Mean Of Positive Pixels Research Articles

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 < 0.001). Four texture parameters significantly predicted survival: SD(p = 0.002), entropy(p = 0.001), MPP(p < 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.

Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study.

To explore the utility of first-order MRI-texture analysis (TA) parameters in predicting histologic grade and muscle invasion in urinary bladder cancer (UBC). After ethical clearance, 40 patients with UBC, who were imaged on a 3.0-Tesla scanner, were retrospectively included. Using the TexRADTM platform, two readers placed freehand ROI on the sections demonstrating the largest dimension of the tumor, evaluating only one tumor per patient. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC). Mann-Whitney U test and ROC curve analysis were used to identify statistical significance and select parameters with high class separation capacity (AUC >0.8), respectively. Pearson's test was used to identify redundancy in the results. All texture parameters showed excellent ICC. The best parameters in differentiating high and low-grade tumors were mean/ mean of positive pixels (MPP) at SSF 0 (AUC: 0.897) and kurtosis at SSF 5 (AUC: 0.828) on the ADC images. In differentiating muscle invasive from non-muscle invasive tumors, mean/ MPP at SSF 0 on the ADC images showed AUC >0.8; however, this finding resulted from the confounding effect of high-grade histology on the ADC values of muscle invasive tumors. MRI-TA generated few parameters which were reproducible and useful in predicting histologic grade. No independent parameters predicted muscle invasion. There is lacuna in the literature concerning the role of MRI-TA in the prediction of histologic grade and muscle invasion in UBC. Our study generated a few first-order parameters which were useful in predicting high-grade histology.

Radiomics Biomarkers Correlate with CD8 Expression and Predict Immune Signatures in Melanoma Patients.

Treatment for metastatic melanoma includes targeted and/or immunotherapy. Although many patients respond, only a subset has complete response. As late-stage patients often have multiple tumors in difficult access sites, non-invasive techniques are necessary for the development of predictive/prognostic biomarkers. PET/CT scans from 52 patients with stage III/IV melanoma were assessed and CT image parameters were evaluated as prognostic biomarkers. Analysis indicated patients with high standard deviation or high mean of positive pixels (MPP) had worse progression-free survival (P = 0.00047 and P = 0.0014, respectively) and worse overall survival (P = 0.0223 and P = 0.0465, respectively). Whole-exome sequencing showed high MPP was associated with BRAF mutation status (P = 0.0389). RNA-sequencing indicated patients with immune "cold" signatures had worse survival, which was associated with CT biomarker, MPP4 (P = 0.0284). Multiplex immunofluorescence confirmed a correlation between CD8 expression and image biomarkers (P = 0.0028). IMPLICATIONS: CT parameters have the potential to be cost-effective biomarkers of survival in melanoma, and reflect the tumor immune-microenvironment. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/19/6/950/F1.large.jpg.

Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models.

Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

DIPG-37. PREDICTING OUTCOME IN CHILDHOOD DIFFUSE MIDLINE GLIOMAS USING MAGNETIC RESONANCE IMAGING BASED TEXTURE ANALYSIS

BACKGROUNDDiffuse midline gliomas (DMG) are aggressive brain tumours with 10% overall survival (OS) at 18 months. Predicting OS will help refine treatment strategy in this patient group. MRI based texture analysis (MRTA) is a novel technique that provides objective information about spatial arrangement of MRI signal intensity and has potential as an imaging biomarker. OBJECTIVES: To investigate MRTA in predicting OS in childhood DMG.METHODSRetrospective study of patients diagnosed with DMG, based on radiological features, treated at our institution 2007–2017. MRIs were accomplished at diagnosis and 6 weeks after radiotherapy (54Gy in 30 fractions). MRTA, performed using TexRAD software, on T2W sequence and Apparent Diffusion Coefficient (ADC) maps encapsulated tumour in the largest single axial plane. MRTA comprised filtration-histogram technique using statistical and histogram metrics for quantification of texture. Kaplan-Meier analysis determined association of MRI texture parameters with OS.RESULTS32 children 2–14 years (median 7 years) were included. MRTA was undertaken on T2W (n=32) and ADC (n=22). MRTA on T2W was better at prognosticating than on ADC maps. Children with homogenous tumour texture, at medium scale on baseline T2W MRI, had worse prognosis (mean p=0.0098, SD p=0.0115, entropy p=0.0422, mean of positive pixels (MPP) p=0.0051, kurtosis p=0.0374). MPP was the most significant texture parameter. Median survival in this group as identified by MRTA (medium texture, MPP) was 7.5 months versus 17.5 months.CONCLUSIONSDMG with more homogeneous texture on diagnostic MRI is associated with worse prognosis. MPP texture parameter is the most predictive of OS in childhood DMG.

