Abstract
PurposeA number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We report a systematic review of the type-I error inflation in such studies and review the evidence regarding associations between patient outcome and texture features derived from positron emission tomography (PET) or computed tomography (CT) images.MethodsFor study identification PubMed and Scopus were searched (1/2000–9/2013) using combinations of the keywords texture, prognostic, predictive and cancer. Studies were divided into three categories according to the sources of the type-I error inflation and the use or not of an independent validation dataset. For each study, the true type-I error probability and the adjusted level of significance were estimated using the optimum cut-off approach correction, and the Benjamini-Hochberg method. To demonstrate explicitly the variable selection bias in these studies, we re-analyzed data from one of the published studies, but using 100 random variables substituted for the original image-derived indices. The significance of the random variables as potential predictors of outcome was examined using the analysis methods used in the identified studies.ResultsFifteen studies were identified. After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34–99%) was estimated with the majority of published results not reaching statistical significance. Only 3/15 studies used a validation dataset. For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to ROC and multiple hypothesis testing analysis.ConclusionsWe found insufficient evidence to support a relationship between PET or CT texture features and patient survival. Further fit for purpose validation of these image-derived biomarkers should be supported by appropriate biological and statistical evidence before their association with patient outcome is investigated in prospective studies.
Highlights
This is an exciting era for imaging biomarkers
For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to receiver operating characteristic (ROC) and multiple hypothesis testing analysis
We found insufficient evidence to support a relationship between positron emission tomography (PET) or computed tomography (CT) texture features and patient survival
Summary
This is an exciting era for imaging biomarkers. Fast computing and state of the art software has facilitated the collection and analysis of large amounts of data, while the development of data mining techniques enables researchers to test a large number of hypotheses simultaneously. The most commonly used metrics currently applied to positron emission tomography (PET) images are the standardised uptake value (SUV) derived indices These include SUVmax, the voxel with the maximum activity concentration in the tumour; SUVmean, calculated by averaging the activity concentration in all voxels inside a tumour volume; SUVpeak, calculated by averaging the voxel values inside a small region of interest centred on the SUVmax; the metabolically active tumour volume (MTV), and total lesion glycolysis (TLG), which is the product of MTV and the SUVmean. The application of image classification techniques to PET and CT images has resulted in a new family of indices [2,3], known as texture features, that have been used to characterise tumour heterogeneity
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