Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis.

Abstract

CONCLUSION

Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers.

OBJECTIVES

To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection.

RESULTS

Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively).

KEY POINTS

MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.

CLINICAL RELEVANCE STATEMENT

In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments.

MATERIALS AND METHODS

A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated.