To determine if computed tomographic (CT) texture features of primary colorectal cancer are related to 5-year overall survival rate.
Institutional review board waiver was obtained for this ...retrospective analysis. Texture features of the entire primary tumor were assessed with contrast material-enhanced staging CT studies obtained in 57 patients as part of an ethically approved study and by using proprietary software. Entropy, uniformity, kurtosis, skewness, and standard deviation of the pixel distribution histogram were derived from CT images without filtration and with filter values corresponding to fine (1.0), medium (1.5, 2.0), and coarse (2.5) textures. Patients were followed up until death and were censored at 5 years if they were still alive. Kaplan-Meier analysis was performed to determine the relationship, if any, between CT texture and 5-year overall survival rate. The Cox proportional hazards model was used to assess independence of texture parameters from stage.
Follow-up data were available for 55 of 57 patients. There were eight stage I, 19 stage II, 17 stage III, and 11 stage IV cancers. Fine-texture feature Kaplan-Meier survival plots for entropy, uniformity, kurtosis, skewness, and standard deviation of the pixel distribution histogram were significantly different for tumors above and below each respective threshold receiver operating characteristic (ROC) curve optimal cutoff value (P = .001, P = .018, P = .032, P = .008, and P = .001, respectively), with poorer prognosis for ROC optimal values (a) less than 7.89 for entropy, (b) at least 0.01 for uniformity, (c) less than 2.48 for kurtosis, (d) at least -0.38 for skewness, and (e) less than 61.83 for standard deviation. Multivariate Cox proportional hazards regression analysis showed that each parameter was independent from the stage predictor of overall survival rate (P = .001, P = .009, P = .006, P = .02, and P = .001, respectively).
Fine-texture features are associated with poorer 5-year overall survival rate in patients with primary colorectal cancer.
http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120254/-/DC1.
To correlate computed tomographic (CT) texture in non-small cell lung cancer (NSCLC) with histopathologic markers for angiogenesis and hypoxia.
The study was institutional review board approved, and ...informed consent was obtained. Fourteen patients with NSCLC underwent CT prior to intravenous administration of pimonidazole (0.5 g/m(2)), a marker of hypoxia, 24 hours before surgery. Texture was assessed for unenhanced and contrast material-enhanced CT images by using a software algorithm that selectively filters and extracts texture at different anatomic scales (1.0 fine detail to 2.5 coarse features), with quantification of the standard deviation (SD) of all pixel values and the mean value of positive pixels (MPP) and uniformity of distribution of positive gray-level pixel values (UPP). After surgery, matched tumor sections were stained for angiogenesis (CD34 expression) and for markers of hypoxia (glucose transporter protein 1 Glut-1 and pimonidazole). The percentage and average intensity of the tumor stained were assessed. A linear mixed-effects model was used to assess the correlations between CT texture and staining intensity.
SD and MPP quantified from medium to coarse texture on contrast-enhanced CT images showed significant associations with the average intensity of tumor staining with pimonidazole (for SD: filter value, 2.5; slope = 0.003; P = .0003). UPP (medium to coarse texture) on unenhanced CT images showed a significant inverse association with tumor Glut-1 expression (filter value, 2.5; slope = -115.13; P = .0008). MPP quantified from medium to coarse texture on both unenhanced and contrast-enhanced CT images showed significant inverse associations with tumor CD34 expression (unenhanced CT: filter value, 1.8; slope = -0.0008; P = .003; contrast-enhanced CT: filter value, 1.8; slope = -0.0006; P = .004).
Texture parameters derived from CT images of NSCLC have the potential to act as imaging correlates for tumor hypoxia and angiogenesis.
http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112428/-/DC1.
