ArticleLa Radiologia medica2022
Differentiating solitary brain metastases from high-grade gliomas with MR: comparing qualitative versus quantitative diagnostic strategies.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Metal-based nanomaterials achieve tumor treatment strategies by overcoming the blood-brain barrier of glioma and responding to the tumor microenvironment.Materials today. Bio · 2026Review
- Quantitative MRI in Neuroimaging: A Review of Techniques, Biomarkers, and Emerging Clinical Applications.Brain sciences · 2025Review
- Review
- Immune response recalibration using immune therapy and biomimetic nano-therapy against high-grade gliomas and brain metastases.Asian journal of pharmaceutical sciences · 2025Review
- Bi-exponential diffusion-weighted imaging for differentiating high-grade gliomas from solitary brain metastases: a VOI-based histogram analysis.Scientific reports · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Review
- Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.NPJ precision oncology · 2024Review
- Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?European radiology · 2024Article
- Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool.La Radiologia medica · 2023Article
- Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor.Cancers · 2023Review
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
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6 authors.
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Abstract
purposeTo investigate the diagnostic efficacy of MRI diagnostic algorithms with an ascending automatization, in distinguishing between high-grade glioma (HGG) and solitary brain metastases (SBM).
methods36 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.
resultsQualitative 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).
conclusionWhen 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.
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