Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing ‘translational gaps’ through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored ‘roadmap’. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

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.

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.

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FRCR 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 20.38 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.q RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup /suppl

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112428/-/DC1.

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.

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.