Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review

Abstract

Background: Many 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.

Methodology: The 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 findings: Of 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.

Conclusions: In 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.

Figure 1. Results of the literature search.

PRISMA flow diagram for study collection , showing the number of studies identified, screened, eligible, and included in the systematic review. This study is registered with the PROSPERO registry for systematic reviews (Identification number: CRD42013003634) .

Figure 2. Number of publications reporting on tumor heterogeneity analysis for all publications bi-annually.

Total number of publications (A), publications per imaging modality (B), publications per analysis method (C), and publications per goal (D).

Figure 3. Publications reporting on quantification of tumor heterogeneity in cancer sites summarized for imaging modality (A), analysis method (B), and study aim (C).

Publications can report on more than one analysis method. The acronyms used: Gyn – gynecological, H&N - head and neck, GIST – gastrointestinal.

Figure 4. All included publications reporting cross-sectional (A) and longitudinal (B) studies.

Several publications report more than one analysis method.

Figure 5. Publications reporting a prospective study design cross-sectional (At) and longitudinal (B) studies.

Several publications report more than one analysis method.

Figure 6. Number of features used in classification experiments for different imaging modalities (A) and for different analysis methods (B).

Boxplot representing distribution in number selected features for imaging modality (C) and for analysis methods (D). To enhance visibility, we excluded for both boxplots two studies with large numbers of selected features.

Figure 7. The AUC for different imaging modalities (A) and for different analysis methods (B) as a function of tumor-feature ratio in the classification experiments.

The scatter plot shows each imaging modality and analysis method separately. Dotted line represents the ratio of 10 tumors per selected feature. Boxplot representing distribution in AUC for imaging modality (C) and for analysis methods (D).

Figure 8. Number of significant features before and after Holm-Bonferroni correction in publications reporting on significance testing for all image modalities (A) and all analysis methods (B).