemical parameters were identified in metastasized lymph nodes of cases with vulvar cancer.

Performance, digestibility and meat quality from lambs fed diets with plantago lanceolata, Italian ryegrass or sainfoin herbages.

Plantago species, which is known to adapt to different climatic conditions, drought, temperature and different soil types. The aim of this study was to compare the effects of P. lanceolata herbage on fattening performance, rumen variables, digestibility, meat quality, meat nutrients and meat textural variables in lambs with the values of Italian ryegrass and sainfoin herbages. The lambs were fed total mix ration (TMR) with concentrated feed (about 30%) plus forage (about 70%) (P. lanceolata, PLA, Italian ryegrass (Lolium multiflorum); IRY or sainfoin (Onobrychis viciifolia) herbages, SAI). Twenty-four male lambs (Akkaraman breed) were assigned to three treatments with 8 lambs in each group. Fattening performance parameters, digestibility and carcass variables were determined. Meat quality and shelf life variables (oxidation, fatty acid profile and textural variables) was detected in the loin (Musculus longissimus), shoulder (M. deltoideus) and leg (M. semitendinosus) muscle samples, which were rested for 24 h +4°C and stored at -20°C for one week, one month, three months and six months. The dry matter (DM) intake, body weight gain, feed efficiency and carcass yield values of lambs, the DM and organic matter (OM) digestions (DMD and OMD) of TMRs, and the pH value, ammonia-nitrogen and short chain fatty acid (SCFA) concentrations of rumen fluid in lambs consuming PLA were similar to those of IRY and SAI (p > 0.05). PLA increased water holding capacity (WHC) and decreased cooking loss of meat compared to feeding with those of IRY and SAI (p < 0.05). The peroxide value (PV) and malondialdehyde (MDA) concentration increased with the stocking time in the meats (p < 0.05). Therefore, PV and MDA concentrations of shoulder, leg and loin meats up to 6th months for PLA were lower than those of IRY and SAI (p < 0.05). The linoleic acid concentration of loin and shoulder meats in PLA were higher than those of IRY and SAI (p < 0.05). The average oleic acid and ∑ω6 fatty acids concentration of meat in PLA was lower than those in IRY and SAI (p < 0.05). Atherogenic index and thrombogenic index values and palmitic acid levels of meat in PLA were similar to those in IRY, but lower than those in SAI (p < 0.05). Springiness, cohesiveness, resilience, hardness and chewiness values of the textural analysis parameters in meat of PLA were lower than those of IRY and SAI. As a result, effect of P. lanceolata on the fattening performance and rumen fermentation of lambs were similar with those of Italian ryegrass (Gramineae family) and sainfoin (Leguminosae family) forages and, it had a positive effect on meat quality (water holding capacity, cooking loss) and meat shelf life criteria (MDA, PV, fatty acid profile and textural variables). For functional lamb meat production, P. lanceolata can be included in the diet of lamb fattening.

Investigation of aroma profiles, textural, rheological, and sensorial qualities of yogurts with various starter cultures and goat–cow milk combinations

Abstract In the present study, aroma components, textural, rheological and sensory qualities of yogurts produced using YC-350 and ABT-2 starter cultures and combinations of goat/cow milk were investigated during the storage period. For this purpose, aroma components were analyzed by GC-MS, textural analyses by TA.XTplus Texture Analyzer and rheological analyses by Anton Paar MCR 102 rheometer. The effects of goat/cow milk combinations and variety of starter culture on aroma components, textural, rheological and sensory properties of yogurt samples were found significant (p &lt; 0.01). Also, the storage period significantly (p &lt; 0.01) affected all parameters tested except cohesiveness, resilience, and appearance. While it was found that the use of cow milk in the experimental yogurt samples increased the acetaldehyde content, it was found that it had varying effects on the diacetyl content, acetoin content, and textural and rheological analysis parameters. It was determined that the content of diacetyl and acetoin reduced with the progression of the storage times. In the sensory evaluations, the use of goat milk had positive effects on the parameters of appearance and consistency by spoon, and the use of probiotic culture (ABT-2) had positive effects on the parameters of consistency by mouth and overall acceptability.

Possibility of Using Conventional Computed Tomography Features and Histogram Texture Analysis Parameters as Imaging Biomarkers for Preoperative Prediction of High-Risk Gastrointestinal Stromal Tumors of the Stomach.

The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs). This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis. Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%. This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.

Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy.

Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS. Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF. SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r = - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all). NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.

Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker

PurposeTo evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM). MethodsPatients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two–three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated. ResultsOf the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652–0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586–0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537–0.86) in the validation set. ConclusionsBaseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.

Role of Textural Analysis of Pretreatment 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Response Prediction in Esophageal Carcinoma Patients.

