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Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.

Purpose The regional quantification of amyloid burden is crucial for the clinical diagnosis of Alzheimer’s disease [1]. The best method to evaluate regional amyloid deposition in PET is through the use MR imaging for brain space normalization. However, since MR imaging is not always available in the clinical practice, a MR-less methodology is needed in order to compute semi-quantitative and analyze regional amyloid burden. Methods Forty-four patients with clinical evidence of dementia, underwent 18F-Florbetaben PET (FBB-PET), FDG-PET, neuropsychological assessment and cerebrospinal fluid analysis. We implemented a methodology that uses SPM12 to import and normalize the FBB-PET images in Montreal Neurological Institute (MNI) space without MRI, and the Automated Anatomical Labeling (AAL) atlas [2] in order to extract regional uptake from normalized FBB-PET. SUVR has been computed by using cerebellum as control region (‘ Cerebelum _ 4 _ 5 ’ of AAL atlas). We then computed Receiver Operating Characteristic (ROC) curve in order to find best thresholds for identifying clinical subgroups in our patients. Results Semi-quantitative evaluation of FBB-PET images and ROC analysis stated that SUVR value of 1,006 in the bilateral inferior frontal cortex and a SUVR of 1.03 in the precuneus region were the best cutoff (AUC 0.883 and 0.826, respectively). Box-Plot analysis showed a trend distribution of elevated SUVR levels in bilateral frontal cortex, angular girus, occipital, parietal, precuneus and paracentral lobule, among AD patients. Conclusion MR-less methodology based on Tissue Probability Map and AAL atlas provide regional quantification of amyloid burden. The ROC analysis is able to retrieve useful thresholds for the classification of AD versus non-AD thus providing a tool in clinical practice. We will perform the study on a larger sample in order to confirm the results.