Quantifying guideline adherence in mucormycosis management using the EQUAL score.

OBJECTIVES: Mucormycosis is a difficult-to-diagnose life-threatening disease with high morbidity and mortality. Adherence to guidelines that lead through complex management and support clinical decisions is however rarely reported. By applying the EQUAL Score, our study evaluates the management of mucormycosis at the University Hospital of Cologne, Germany. METHODS: We performed a retrospective chart review of patients with mucormycosis at the University Hospital of Cologne. Data collection comprised items for quality assessment in mucormycosis management according to the EQUAL Mucormycosis Score and economics. RESULTS: Of 29 patients identified, 27 were documented retrospectively. Eight patients of 18 with neutropenia (>10 days) or receiving allogeneic stem cell transplantation (44.4%) received mould active prophylaxis. Chest CT was done in 21 patients (77.8%), while BAL and direct microscopy of BAL fluid was performed in 22 patients (81.5%), culture in 22 (81.5%) and fungal PCR in 24 (88.9%). First-line treatment was liposomal amphotericin B in 19 patients (70.4%). Isavuconazole or posaconazole with therapeutic drug monitoring was used in four (14.8%) and in one patient (3.7%), respectively. In our cohort, crude mortality was 51.9% (n = 14) with a median survival time of 113 days. During the management of the 27 patients, 450 points (53.8%) of the maximum EQUAL Mucormycosis Score were achieved (median 15 points, range 6-30). CONCLUSIONS: We observed management of mucormycosis aligning with current guidelines and hope to encourage other groups to use the EQUAL Score in routine clinical settings. Future studies will evaluate whether guideline adherence in mucormycosis management improves patient outcome.

Automated peripheral nerve segmentation for MR-neurography.

BACKGROUND: Magnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves. METHODS: A neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals. RESULTS: Mean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI]: 0.888-0.897) with a mean Jaccard index (JI) of 0.806 (95% CI: 0.799-0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI: 2.184-2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI: 0.760-0.815), mean JI of 0.672 (95% CI: 0.642-0.699), and mean HD of 2.118 (95% CI: 2.047-2.190). CONCLUSION: The deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization. RELEVANCE STATEMENT: The results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations. KEY POINTS: Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation.

Comprehensive analysis of Twitter usage during a major medical conference held virtually versus in-person.

BACKGROUND: Twitter has become one of the most important social media platforms in science communication. During scientific conferences, Twitter can facilitate the communication between audience and speakers present at the venue and can extend the reach of a conference to participants following along from home. To examine whether Twitter activity can serve as a surrogate parameter for attendance at the RSNA conferences in 2019 and in 2020, and to characterize changes in topics discussed due to the virtual character of the 2020 RSNA conference. METHODS: The Twitter API and R Studio were used to analyze the absolute number and frequency of tweets, retweets, and conference-related hashtags during the 2019 and 2020 RSNA conference. Topics of discussion were compared across years by visualizing networks of co-occurring hashtags. RESULTS: There was a 46% decrease in total tweets and a 39% decrease in individual Twitter users in 2020, mirroring a 43% decrease in registered attendees during the virtual conference. Hashtags related to social initiatives in radiology (e.g., "#radxx" and "#womeninradiology" for promoting women's empowerment in radiology or "#pinksocks," "#weareradiology" and "#diversityisgenius" for diversity in general) were less frequently used in 2020 than in 2019. CONCLUSION: Twitter and congress attendance were highly related and interpersonal topics underwent less discussion during the virtual meeting. Overall engagement during the virtual conference in 2020 was lower compared to the in-person conference in 2019.