Positron emission tomography/computed tomography (PET/CT) is routinely used for staging, response assessment, and surveillance in esophageal carcinoma patients. The aim of this study was to investigate whether textural features of pretreatment 18F-fluorodeoxyglucose (18F-FDG) PET/CT images can contribute to prognosis prediction in carcinoma oesophagus patients. This is a retrospective study of 30 diagnosed carcinoma esophagus patients. These patients underwent pretreatment 18F-FDG PET/CT for staging. The images were processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between progression group and nonprogression group. The original dataset was subject separately to receiver operating curve analysis. Receiver operating characteristic (ROC) curves were used to identify the cutoff values for textural features with a P < 0.05 for statistical significance. Feature selection was done with principal component analysis. The selected features of each evaluator were subject to 4 machine-learning algorithms. The highest area under the curve (AUC) values was selected for 10 features. A retrospective study of 30 primary carcinoma esophagus patients was done. Patients were followed up after chemo-radiotherapy and they underwent follow-up PET/CT. On the basis of their response, patients were divided into progression group and nonprogression group. Among them, 15 patients showed disease progression and 15 patients were in the nonprogression group. Ten textural analysis parameters turned out to be significant in the prediction of disease progression. Cutoff values were calculated for these parameters according to the ROC curves, GLZLM_long zone emphasis (Gray Level Zone Length Matrix)_long zone emphasis (44.9), GLZLM_low gray level zone emphasis (0.006), GLZLM_short zone low gray level emphasis (0.0032), GLZLM_long zone low gray level emphasis (0.185), GLRLM_long run emphasis (Gray Level Run Length Matrix) (1.31), GLRLM_low gray level run emphasis (0.0058), GLRLM_short run low gray level emphasis (0.005496), GLRLM_long run low gray level emphasis (0.00727), NGLDM_Busyness (Neighborhood Gray Level Difference Matrix) (0.75), and gray level co-occurrence matrix_homogeneity (0.37). Feature selection by principal components analysis and feature classification by the K-nearest neighbor machine-learning model using independent training and test samples yielded the overall highest AUC. Textural analysis parameters could provide prognostic information in carcinoma esophagus patients. Larger multicenter studies are needed for better clinical prognostication of these parameters.

Diagnostic Value of the Texture Analysis Parameters of Retroperitoneal Residual Masses on Computed Tomographic Scan after Chemotherapy in Non-Seminomatous Germ Cell Tumors.

After chemotherapy, patients with non-seminomatous germ cell tumors (NSGCTs) with residual masses >1 cm on computed tomography (CT) undergo surgery. However, in approximately 50% of cases, these masses only consist of necrosis/fibrosis. We aimed to develop a radiomics score to predict the malignant character of residual masses to avoid surgical overtreatment. Patients with NSGCTs who underwent surgery for residual masses between September 2007 and July 2020 were retrospectively identified from a unicenter database. Residual masses were delineated on post-chemotherapy contrast-enhanced CT scans. Tumor textures were obtained using the free software LifeX. We constructed a radiomics score using a penalized logistic regression model in a training dataset, and evaluated its performance on a test dataset. We included 76 patients, with 149 residual masses; 97 masses were malignant (65%). In the training dataset (n = 99 residual masses), the best model (ELASTIC-NET) led to a radiomics score based on eight texture features. In the test dataset, the area under the curve (AUC), sensibility, and specificity of this model were respectively estimated at 0.82 (95%CI, 0.69-0.95), 90.6% (75.0-98.0), and 61.1% (35.7-82.7). Our radiomics score may help in the prediction of the malignant nature of residual post-chemotherapy masses in NSGCTs before surgery, and thus limit overtreatment. However, these results are insufficient to simply select patients for surgery.

Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT

IntroductionThe aim of our study was to evaluate the feasibility of texture analysis of epicardial fat (EF) and thoracic subcutaneous fat (TSF) in patients undergoing cardiac CT (CCT). Materials and methodsWe compared a consecutive population of 30 patients with BMI ≤25 kg/m2 (Group A, 60.6 ± 13.7 years) with a control population of 30 patients with BMI >25 kg/m2 (Group B, 63.3 ± 11 years). A dedicated computer application for quantification of EF and a texture analysis application for the study of EF and TSF were employed. ResultsThe volume of EF was higher in group B (mean 116.1 cm3 vs. 86.3 cm3, p = 0.014), despite no differences were found neither in terms of mean density (−69.5 ± 5 HU vs. −68 ± 5 HU, p = 0.28), nor in terms of quartiles distribution (Q1, p = 0.83; Q2, p = 0.22, Q3, p = 0.83, Q4, p = 0.34). The discriminating parameters of the histogram class were mean (p = 0.02), 0,1st (p = 0.001), 10th (p = 0.002), and 50th percentiles (p = 0.02). DifVarnc was the discriminating parameter of the co-occurrence matrix class (p = 0.007).The TSF thickness was 15 ± 6 mm in group A and 19.5 ± 5 mm in group B (p = 0.003). The TSF had a mean density of −97 ± 19 HU in group A and −95.8 ± 19 HU in group B (p = 0.75). The discriminating parameters of texture analysis were 10th (p = 0.03), 50th (p = 0.01), 90th percentiles (p = 0.04), S(0,1)SumAverg (p = 0.02), S(1,-1)SumOfSqs (p = 0.02), S(3,0)Contrast (p = 0.03), S(3,0)SumAverg (p = 0.02), S(4,0)SumAverg (p = 0.04), Horzl_RLNonUni (p = 0.02), and Vertl_LngREmph (p = 0.0005). ConclusionsTexture analysis provides distinctive radiomic parameters of EF and TSF. EF and TSF had different radiomic features as the BMI varies.

