Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.

Purpose: Radiomics-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.

Diagnostic accuracy of low-dose myocardial perfusion imaging in a real-world setting.

Background: This large-scale study analyzes factors affecting the diagnostic accuracy of low-dose myocardial perfusion imaging and correlation with coronary angiography in a real-world practice.

Methods: We compared data extracted from routine reports of (i) low-dose [Tc]sestamibi stress-MPI performed with no attenuation correction and predominantly exercise stress testing and (ii) the corresponding coronary angiography.

Results: We considered 1070 pairs of coronary angiography/stress-MPI results reported by 11 physicians.

Joint EANM/EANO/RANO/SNMMI practice guideline/procedure standard for PET imaging of brain metastases: version 1.0.

This joint practice guideline/procedure standard was collaboratively developed by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neuro-Oncology (EANO), and the PET task force of the Response Assessment in Neurooncology Working Group (PET/RANO). Brain metastases are the most common malignant central nervous system (CNS) tumors. PET imaging with radiolabeled amino acids and to lesser extent [F]FDG has gained considerable importance in the assessment of brain metastases, especially for the differential diagnosis between recurrent metastases and treatment-related changes which remains a limitation using conventional MRI.

School autonomy with accountability as a cross-national policy model: diverse adoptions, practices and impacts.

This special issue examines the policy model of School Autonomy with Accountability (SAWA) and its diverse configurations . The focus is on understanding how SAWA reforms shape governance practices and influence educational systems in different educational settings. Drawing on a range of theoretical perspectives, the issue explores how political factors, administrative traditions, and local professional frameworks shape the adoption and evolution of these policies.