Journal
RADIOLOGIA MEDICA
Volume 127, Issue 8, Pages 891-898Publisher
SPRINGER-VERLAG ITALIA SRL
DOI: 10.1007/s11547-022-01516-2
Keywords
Brain neoplasms; Magnetic resonance imaging; Glioma; Neoplasm metastasis; Perfusion; Area under curve
Funding
- Universita degli Studi G. D'Annunzio Chieti Pescara within the CRUI-CARE Agreement
Ask authors/readers for more resources
The purpose of this study was to investigate the diagnostic efficacy of MRI diagnostic algorithms in distinguishing between high-grade glioma and solitary brain metastases. The results showed that the qualitative algorithm had poor performance, while the analytical qualitative algorithm and the semi-quantitative algorithm had similar results. The data-driven quantitative algorithm achieved excellent differentiation.
Purpose To investigate the diagnostic efficacy of MRI diagnostic algorithms with an ascending automatization, in distinguishing between high-grade glioma (HGG) and solitary brain metastases (SBM). Methods 36 patients with histologically proven HGG (n = 18) or SBM (n = 18), matched by size and location were enrolled from a database containing 655 patients. Four different diagnostic algorithms were performed serially to mimic the clinical setting where a radiologist would typically seek out further findings to reach a decision: pure qualitative, analytic qualitative (based on standardized evaluation of tumor features), semi-quantitative (based on perfusion and diffusion cutoffs included in the literature) and a quantitative data-driven algorithm of the perfusion and diffusion parameters. The diagnostic yields of the four algorithms were tested with ROC analysis and Kendall coefficient of concordance. Results Qualitative algorithm yielded sensitivity of 72.2%, specificity of 78.8%, and AUC of 0.75. Analytic qualitative algorithm distinguished HGG from SBM with a sensitivity of 100%, specificity of 77.7%, and an AUC of 0.889. The semi-quantitative algorithm yielded sensitivity of 94.4%, specificity of 83.3%, and AUC = 0.889. The data-driven algorithm yielded sensitivity = 94.4%, specificity = 100%, and AUC = 0.948. The concordance analysis between the four algorithms and the histologic findings showed moderate concordance for the first algorithm, (k = 0.501, P < 0.01), good concordance for the second (k = 0.798, P < 0.01), and third (k = 0.783, P < 0.01), and excellent concordance for fourth (k = 0.901, p < 0.0001). Conclusion When differentiating HGG from SBM, an analytical qualitative algorithm outperformed qualitative algorithm, and obtained similar results compared to the semi-quantitative approach. However, the use of data-driven quantitative algorithm yielded an excellent differentiation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available