Differentiation of giant cell tumours of bone, primary aneurysmal bone cysts, and aneurysmal bone cysts secondary to giant cell tumour of bone: using whole-tumour CT texture analysis parameters as quantitative biomarkers

To determine whether computed tomography (CT) texture analysis parameters can be used as quantitative biomarkers to help differentiate giant cell tumour of bones (GCTs), primary aneurysmal bone cysts (PABCs), and aneurysmal bone cysts (ABCs) secondary to giant cell tumours of bone (GABCs). One hundred and seven patients with 63 GCTs, 31 PABCs, and 13 GABCs were analysed retrospectively. All patients underwent preoperative CT. Two radiologists independently evaluated the qualitative features of the CT images and extracted texture parameters. Patient demographics, qualitative features, and texture parameters among GCTs, PABCs, and GABCs were compared statistically. Differences in these parameters between ABCs and GCTs were also assessed. ROC curves were obtained to determine optimal parameter values. The best preoperative CT parameters to differentiate GCTs, PABCs, and GABCs included one qualitative feature (location around the knee) and four texture parameters (95th percentile, maximum intensity, skewness, and kurtosis). Age and three texture parameters (5th percentile, inhomogeneity, and kurtosis) enabled statistically significant differentiation between GCTs and ABCs. Combination of the above four parameters generated the largest area under the ROC curve (AUC) for the differentiation of GCTs and ABCs. CT texture analysis parameters can be used as quantitative biomarkers for preoperative differentiation among GCTs, PABCs, and GABCs.

Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases.

Texture and radiomic analysis characterize the tumor's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-FDG PET/computed tomography (CT) images in differentiating hepatocellular carcinoma (HCC) and hepatic metastasis in patients with suspected liver tumors. This is a retrospective, single-center study of 30 patients who underwent FDG PET/CT for the characterization of liver lesions or for staging a suspected liver tumor. The histological diagnosis of either primary or metastatic tumor was obtained from CT-guided biopsy, ultrasound-guided biopsy, or surgical removal of a liver lesion. The PET/CT images were then processed in commercially available textural analysis software. Region of interest was drawn over the primary tumor with a 40% threshold and was processed further to derive 42 textural and radiomic parameters. These parameters were then compared between HCC group and hepatic metastases group. Receiver-operating characteristic (ROC) curves were used to identify cutoff values for textural features with a P value <0.05 for statistical significance. A retrospective study of 30 patients with suspected liver tumors was done. After undergoing PET/CT, the histological diagnosis of these lesions was confirmed. Among these 30 patients, 15 patients had HCC, and 15 patients had hepatic metastases from various primary sites. Seven textural analysis parameters were significant in differentiating HCC from liver metastasis. Cutoff values were calculated for these parameters according to the ROC curves, standardized uptake value (SUV) Skewness (0.705), SUV Kurtosis (3.65), SUV Excess Kurtosis (0.653), gray-level zone length matrix_long zone emphasis (349.2), gray-level zone length matrix_long zone low gray-level emphasis (1.6), gray-level run length matrix_long run emphasis (1.38) and gray-level co-occurrence matrix_Homogeneity (0.406). Textural analysis parameters could successfully differentiate HCC and hepatic metastasis non-invasively. Larger multi-center studies are needed for better clinical prognostication of these parameters.