Response prediction of hepatocellular carcinoma undergoing transcatheter arterial chemoembolization: unlocking the potential of CT texture analysis through nested decision tree models

ObjectivesTo investigate if nested multiparametric decision tree models based on tumor size and CT texture parameters from pre-therapeutic imaging can accurately predict hepatocellular carcinoma (HCC) lesion response to transcatheter arterial chemoembolization (TACE).Materials and methodsThis retrospective study (January 2011–September 2017) included consecutive pre- and post-therapeutic dynamic CT scans of 37 patients with 92 biopsy-proven HCC lesions treated with drug-eluting bead TACE. Following manual segmentation of lesions according to modified Response Evaluation Criteria in Solid Tumors criteria on baseline arterial phase CT images, tumor size and quantitative texture parameters were extracted. HCCs were grouped into lesions undergoing primary TACE (VT-lesions) or repeated TACE (RT-lesions). Distinct multiparametric decision tree models to predict complete response (CR) and progressive disease (PD) for the two groups were generated. AUC and model accuracy were assessed.ResultsThirty-eight of 72 VT-lesions (52.8%) and 8 of 20 RT-lesions (40%) achieved CR. Sixteen VT-lesions (22.2%) and 8 RT-lesions (40%) showed PD on follow-up imaging despite TACE treatment. Mean of positive pixels (MPP) was significantly higher in VT-lesions compared to RT-lesions (180.5 vs 92.8, p = 0.001). The highest AUC in ROC curve analysis and accuracy was observed for the prediction of CR in VT-lesions (AUC 0.96, positive predictive value 96.9%, accuracy 88.9%). Prediction of PD in VT-lesions (AUC 0.88, accuracy 80.6%), CR in RT-lesions (AUC 0.83, accuracy 75.0%), and PD in RT-lesions (AUC 0.86, accuracy 80.0%) was slightly inferior.ConclusionsNested multiparametric decision tree models based on tumor heterogeneity and size can predict HCC lesion response to TACE treatment with high accuracy. They may be used as an additional criterion in the multidisciplinary treatment decision-making process.Key Points• HCC lesion response to TACE treatment can be predicted with high accuracy based on baseline tumor heterogeneity and size.• Complete response of HCC lesions undergoing primary TACE was correctly predicted with 88.9% accuracy and a positive predictive value of 96.9%.• Progressive disease was correctly predicted with 80.6% accuracy for lesions undergoing primary TACE and 80.0% accuracy for lesions undergoing repeated TACE.

Tumour texture features from preoperative CT predict high-risk disease in endometrial cancer

To enable more individualised treatment of endometrial cancer, improved methods for preoperative tumour characterization are warranted. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in oncological imaging, but largely unexplored in endometrial cancer AIM: To explore whether tumour texture features from preoperative computed tomography (CT) are related to known prognostic histopathological features and to outcome in endometrial cancer patients. Preoperative pelvic contrast-enhanced CT was performed in 155 patients with histologically confirmed endometrial cancer. Tumour ROIs were manually drawn on the section displaying the largest cross-sectional tumour area, using dedicated texture analysis software. Using the filtration-histogram technique, the following texture features were calculated: mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis. These imaging markers were evaluated as predictors of histopathological high-risk features and recurrence- and progression-free survival using multivariable logistic regression and Cox regression analysis, including models adjusting for high-risk status based on preoperative biopsy, magnetic resonance imaging (MRI) findings, and age. High tumour entropy independently predicted deep myometrial invasion (odds ratio [OR] 3.7, p=0.008) and cervical stroma invasion (OR 3.9, p=0.02). High value of MPP (MPP5 >24.2) independently predicted high-risk histological subtype (OR 3.7, p=0.01). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival (HR 1.1, p=0.06). CT texture analysis yields promising imaging markers in endometrial cancer and may supplement other imaging techniques in providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies.

MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma.

To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.

Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification.

To create a CT texture-based machine learning algorithm that distinguishes benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019. In this IRB-approved, HIPAA-compliant study, 4,454 adult patients underwent renal mass protocol CT or CT urography from January 2011 to June 2018. Of these, 257 cystic renal masses were included in the final study cohort. Each mass was independently classified using Bosniak version 2019 by three radiologists, resulting in 185 benign (Bosniak I or II) and 72 potentially malignant (Bosniak IIF, III or IV) masses. Six texture features: mean, standard deviation, mean of positive pixels, entropy, skewness, kurtosis were extracted using commercial software TexRAD (Feedback PLC, Cambridge, UK). Random forest (RF), logistic regression (LR), and support vector machine (SVM) machine learning algorithms were implemented to classify cystic renal masses into the two groups and tested with tenfold cross validations. Higher mean, standard deviation, mean of positive pixels, entropy, skewness were statistically associated with the potentially malignant group (P ≤ 0.0015 each). Sensitivity, specificity, positive predictive value, negative predictive value, and area under curve of RF model was 0.67, 0.91, 0.75, 0.88, 0.88; of LR model was 0.63, 0.93, 0.78, 0.86, 0.90, and of SVM model was 0.56, 0.91, 0.71, 0.84, 0.89, respectively. Three CT texture-based machine learning algorithms demonstrated high discriminatory capability in distinguishing benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019. If validated, CT texture-based machine learning algorithms may help reduce interreader variability when applying the Bosniak classification.

Efficacy of ZOOMit coronal diffusion-weighted imaging and MR texture analysis for differentiating between benign and malignant distal bile duct strictures.

To investigate the diagnostic efficacy of ZOOMit coronal diffusion-weighted imaging (Z-DWI) and MR texture analysis (MRTA) for differentiating benign from malignant distal bile duct strictures. We retrospectively enrolled a total of 71 patients with distal bile duct stricture who underwent magnetic resonance cholangiopancreatography (MRCP). For quantitative analysis, the average apparent diffusion coefficient (ADC) value at suspected stricture sites was assessed on both Z-DWI and conventional DWI (C-DWI). For qualitative analysis, two reviewers independently reviewed two image sets containing different diffusion-weighted images, and receiver operating characteristic (ROC) curve analysis was performed. Several MRTA parameters were extracted from the area of the stricture on the ADC map of the ZOOMit coronal diffusion-weighted images using commercially available software. Among 71 patients, 26 patients were diagnosed with malignant stricture. On quantitative analysis, the average ADC value of the malignant and benign strictures, using Z-DWI, was 1.124 × 10-3 mm2/s and 1.522 × 10-3 mm2/s, respectively (P < 0.001). The average ADC value of the malignant and benign strictures, using C-DWI, was 1.107 × 10-3 mm2/s and 1.519 × 10-3 mm2/s, respectively (P < 0.001). On qualitative analysis, for each reviewer, the area under the ROC curve (AUC) values for differentiating benign from malignant stricture was 0.928 and 0.939, respectively, for the ZOOMit diffusion set and 0.851 and 0.824, respectively, for the conventional diffusion set. Multiple MRTA parameters showed a significantly different distribution for the benign and malignant strictures, including mean, entropy, mean of positive pixels, and kurtosis at spatial filtration values of 0, 5, and 6mm. The addition of Z-DWI to conventional MRCP is helpful in differentiating benign from malignant bile duct strictures, and some MRTA parameters also can be helpful in differentiating benign from malignant distal bile duct strictures.

Predicting outcome in childhood diffuse midline gliomas using magnetic resonance imaging based texture analysis

