BACKGROUND AND PURPOSE: Wernicke encephalopathy (WE) is a severe neurologic disorder resulting from dietary vitamin B₁ deficiency. This study was undertaken to analyze and compare MR imaging findings and neurologic manifestations at clinical presentations of patients with WE with and without a history of alcohol abuse.

BACKGROUND AND PURPOSE: Wernicke encephalopathy is a severe neurologic disorder that results from a dietary vitamin B1 deficiency. It is characterized by changes in consciousness, ocular abnormalities, and ataxia. This study was undertaken to analyze and compare findings on MR imaging and neurologic symptoms at clinical presentations of patients with Wernicke encephalopathy with and without a history of alcohol abuse.

Reggio Emilia hospital installed Picture Archiving and Communications Systems (PACS) as the final step towards a completely digital clinical environment completing the HIS/EMR and 1,400 web/terminals for patient information access. Financial benefits throughout the hospital were assessed upfront and measured periodically. Key indicators (radiology exam turnaround time, number of radiology procedures performed, inpatients length of stay before and after the PACS implementation, etc.) were analyzed and values were statistically tested to assess workflow and productivity improvements. The hospital went “filmless” in 28 weeks. Between the half of 2004 and the respective period in 2003, overall Radiology Department productivity increased by 12%, TAT improved by more than 60%. Timelier patient care resulted in decreased lengths of stay. Neurology alone experienced a 12% improvement in average patient stay. To quantify the impact of PACS on the average hospital stays and the expected productivity benefits to inpatient productivity were used a “high level” and a “detailed” business model. Annual financial upsides have exceeded $1.9 millions/year. A well-planned PACS deployment simplifies imaging workflow and improves patient care throughout the hospital while delivering substantial financial benefits. Staff buy-in was the key in this process and on-going training and process monitoring are a must.

Background: Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. Materials and Methods: We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters’ influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. Results: After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models’ best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. Conclusions: From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.