Abstract Background and purpose Grading of cerebral gliomas is important both in treatment decision and assessment of prognosis. The purpose of this study was to determine the diagnostic accuracy of ...grading cerebral gliomas by assessing the tumor heterogeneity using MRI texture analysis (MRTA). Material and methods 95 patients with gliomas were included, 27 low grade gliomas (LGG) all grade II and 68 high grade gliomas (HGG) (grade III = 34 and grade IV = 34). Preoperative MRI examinations were performed using a 3T scanner and MRTA was done on preoperative contrast-enhanced three-dimensional isotropic spoiled gradient echo images in a representative ROI. The MRTA was assessed using a commercially available research software program (TexRAD) that applies a filtration-histogram technique for characterizing tumor heterogeneity. Filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine features) to 6 mm (coarse features), the statistical parameter standard deviation (SD) was obtained. Receiver operating characteristics (ROC) was performed to assess sensitivity and specificity for differentiating between the different grades and calculating a threshold value to quantify the heterogeneity. Results LGG and HGG was best discriminated using SD at fine texture scale, with a sensitivity and specificity of 93% and 81% (AUC 0.910, p < 0.0001). The diagnostic ability for MRTA to differentiate between the different sub-groups (grade II–IV) was slightly lower but still significant. Conclusions Measuring heterogeneity in gliomas to discriminate HGG from LGG and between different histological sub-types on already obtained images using MRTA can be a useful tool to augment the diagnostic accuracy in grading cerebral gliomas and potentially hasten treatment decision.
Purpose
To assess the potential role of computed tomography (CT) texture analysis (CTTA) in identifying vulnerable patients with carotid artery atherosclerosis.
Methods
In this case-control pilot ...study, 12 patients with carotid atherosclerosis and a subsequent history of transient ischemic attack or stroke were age and sex matched with 12 control cases with asymptomatic carotid atherosclerosis (follow-up time 103.58 ± 9.2 months). CTTA was performed using a commercially available research software package (TexRAD) by an operator blinded to clinical data. CTTA comprised a filtration-histogram technique to extract features at different scales corresponding to spatial scale filter (fine = 2 mm, medium = 3 mm, coarse = 4 mm), followed by quantification using histogram-based statistical parameters: mean, kurtosis, skewness, entropy, standard deviation, and mean value of positive pixels. A single axial slice was selected to best represent the largest cross-section of the carotid bifurcation or the greatest degree of stenosis, in presence of an atherosclerotic plaque, on each side.
Results
CTTA revealed a statistically significant difference in skewness between symptomatic and asymptomatic patients at the medium (0.22 ± 0.35 vs − 0.18 ± 0.39,
p
< 0.001) and coarse (0.23 ± 0.22 vs 0.03 ± 0.29,
p
= 0.003) texture scales. At the fine-texture scale, skewness (0.20 ± 0.59 vs − 0.18 ± 0.58,
p
= 0.009) and standard deviation (366.11 ± 117.19 vs 300.37 ± 82.51,
p
= 0.03) were significant before correction.
Conclusion
Our pilot study highlights the potential of CTTA to identify vulnerable patients in stroke and TIA. CT texture may have the potential to act as a novel risk stratification tool in patients with carotid atherosclerosis.
Aims
This pilot study aims to determine if tumour heterogeneity assessed using magnetic resonance imaging (MRI) radiomics-based texture analysis (TA) can differentiate between lipoma and atypical ...lipomatous tumour (ALT)/well-differentiated liposarcoma (WDL).
Materials and methods
Thirty consecutive ALT/WDLs and 30 lipomas were included in the study, cases diagnosed both histologically and with murine double minute 2 (
MDM2
) gene amplification by fluorescence in situ hybridisation (FISH) in excision specimens. Multiple patient, MRI and MRTA factors were assessed. Heterogeneity was evaluated using a filtration-histogram technique-based textural analysis on single axial proton density (PD) and coronal T1-W images of the most homogenously fatty component of the lesion.
Results
Thirty-three percent of the diagnoses of ALT/WDL vs lipoma were confirmed using FISH
MDM2
analysis. ALT/WDLs were statistically different from lipomas in location (site in the body and depth from skin surface) and fat content, with
p
values of 0.021, 0.001, and 0.021 respectively. Nine of 36 (25%) texture parameters had significant differences between ALT/WDLs and lipomas on axial PD MRTA, with the most significant results at medium and coarse texture scales particularly mean intensity (
p
= 0.003) at SSF = 6, and kurtosis (
p
= 0.012) at SSF = 5. A cut-off value of < 304 for coarse-filtered texture on axial PD MRI identified ALT from lipoma with a sensitivity and specificity of 70% (AUC = 0.73,
p
= 0.003).
Conclusions
Texture heterogeneity quantified at fine, medium, and coarse texture scales are significant differentiators of lipoma and ALT/WDL with the difference particularly marked in medium and coarse texture scales for two MR TA parameters: mean and kurtosis.
Background
Texture analysis has been done on several radiological modalities to stage, differentiate, and predict prognosis in many oncologic tumors.
Purpose
To determine the diagnostic accuracy of ...discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with magnetic resonance imaging (MRI) texture analysis (MRTA).
Material and Methods
Preoperative MRI examinations done on a 3-T scanner of 43 patients were included: 22 GBM and 21 MET. MRTA was performed on diffusion tensor imaging (DTI) in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterizing tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine) to 6 mm (coarse). Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristic (ROC) analysis.
Results
Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However, the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%.
Conclusion
Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management.
Analysis of texture within tumours on computed tomography (CT) is emerging as a potentially useful tool in assessing prognosis and treatment response for patients with cancer. This article ...illustrates the image and histological features that correlate with CT texture parameters obtained from tumours using the filtration-histogram approach, which comprises image filtration to highlight image features of a specified size followed by histogram analysis for quantification. Computer modelling can be used to generate texture parameters for a range of simple hypothetical images with specified image features. The model results are useful in explaining relationships between image features and texture parameters. The main image features that can be related to texture parameters are the number of objects highlighted by the filter, the brightness and/or contrast of highlighted objects relative to background attenuation, and the variability of brightness/contrast of highlighted objects. These relationships are also demonstrable by texture analysis of clinical CT images. The results of computer modelling may facilitate the interpretation of the reported associations between CT texture and histopathology in human tumours. The histogram parameters derived during the filtration-histogram method of CT texture analysis have specific relationships with a range of image features. Knowledge of these relationships can assist the understanding of results obtained from clinical CT texture analysis studies in oncology.
Background
Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in ...images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer.
Purpose
To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high‐risk histological subtype) and to outcome in endometrial cancer patients.
Study type
Prospective cohort study.
Population/Subjects
In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017.
Field Strength/Sequences
Preoperative pelvic MRI including contrast‐enhanced T1‐weighted (T1c), T2‐weighted, and diffusion‐weighted imaging at 1.5T.
Assessment
Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross‐sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration‐histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated.
Statistical Tests
Associations between texture parameters and histological features were assessed by uni‐ and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis.
Results
High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio OR 3.2, P lt 0.001), and high MPP in T1c images independently predicted high‐risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T1c images predicted reduced recurrence‐ and progression‐free survival (hazard ratio HR 1.5, P lt 0.001) after adjusting for MRI‐measured tumor volume and histological risk at biopsy.
Data Conclusion
MRI‐derived tumor texture parameters independently predicted deep myometrial invasion, high‐risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer.
Level of Evidence: 2
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:1637–1647
Purpose
To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0–F4)
Methods
Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using ...commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I–III were obtained. CTTA parameters were correlated against fibrosis stage (F0–F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1–F3).
Results
The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0,
n
= 77), and patients with increasing stages of fibrosis (F1,
n
= 42; F2
n
= 37; F3
n
= 53; F4
n
= 80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71–0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3.
Conclusion
CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.
Purpose
To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT.
Materials and ...methods
One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled.
CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann–Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves.
Results
Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (
p
= 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (
p
= 0.004) and MPP (
p
= 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (
p
= 0.001).
Conclusions
Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.
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