BackgroundDiffuse midline gliomas (DMG) are aggressive brain tumours, previously known as diffuse intrinsic pontine gliomas (DIPG), with 10% overall survival (OS) at 18 months. Predicting OS will help refine treatment strategy in this patient group. MRI based texture analysis (MRTA) is novel image analysis technique that provides objective information about spatial arrangement of MRI signal intensity (heterogeneity) and has potential to be imaging biomarker. ObjectivesTo investigate MRTA in predicting OS in childhood DMG. MethodsRetrospective study of patients diagnosed with DMG, based on radiological features, treated at our institution 2007–2017. MRIs were acquired at diagnosis and 6 weeks after radiotherapy (54Gy in 30 fractions). MRTA was performed using commercial available TexRAD research software on T2W sequence and Apparent Diffusion Coefficient (ADC) maps encapsulating tumour in the largest single axial plane. MRTA comprised filtration-histogram technique using statistical and histogram metrics for quantification of texture. Kaplan-Meier survival analysis determined association of MRI texture parameters with OS. ResultsIn all, 32 children 2–14 years (median 7 years) were included. MRTA was undertaken on T2W (n=32) and ADC (n=22). T2W-MRTA parameters were better at prognosticating than ADC-MRTA. Children with homogenous tumour texture, at medium scale on diagnostic T2W MRI, had worse prognosis (Mean of Positive Pixels (MPP): P=0.005, mean: P=0.009, SD: P=0.011, kurtosis: P=0.037, entropy: P=0.042). Best predictor MPP was able to stratify patients into poor and good prognostic groups with median survival of 7.5 months versus 17.5 months, respectively. ConclusionsDMG with more homogeneous texture on diagnostic MRI is associated with worse prognosis. Texture parameter MPP is the most predictive marker of OS in childhood DMG.

Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers.

To study if pre-treatment CT texture features in locally advanced squamous cell carcinoma of laryngo-pharynx can predict long-term local control and laryngectomy free survival (LFS). Image texture features of 60 patients treated with chemoradiation (CTRT) within an ethically approved study were studied on contrast-enhanced images using a texture analysis research software (TexRad, UK). A filtration-histogram technique was used where the filtration step extracted and enhanced features of different sizes and intensity variations corresponding to a particular spatial scale filter (SSF): SSF = 0 (without filtration), SSF = 2 mm (fine texture), SSF = 3-5 mm (medium texture) and SSF = 6 mm (coarse texture). Quantification by statistical and histogram technique comprised mean intensity, standard-deviation, entropy, mean positive pixels, skewness and kurtosis. The ability of texture analysis to predict LFS or local control was determined using Kaplan-Meier analysis and multivariate cox model. Median follow-up of patients was 24 months (95% CI:20-28). 39 (65%) patients were locally controlled at last follow-up. 10 (16%) had undergone salvage laryngectomy after CTRT. For both local control & LFS, threshold optimal cut-off values of texture features were analyzed. Medium filtered-texture feature that were associated with poorer laryngectomy free survival were entropy ≥4.54, (p = 0.006), kurtosis ≥4.18; p = 0.019, skewness ≤-0.59, p = 0.001, and standard deviation ≥43.18; p = 0.009). Inferior local control was associated with medium filtered features entropy ≥4.54; p 0.01 and skewness ≤ - 0.12; p = 0.02. Using fine filters, entropy ≥4.29 and kurtosis ≥-0.27 were also associated with inferior local control (p = 0.01 for both parameters). Multivariate analysis showed medium filter entropy as an independent predictor for LFS and local control (p < 0.001 & p = 0.001). Medium texture entropy is a predictor for inferior local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancer and this can complement clinico-radiological factors in predicting prognosticating these tumors. Texture features play an important role as a surrogate imaging biomarker for predicting local control and laryngectomy free survival in locally advanced laryngo-pharyngeal tumors treated with definitive chemoradiation.

CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer

Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes. The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer (CRC) patients using CT texture analysis. Methods This retrospective study included 92 patients who had histopathologically confirmed CRC and underwent preoperative contrast-enhanced CT examinations. The patients were assigned into a training cohort (n=51) and a validation cohort (n=41). We placed the region of interest in the tumour regions on the selected axial images using software of TexRad to extract a series of quantitative parameters based on the spatial scaling factors (SSFs), including mean, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. The texture parameters and clinical characteristics (age, gender, tumour location, histopathology, tumour size, T, N, M stages) were compared between the mutated and wild-type KRAS patient groups in training cohort and validation cohort. Before building the multiple feature classifier, we calculated the correlations of the features using Pearson's correlation coefficient, and if any two features were significantly correlated, the one with lower AUC was removed. Ultimately, only the most discriminative isolated features were combined to train a supporting vector machine (SVM) classifier. The receiver operating characteristic (ROC) curve was processed for evaluating the diagnostic efficiency of texture parameters in differentiating CRC patients with mutated KRAS from those with wild-type KRAS. Results None of the clinical characteristics were significant different between CRC patients with wild-type KRAS and mutated KRAS in both cohorts. For predicting the expression of mutated KRAS in CRC patients, the perfect model which combined skewness on SSF 5 by unenhanced CT, entropy on SSF 2, skewness and kurtosis on SSF 0, and kurtosis and mean on SSF 3 by enhanced CT, showed a desirable AUC of 0.951 (95% CI: 0.895-1, P<0.001), with a sensitivity of 88.9% and a specificity of 91.7%, when the cut-off value was 0.46 in the training cohort; while in the validation cohort, the AUC value was 0.995 (95% CI: 0.982-1, P<0.001), the sensitivity was 100%, and the specificity was 93.7% when the cut-off value was 0.28. Conclusion It is feasible to evaluate the KRAS mutational status in CRC using CT texture analysis.

