Classification of amyloid status using machine learning with histograms of oriented 3D gradients

AbstractBrain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 133 18F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 209 11C-PiB images and 128 18F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy

Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning

Purpose!#!In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers.!##!Methods!#!In 173 subjects imaged with !##!Results!#!In the development set, including !##!Conclusion!#!The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-bod

Influence of agglomeration and specific lung lining lipid/protein interaction on short-term inhalation toxicity

<p>Lung lining fluid is the first biological barrier nanoparticles (NPs) encounter during inhalation. As previous inhalation studies revealed considerable differences between surface functionalized NPs with respect to deposition and toxicity, our aim was to investigate the influence of lipid and/or protein binding on these processes. Thus, we analyzed a set of surface functionalized NPs including different SiO₂ and ZrO₂ in pure phospholipids, CuroSurf^TM and purified native porcine pulmonary surfactant (nS). Lipid binding was surprisingly low for pure phospholipids and only few NPs attracted a minimal lipid corona. Additional presence of hydrophobic surfactant protein (SP) B in CuroSurf^TM promoted lipid binding to NPs functionalized with Amino or PEG residues. The presence of the hydrophilic SP A in nS facilitated lipid binding to all NPs. In line with this the degree of lipid and protein affinities for different surface functionalized SiO₂ NPs in nS followed the same order (SiO₂ Phosphate ∼ unmodified SiO₂ < SiO₂ PEG < SiO₂ Amino NPs). Agglomeration and biomolecule interaction of NPs in nS was mainly influenced by surface charge and hydrophobicity. Toxicological differences as observed in short-term inhalation studies (STIS) were mainly influenced by the core composition and/or surface reactivity of NPs. However, agglomeration in lipid media and lipid/protein affinity appeared to play a modulatory role on short-term inhalation toxicity. For instance, lipophilic NPs like ZrO₂, which are interacting with nS to a higher extent, exhibited a far higher lung burden than their hydrophilic counterparts, which deserves further attention to predict or model effects of respirable NPs.</p