Radiomic Features Associated with Lymphoma Development in the Parotid Glands of Patients with Primary Sjögren’s Syndrome

Non-Hodgkin Lymphoma (NHL) represents a severe complication and the main cause of morbidity in patients with primary Sjögren's syndrome (pSS). This study aimed to assess the role of textural analysis (TA) in revealing lymphoma-associated imaging parameters in the parotid gland (PG) parenchyma of patients with pSS. This retrospective study included a total of 36 patients (54.93 ± 13.34 years old; 91.6% females) diagnosed with pSS according to the American College of Rheumatology and the European League Against Rheumatism criteria (24 subjects with pSS and no lymphomatous proliferation; 12 subjects with pSS and NHL development in the PG, confirmed by the histopathological analysis). All subjects underwent MR scanning between January 2018 and October 2022. The coronal STIR PROPELLER sequence was employed to segment PG and perform TA using the MaZda5 software. A total of 65 PGs underwent segmentation and texture feature extraction (48 PGs were included in the pSS control group, and 17 PGs were included in the pSS NHL group). Following parameter reduction techniques, univariate analysis, multivariate regression, and receiver operating characteristics (ROC) analysis, the following TA parameters proved to be independently associated with NHL development in pSS: CH4S6_Sum_Variance and CV4S6_Inverse_Difference_Moment, with an area under ROC of 0.800 and 0.875, respectively. The radiomic model (resulting by combining the two previously independent TA features), presented 94.12% sensitivity and 85.42% specificity in differentiating between the two studied groups, reaching the highest area under ROC of 0.931 for the chosen cutoff value of 1.556. This study suggests the potential role of radiomics in revealing new imaging biomarkers that might serve as useful predictors for lymphoma development in patients with pSS. Further research on multicentric cohorts is warranted to confirm the obtained results and the added benefit of TA in risk stratification for patients with pSS.

Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis.

This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively). CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.

Application of Dynamic Enhanced Magnetic Resonance Imaging Texture Analysis Combined with ADCs in Predicting Pelvic Lymph Metastasis of Prostate Cancer.

The application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis combined with apparent diffusion coefficient (ADC) in predicting pelvic lymph node metastasis of prostate cancer was explored. The clinical and imaging data of 151 patients with prostate cancer admitted to The Affiliated Tumor Hospital of Guizhou Medical University from November 2019 to November 2021 were retrospectively analysed. According to the final pathological diagnosis results, they were divided into two groups: Metastasis group (n = 63, pelvic lymph node metastasis) and non-metastasis group (n = 88, no pelvic lymph node metastasis). The DCE-MRI texture parameters and ADCs of the two groups were compared using Omni-Kinetics software and MADC software packages. The receiver operating characteristic (ROC) curve was used in evaluating the predictive value of each method and their combination, and Spearman rank correlation analysis was used in evaluating their correlation. The volume transfer (Ktrans) and interstitium-to-plasmarate rate constant (Kep) in the metastatic group were significantly higher than those in the non-metastatic group (p < 0.001). However, no significant difference in extravascular extracellular space volume fraction (Ve) was found between the groups (p > 0.05). The ADC of the metastatic group was lower (p < 0.001). The Ktrans and Kep values were positively correlated with pelvic lymph node metastasis of prostate cancer (r = 0.580, 0.684; p < 0.001), and the ADC was negatively correlated with pelvic lymph node metastasis of prostate cancer (r = -0.478; p < 0.001). The ROC curve showed that the area under the curve (AUC) of DCE-MRI texture analysis parameters Ktrans and Kep combined with ADC was large, and the prediction efficiency increased. The AUC, sensitivity and specificity were 0.974, 95.20% and 93.20% (p < 0.001), respectively. DCE-MRI texture analysis combined with ADC value can accurately predict pelvic lymphatic metastasis of prostate cancer, which is helpful for the selection and formulation of clinical treatment plans and has certain guiding value for the implementation of pelvic lymph node clearing in patients.

Radiomics Approach to the Detection of Prostate Cancer Using Multiparametric MRI: A Validation Study Using Prostate-Cancer-Tissue-Mimicking Phantoms

Over the last few years, a number of studies have quantified the role of radiomics, dynamic contrast enhancement and standard MRI (T2WI + DWI) in detecting prostate cancer; however, the aim of this paper was to assess the advantage of combining radiomics with other multiparametric magnetic resonance imaging (mpMRI) (T2-DWI-DCE) in improving the detection of prostate cancer. This study used 10 prostate-cancer-tissue-mimicking phantoms to obtain preclinical data. We then focused on 46 patients who underwent mpMRI and Transrectal Ultrasound (TRUS) guided biopsy between September 2016 and December 2017. The texture analysis parameters combined with the mpMRI and compared with the histopathology of TRUS biopsy have been assessed statistically by principal component analysis (PCA) and discriminant component analysis (DCA). The prediction model and goodness-of-fit were examined with the Akaike information criterion (AIC) and McFadden pseudo-R-squared. In the PCA, there was a higher separation between cancerous and noncancerous tissue in the preclinical compared with the clinical data. Both AIC and R2 showed an improvement in the model in cancer prediction by adding the radiomics to mpMRI. The discriminant analysis showed an accuracy of cancer prediction of 81% compared with 100% in the pre-clinical phantom data. Combining radiomics with mpMRI showed an improvement in prostate cancer prediction. The ex vivo experiments validated the findings of this study.