Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review

Abstract

BackgroundMany techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice.MethodologyThe Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared.Principal FindingsOf the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description.ConclusionsIn a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis

“…Texture analysis provides a quantitative and objective method to assess the intralesional heterogeneity on medical images (21). The advantage of imaging intralesional heterogeneity is its noninvasiveness and the fact that it can evaluate overall lesion spatial complexity or identify the subregions reflecting unique, spatially explicit biological processes, whereas random sampling or biopsy is invasive and limited to a local extent of the entire lesion (22,23). Chest CT texture analysis is emerging as a useful technique for assessing the properties of several pulmonary diseases including pulmonary nodules (10,11,(24)(25)(26).…”

Impact of Contrast-Enhanced Intraoperative Ultrasound on Operation Strategy in Case of Colorectal Liver Metastasis

“…Texture analysis may provide important information for the determination of the optimal treatment approach. However, the optimal texture parameter and its clinical impact have yet to be determined [140,141]. Initial results from our institution have shown promising results predicting early recurrence in patients with CRLM prior liver resection (CT based Figure 10) or for the discrimination of low grade and high grade gliomas (MRI based Figure 11) [142,143].…”

LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity

“…Recently, there has been renewed interest in texture analysis in the context of tumor imaging (1), assuming that textural features could reflect intratumor heterogeneity, which is known to have important implications in cancer research (2). Texture analysis is also a key component of radiomics (3).…”