Antonio Di Ieva

This study evaluates three cell counting assays in 2D and 3D in vitro models, finding that 2D cultures yield more precise and reliable results than 3D ECM-based cultures.

This paper applies fractal analysis to MALDI-TOF MS data for glioblastoma diagnosis, showing fractals can improve machine learning models but still underperform compared to traditional feature extraction.

This study uses MALDI-TOF MS and machine learning to identify serum biomarkers for astrocytoma diagnosis, achieving 94.5% accuracy in distinguishing GBM from healthy controls.

This paper develops a radiomics pipeline using fractal analysis and statistical methods to characterize gliomas and brain metastases via susceptibility-weighted imaging.