Assessment of Response to Neoadjuvant Therapy Using CT Texture Analysis in Patients With Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma.

OBJECTIVE. The goal of this study was to assess the correlation between CT-derived texture features of pancreatic ductal adenocarcinoma (PDAC) and histologic and biochemical markers of response to neoadjuvant treatment as well as disease-free survival in patients with potentially resectable PDAC. SUBJECTS AND METHODS. Thirty-nine patients completed this prospective study protocol between November 2013 and December 2016. All patients received neoadjuvant chemotherapy, underwent surgical resection, and had histologic grading of tumor response. Similar CT protocol was used for all patients. Pancreatic (late arterial) phase of pre- and posttreatment CT scans were evaluated. Histogram analysis and spatial-band-pass filtration were used to extract textural features. Correlation between textural parameters, histologic response, biochemical response, and genetic mutations was assessed using Mann-Whitney test, chi-square analysis, and multivariate logistic regression. Association with disease-free survival was assessed using Kaplan-Meier method and Cox model. RESULTS. Pretreatment mean positive pixel (MPP) at fine- and medium-level filtration, pretreatment kurtosis at medium-level filtration, changes in kurtosis, and pretreatment tumor SD were statistically different between patients with no or poor histologic response and favorable histologic response (p < 0.05). Changes in skewness and kurtosis at medium-level filtration significantly correlated with biochemical response (p < 0.01). On the basis of multivariate analysis, patients with higher MPP at pretreatment CT were more likely to have favorable histologic response (odds ratio, 1.06; 95% CI, 1.002-1.12). The Cox model for association between textural features and disease-free survival was statistically significant (p = 0.001). CONCLUSION. Textural features extracted from baseline pancreatic phase CT imaging of patients with potentially resectable PDAC and longitudinal changes in tumor heterogeneity can be used as biomarkers for predicting histologic response to neoadjuvant chemotherapy and disease-free survival.

Combined Qualitative and Quantitative Assessment of Low-Attenuation Renal Lesions Improves Identification of Renal Malignancy on Noncontrast Computed Tomography.

The objective of this study was to assess renal lesions measuring less than 20 Hounsfield units (HU) on noncontrast computed tomography (NCT). Twenty-one (18.1%) of 116 consecutive pathologically proven renal cell carcinomas measured less than 20 HU on NCT and were compared with 40 confirmed benign cysts also measuring less than 20 HU. All lesions were assessed qualitatively (heterogeneous or homogenous) by 3 blinded readers and quantitatively with commercially available textural analysis software. Finally, a combined assessment was performed. Qualitative assessment performed well (sensitivity, 76%-90%; specificity, 70%-88%). Quantitative assessment revealed mean positive pixels as having the highest performance (area under the curve, 0.912; sensitivity, 90%; specificity, 80% at a cutoff value of 21). The combined assessment, using the mean positive pixel cutoff, improved the sensitivity (reader 1, 100%; reader 2, 95%; and reader 3, 95%). Qualitative and quantitative assessments have relatively good performance, but the combination can nearly eliminate renal cell carcinomas being missed on NCT.

Usefulness of CT texture analysis in differentiating benign and malignant renal tumours

To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters. A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy. Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC. Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.

