Kenneth A. Miles

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

BackgroundTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical imagesMethodsImage texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.ResultsEarly evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.ConclusionThis review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.Teaching Points• Tumor spatial heterogeneity is an important prognostic factor.• Image texture analysis is an approach of quantifying heterogeneity.• Different methods can be applied, including statistical-, model-, and transform-based methods.• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.

AbstractPurposeTo establish the potential for tumour heterogeneity in non-small cell lung cancer (NSCLC) as assessed by CT texture analysis (CTTA) to provide an independent marker of survival for patients with NSCLC.Materials and methodsTumour heterogeneity was assessed by CTTA of unenhanced images of primary pulmonary lesions from 54 patients undergoing 18F-fluorodeoxyglucose (FDG) PET-CT for staging of NSCLC. CTTA comprised image filtration to extract fine, medium and coarse features with quantification of the distribution of pixel values (uniformity) within the filtered images. Receiver operating characteristics identified thresholds for PET and CTTA parameters that were related to patient survival using Kaplan-Meier analysis.ResultsThe median (range) survival was 29.5 (1–38) months. 24, 10, 14 and 6 patients had tumour stages I, II, III and IV respectively. PET stage and tumour heterogeneity assessed by CTTA were significant independent predictors of survival (PET stage: Odds ratio 3.85, 95% confidence limits 0.9–8.09, P = 0.002; CTTA: Odds ratio 56.4, 95% confidence limits 4.79–666, p = 0.001). SUV was not a significantly associated with survival.ConclusionAssessment of tumour heterogeneity by CTTA of non-contrast enhanced images has the potential for to provide a novel, independent predictor of survival for patients with NSCLC.Key Points• Computed tomography is a routine staging procedure in non-small cell lung cancer • CT texture analysis (CTTA) can quantify heterogeneity within these lung tumours • CTTA seems to offer a novel independent predictor of survival for NSCLC • CTTA could contribute to disease risk-stratification for patients with NSCLC

Assessment of Response to Tyrosine Kinase Inhibitors in Metastatic Renal Cell Cancer: CT Texture as a Predictive Biomarker

PURPOSE To assess changes in tumor computed tomographic (CT) texture after two cycles of treatment with tyrosine kinase inhibitors (TKIs) and to determine if tumor texture correlates with measured time to progression in patients with metastatic renal cell cancer who received TKIs. MATERIALS AND METHODS A waiver of institutional review board approval was obtained for this retrospective analysis. Contrast material-enhanced CT texture parameters were assessed in 39 patients with metastatic renal cell cancer who received a TKI. A total of 87 metastases were analyzed at baseline and after two treatment cycles. Changes in tumor entropy and uniformity were derived with a software algorithm that selectively filters and extracts texture at different scales (fine to coarse detail: 1.0-2.5) and were recorded. Response assessment was also obtained by using response evaluation criteria in solid tumors (RECIST), as well as Choi and modified Choi criteria. The correlation of texture parameters and standard criteria with measured time to progression was assessed by using Kaplan-Meier analysis and a Cox regression model. Statistical significance was set at 5%. RESULTS Tumor entropy decreased by 3%-45% and uniformity increased by 5%-21% for the different scale values after administration of a TKI. With a threshold change of -2% or less for uniformity at a coarse scale value of 2.5, Kaplan-Meier curves of the proportion of patients without disease progression were significantly different and better than those for standard response assessment (P = .008 vs P = .267, P = .053, and P = .042 for RECIST, Choi, and modified Choi criteria, respectively). Cox regression analysis showed that texture uniformity was an independent predictor of time to progression (odds ratio, 4.02; 95% confidence interval: 1.52, 10.65; P = .005). CONCLUSION CT texture analysis reflecting tumor heterogeneity is an independent factor associated with time to progression and has potential as a predictive imaging biomarker of response of metastatic renal cancer to targeted therapy.

Assessment of Primary Colorectal Cancer Heterogeneity by Using Whole-Tumor Texture Analysis : Contrast-enhanced CT Texture as a Biomarker of 5-year Survival

PURPOSE To determine if computed tomographic (CT) texture features of primary colorectal cancer are related to 5-year overall survival rate. MATERIALS AND METHODS 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. RESULTS 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). CONCLUSION Fine-texture features are associated with poorer 5-year overall survival rate in patients with primary colorectal cancer. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120254/-/DC1.

Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage

Abstract The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = −0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: rs = 0.71, p = 0.001; E: rs = 0.55, p = 0.02; U: rs = −0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT.

Non–Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT

PURPOSE To correlate computed tomographic (CT) texture in non-small cell lung cancer (NSCLC) with histopathologic markers for angiogenesis and hypoxia. MATERIALS AND METHODS 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. RESULTS 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). CONCLUSION Texture parameters derived from CT images of NSCLC have the potential to act as imaging correlates for tumor hypoxia and angiogenesis. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112428/-/DC1.

