Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.

Purpose: We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.

Materials And Methods: Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected.

[Airway remodeling assessed by high-resolution computed tomography in patients with asthma: relationship to biological markers in induced sputum].

Objective: To explore the significance of assessing asthma control by high-resolution computed tomography (HRCT) and biological markers in induced sputum.

Methods: Forty-eight patients with asthma (asthma group) and 10 healthy subjects (control group) were retrospectively analyzed. The asthma patients were divided into 4 groups based on severity: 6 with near-fatal attacks, 12 with severe, 14 with moderate and 16 with mild asthma.