Differentiating solitary brain metastases from high-grade gliomas with MR: comparing qualitative versus quantitative diagnostic strategies

Abstract

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.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

“…Voicu et al compared four algorithms evaluating both conventional qualitative MRI features and quantitative analyses of DWI and DSC PWI parameters in NEPAs. The study confirmed that, among the quantitative parameters, the mean-normalized relative CBV and the mean CBV percentage recovery in NEPAs were more accurate than the ADC in the discrimination of HGGs from BMs, with cutoffs of 1.46 (sensitivity: 83%, specificity: 89%, AUC: 0.904) and 87.5% (sensitivity: 88.9%, specificity: 94.4%, AUC: 0.907) [98]. The quantitative algorithm based on PWI and DWI values had better diagnostic performance than the qualitative approaches, where the radiologists were asked to give the diagnosis solely on the basis of morphological information, and the semi-quantitative algorithm, where radiologists were also helped in the diagnosis by PWI and ADC values based on cutoffs derived from the literature.…”

“…The quantitative algorithm based on PWI and DWI values had better diagnostic performance than the qualitative approaches, where the radiologists were asked to give the diagnosis solely on the basis of morphological information, and the semi-quantitative algorithm, where radiologists were also helped in the diagnosis by PWI and ADC values based on cutoffs derived from the literature. Additionally, the quantitative algorithm was the algorithm with the best concordance with histologic findings [98].…”

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

“…36 patients with histologically proven HGG or solitary BM matched by size and location. [38] Study of 1 H-MRS to differentiate primary and secondary brain neoplasms.…”

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine

“…Solitary BM and GBM can exhibit quite similar MRI features, such as post-contrast ring enhancement, necrotic core, and large peritumoral edema presenting with high signal on T2-weighted and FLAIR images [ 47 ]. Differentiating these two entities is essential, considering they are the most common brain tumors in the adult population and have quite different treatments [ 47 ].…”

“…Solitary BM and GBM can exhibit quite similar MRI features, such as post-contrast ring enhancement, necrotic core, and large peritumoral edema presenting with high signal on T2-weighted and FLAIR images [ 47 ]. Differentiating these two entities is essential, considering they are the most common brain tumors in the adult population and have quite different treatments [ 47 ]. Thus, several researchers have focused on this topic, showing the advantages of multiparametric MRI [ 48 , 49 ] and, more recently, evaluating the performances of different AI-based classifiers compared to expert neuroradiologists.…”