Multiparametric Imaging of Tumor Response to Therapy

There is an increasing opportunity to perform multifunctional imaging at a variety of organ sites with relatively short examination times. Each technique yields quantitative parameters that reflect specific aspects of the underlying tumor or tissue biology. Many biomarkers have emerged that provide unique information on tumor behavior, including response to treatment. The multiparametric approach combines the information from different functional imaging techniques; this goes beyond what can be achieved by using any single functional technique, thus allowing an improved understanding of biologic processes and of responses to therapeutic interventions. Multiparametric imaging has many potential clinical roles; it is useful for pharmaceutical drug development and for predicting therapeutic efficacy.

Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival

AIM To undertake a pilot study assessing whether tumour heterogeneity evaluated using computed tomography texture analysis (CTTA) has the potential to provide a marker of tumour aggression and prognosis in oesophageal cancer. MATERIALS AND METHODS In 21 patients, unenhanced CT images of the primary oesophageal lesion obtained using positron-emission tomography (PET)-CT examinations underwent CTTA. CTTA was carried out using a software algorithm that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features) with quantification as entropy and uniformity (measures image heterogeneity). Texture parameters were correlated with average tumour 2-[(18)F]-fluoro-2-deoxy-d-glucose (FDG) uptake [standardized uptake values (SUV(mean) and SUV(max))] and clinical staging as determined by endoscopic ultrasound (nodal involvement) and PET-CT (distant metastases). The relationship between tumour stage, FDG uptake, and texture with survival was assessed using Kaplan-Meier analysis. RESULTS Tumour heterogeneity correlated with SUV(max) and SUV(mean). The closest correlations were found for SUV(mean) measured as uniformity and entropy with coarse filtration (r=-0.754, p<0.0001; and r=0.748, p=0.0001 respectively). Heterogeneity was also significantly greater in patients with clinical stage III or IV for filter values between 1.0 and 2.0 (maximum difference at filter value 1.5: entropy: p=0.027; uniformity p=0.032). The median (range) survival was 21 (4-34) months. Tumour heterogeneity assessed by CTTA (coarse uniformity) was an independent predictor of survival [odds ratio (OR)=4.45 (95% CI: 1.08, 18.37); p=0.039]. CONCLUSION CTTA assessment of tumour heterogeneity has the potential to identify oesophageal cancers with adverse biological features and provide a prognostic indicator of survival.

Colorectal Cancer: Texture Analysis of Portal Phase Hepatic CT Images as a Potential Marker of Survival

PURPOSE To assess the utility of texture analysis of liver computed tomographic (CT) images by determining the effect of acquisition parameters on texture and by comparing the abilities of texture analysis and hepatic perfusion CT to help predict survival for patients with colorectal cancer. MATERIALS AND METHODS The study comprised a phantom test and a clinical evaluation of 48 patients with colorectal cancer who had consented to retrospective analysis of hepatic perfusion CT data acquired during a research study approved by the institutional review board. Both components involved texture analysis to quantify the relative contribution of CT features between 2 and 12 pixels wide to overall image brightness and uniformity. The effect of acquisition factors on texture was assessed on CT images of a cylindric phantom filled with water obtained by using tube currents between 100 and 250 mAs and voltages between 80 and 140 kVp. Texture on apparently normal portal phase CT images of the liver and hepatic perfusion parameters were related to patient survival by using Kaplan-Meier survival analysis. RESULTS A texture parameter that compared the uniformity of distribution of CT image features 10 and 12 pixels wide exhibited the least variability with CT acquisition parameters (maximum coefficient of variation, 2.6%) and was the best predictor of patient survival (P < .005). There was no significant association between survival and hepatic perfusion parameters. CONCLUSION The study provides preliminary evidence that analysis of liver texture on portal phase CT images is potentially a superior predictor of survival for patients with colorectal cancer than CT perfusion imaging. SUPPLEMENTAL MATERIAL http://radiology.rsnajnls.org/cgi/content/full/2502071879/DC1.

Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography

AbstractDynamic contrast-enhanced computed tomography (DCE-CT) assesses the vascular support of tumours through analysis of temporal changes in attenuation in blood vessels and tissues during a rapid series of images acquired with intravenous administration of iodinated contrast material. Commercial software for DCE-CT analysis allows pixel-by-pixel calculation of a range of validated physiological parameters and depiction as parametric maps. Clinical studies support the use of DCE-CT parameters as surrogates for physiological and molecular processes underlying tumour angiogenesis. DCE-CT has been used to provide biomarkers of drug action in early phase trials for the treatment of a range of cancers. DCE-CT can be appended to current imaging assessments of tumour response with the benefits of wide availability and low cost. This paper sets out guidelines for the use of DCE-CT in assessing tumour vascular support that were developed using a Delphi process. Recommendations encompass CT system requirements and quality assurance, radiation dosimetry, patient preparation, administration of contrast material, CT acquisition parameters, terminology and units, data processing and reporting. DCE-CT has reached technical maturity for use in therapeutic trials in oncology. The development of these consensus guidelines may promote broader application of DCE-CT for the evaluation of tumour vascularity. Key Points • DCE-CT can robustly assess tumour vascular support • DCE-CT has reached technical maturity for use in therapeutic trials in oncology • This paper presents consensus guidelines for using DCE-CT in assessing tumour vascularity