P5000Wnt/b-catenin signalling drives angiotensin II induced cardiac fibrosis via WISP-1

Abstract Background Myocardial infarction (MI) and hypertension lead to myocardial injury, which induces negative remodelling and cardiac fibrosis. Cardiac fibrosis, which involves inflammatory cell infiltration and myofibroblast activation, causes worldwide mortality and morbidity. In response to MI or hypertension induced by prolonged angiotensin II (AngII) exposure, activated myofibroblasts produce extracellular matrix proteins. However, if unchecked, excessive collagen deposition occurs leading to myocardial stiffening, heart failure and arrhythmias. Purpose The underlying mechanisms leading to pathological collagen deposition are not fully elucidated. There is debate regarding the involvement of the Wnt signalling pathway and its product Wnt Inducible Signalling pathway protein-1 (WISP-1) in cardiac fibrosis. Therefore, this project aimed to investigate the interaction of AngII and the Wnt/β-catenin signalling pathway in cardiac fibrosis. Methods The effect of AngII (100nM) on collagen levels in human cardiac fibroblasts was investigated in vitro (data expressed as fold change from control ± SEM). In vivo experiments (n=6–8) determined the involvement of the Wnt/b-catenin pathway, specifically WISP-1, in response to AngII infusion (500ng/kg/min) for 4 weeks (Apolipoprotein E−/−/WISP-1+/+ vs. ApolipoproteinE−/−/WISP-1−/− mice on a high-fat diet, data expressed as mean positive pixel % ± SEM). Results AngII significantly increased collagen type 1 protein levels produced by human cardiac fibroblasts (2.94±0.75 vs 1±0, p&lt;0.05). Inhibition of Wnt/b-catenin signalling with 25nM iCRT14 significantly suppressed AngII-induced collagen levels (0.46±0.07 vs. 1±0, p&lt;0.05). As expected, AngII infusion significantly induced hypertension in all mice. Immunohistochemistry demonstrated type 1 collagen was markedly higher in AngII mice than control mice (1.07±0.27 vs. 0.29±0.06, p&lt;0.05). However, in the absence of WISP-1, AngII did not enhance collagen type 1 levels. Further immunohistochemical analysis of murine hearts demonstrated that AngII infusion caused significant alterations in the Wnt/β-catenin signalling markers AXIN-2 (35±3.9 vs. 10.7±2.6 p&lt;0.05) and PPAR-d (92±1.4 vs. 17.3±4.3 p&lt;0.05). This effect was reduced by WISP-1 deletion. Furthermore, AngII-infusion disrupted N-cadherin junctions (0.55±0.08 vs. 0.29±0.02 p&lt;0.05) suggesting modulation of cell-to-cell contacts and enhanced β-catenin signalling. Conclusion This study indicates that AngII enhances cardiac fibrosis via modulation of the Wnt signalling pathway, in part via WISP-1. Further delineation of this interaction may lead to the use of Wnt/β-catenin or WISP-1 inhibitors to suppress myocardial injury induced cardiac fibrosis in post-MI or hypertensive patients. Acknowledgement/Funding Elizabeth Blackwell Institute, British Heart Foundation

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

The study evaluated the usefulness of magnetic resonance imaging (MRI) texture parameters in differentiating clear cell renal carcinoma (CC-RCC) from non-clear cell carcinoma (NC-RCC) and in the histological grading of CC-RCC. After institutional ethical approval, this retrospective study analyzed 33 patients with 34 RCC masses (29 CC-RCC and five NC-RCC; 19 low-grade and 10 high-grade CC-RCC), who underwent MRI between January 2011 and December 2012 on a 1.5-T scanner (Avanto, Siemens, Erlangen, Germany). The MRI protocol included T2-weighted imaging (T2WI), diffusion-weighted imaging [DWI; at b 0, 500 and 1000s/mm2 with apparent diffusion coefficient (ADC) maps] and T1-weighted pre and postcontrast [corticomedullary (CM) and nephrographic (NG) phase] acquisition. MR texture analysis (MRTA) was performed using the TexRAD research software (Feedback Medical Ltd., Cambridge, UK) by a single reader who placed free-hand polygonal region of interest (ROI) on the slice showing the maximum viable tumor. Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness and kurtosis] at five spatial scaling factors (SSF) as well as on the unfiltered image. Mann-Whitney test was used to compare the texture parameters of CC-RCC versus NC-RCC, and high-grade versus low-grade CC-RCC. P value < 0.05 was considered significant. A 3-step feature selection was used to obtain the best texture metrics for each MRI sequence and included the receiver-operating characteristic (ROC) curve analysis and Pearson's correlation test. The best performing texture parameters in differentiating CC-RCC from NC-RCC for each sequence included (area under the curve in parentheses): entropy at SSF 4 (0.807) on T2WI, SD at SSF 4 (0.814) on DWI b500, SD at SSF 6 (0.879) on DWI b1000, mean at SSF 0 (0.848) on ADC, skewness at SSF 2 (0.854) on T1WI and skewness at SSF 3 (0.908) on CM phase. In differentiating high from low-grade CC-RCC, the best parameters were: entropy at SSF 6 (0.823) on DWI b1000, mean at SSF 3 (0.889) on CM phase and MPP at SSF 5 (0.870) on NG phase. Several MR texture parameters showed excellent diagnostic performance (AUC > 0.8) in differentiating CC-RCC from NC-RCC, and high-grade from low-grade CC-RCC. MRTA could serve as a useful non-invasive tool for this purpose.

