Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis

Pseudoprogression of brain tumors

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Reporting bias in imaging: higher accuracy is linked to faster publication

OBJECTIVES

The objective of this study was to evaluate whether higher reported accuracy estimates are associated with shorter time to publication among imaging diagnostic accuracy studies.

METHODS

We included primary imaging diagnostic accuracy studies, included in meta-analyses from systematic reviews published in 2015. For each primary study, we extracted accuracy estimates, participant recruitment periods and publication dates. Our primary outcome was the association between Youden's index (sensitivity + specificity - 1, a single measure of diagnostic accuracy) and time to publication.

RESULTS

We included 55 systematic reviews and 781 primary studies. Study completion dates were missing for 238 (30%) studies. The median time from completion to publication in the remaining 543 studies was 20 months (IQR 14-29). Youden's index was negatively correlated with time from completion to publication (rho = -0.11, p = 0.009). This association remained significant in multivariable Cox regression analyses after adjusting for seven study characteristics: hazard ratio of publication was 1.09 (95% CI 1.03-1.16, p = 0.004) per unit increase for logit-transformed estimates of Youden's index. When dichotomizing Youden's index by a median split, time from completion to publication was 20 months (IQR 13-33) for studies with a Youden's index below the median, and 19 months (14-27) for studies with a Youden's index above the median (p = 0.104).

CONCLUSION

Imaging diagnostic accuracy studies with higher accuracy estimates were weakly associated with a shorter time to publication.

KEY POINTS

• Higher accuracy estimates are weakly associated with shorter time to publication. • Lag in time to publication remained significant in multivariate Cox regression analyses. • No correlation between accuracy and time from submission to publication was identified.

Multiparametric MRI as a potential surrogate endpoint for decision-making in early treatment response following concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma: a systematic review and meta-analysis

OBJECTIVE

To evaluate the value of multiparametric MRI for determination of early treatment response following concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma.

METHODS

A computerized search of Ovid-MEDLINE and EMBASE up to 1 October 2017 was performed to find studies on the diagnostic performance of multiparametric MRI for differentiating true progression from pseudoprogression. The beginning search date was not specified. Pooled estimates of sensitivity and specificity were obtained using hierarchical logistic regression modeling. We performed meta-regression and sensitivity analyses to explain the effects of the study heterogeneity.

RESULTS

Nine studies including 456 patients were included. Pooled sensitivity and specificity were 84 % (95 % CI 74-91) and 95 % (95 % CI 83-99), respectively. Area under the hierarchical summary receiver operating characteristic curve was 0.95 (95 % CI 0.92-0.96). Meta-regression showed true progression in the study population, the mean age and the reference standard were significant factors affecting heterogeneity.

CONCLUSION

Multiparametric MRI may be used as a potential surrogate endpoint for assessment of early treatment response, especially in the differentiation of true progression from pseudoprogression. However, based on the current evidence, monoparametric and multiparametric MRI perform equally in the clinical context. Further evaluation will be needed.

KEY POINTS

• Multiparametric MRI shows high diagnostic performance for early treatment response in glioblastoma. • Multiparametric MRI could differentiate true progression from pseudoprogression in newly diagnosed glioblastoma. • The normalized rCBV derived from DSC was the most commonly used parameter.