ArticleEuropean journal of nuclear medicine and molecular imaging2025
Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.
Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999-2025].Journal of thoracic disease · 2026Article
- Reducing acquisition time in O-(2-Quantitative imaging in medicine and surgery · 2025Article
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23 authors.
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Abstract
purposeRadiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study investigated whether an explainable radiomics model modifies nuclear physicians' assessment of glioma aggressiveness at diagnosis.
methodsPatients underwent dynamic 6-[
resultsEighty-five patients (54[39-62] years old, 41 women) were selected. In the second phase, physicians demonstrated a significant improvement in diagnostic accuracy compared to the first phase (0.775 [0.750-0.802] vs. 0.717 [0.694-0.737], p = 0.007). The explainable radiomics model augmented physician agreement, with a 22.72% increase in Fleiss's kappa, and significantly enhanced physician confidence (p < 0.001). Among all physicians, Anchor and SHAP showed efficacy in 75% and 72% of cases, respectively, outperforming LIME (p ≤ 0.001).
conclusionsOur results highlight the potential of an explainable radiomics model using amino acid PET scans as a diagnostic support to assist physicians in identifying glioma aggressiveness.
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