The clinical utility of prostate cancer heterogeneity using texture analysis of multiparametric MRI.

To determine if multiparametric MRI (mpMRI) derived filtration-histogram based texture analysis (TA) can differentiate between different Gleason scores (GS) and the D'Amico risk in prostate cancer. We retrospectively studied patients whose pre-operative 1.5T mpMRI had shown a visible tumour and who subsequently underwent radical prostatectomy (RP). Guided by tumour location from the histopathology report, we drew a region of interest around the dominant visible lesion on a single axial slice on the T2, Apparent Diffusion Coefficient (ADC) map and early arterial phase post-contrast T1 image. We then performed TA with a filtration-histogram software (TexRAD -Feedback Medical Ltd, Cambridge, UK). We correlated GS and D'Amico risk with texture using the Spearman's rank correlation test. We had 26 RP patients with an MR-visible tumour. Mean of positive pixels (MPP) on ADC showed a significant negative correlation with GS at coarse texture scales. MPP showed a significant negative correlation with GS without filtration and with medium filtration. MRI contrast texture without filtration showed a significant, negative correlation with D'Amico score. MR T2 texture showed a significant, negative correlation with the D'Amico risk, particularly at textures without filtration, medium texture scales and coarse texture scales. ADC map mpMRI TA correlated negatively with GS, and T2 and post-contrast images with the D'Amico risk score. These associations may allow for better assessment of disease prognosis and a non-invasive method of follow-up for patients on surveillance. Further, identifying clinically significant prostate cancer is essential to reduce harm from over-diagnosis and over-treatment.

Distinguishing metastases from benign adrenal masses: what can CT texture analysis do?

Background Computed tomography texture analysis (CTTA) has gained an increasing role in oncology and has successfully demonstrated to reflect biological associations with glucose metabolism, hypoxia, angiogenesis, and even genetic variation. Purpose To determine whether quantitative CTTA can be used to differentiate metastases from benign adrenal masses on single energy CT images. Material and Methods A total of 225 patients with 265 histologically confirmed adrenal masses (101 metastases, 98 pheochromocytomas, and 66 lipid-poor adenomas) were included in this retrospective study. CTTA was performed and six texture parameters (including mean, SD of pixel distribution histogram, mean of positive pixels, entropy, kurtosis, skewness) across six spatial scaling factor (SSF) were recorded on both unenhanced and contrast-enhanced CT images. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated using the significant texture parameters for the objective. Diagnostic performance was evaluated using the cut-off values of texture parameters by ROC analysis. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. Results Compared to benign adrenal masses, metastases had significantly lower mean gray-level intensity, SD, entropy, mean of positive pixels and kurtosis on unenhanced images ( P &lt; 0.0083). On contrast-enhanced CT images, except for skewness and kurtosis, the other four texture texture-quantifiers were lower in the metastatic compared to the other group ( P &lt; 0.0083). A model incorporating mean, SD, entropy and mean value of positive pixels produced an AUC of 0.85 ± 0.03 with an SVM accuracy of 77% to identify metastatic from benign adrenal masses. Conclusion Differentiation of metastatic and benign adrenal masses can be achieved accurately with CTTA on contrast-enhanced CT images.