Bianca Bignotti

Objectives: Ankle sprains represent the second most common cause of emergency department access for musculoskeletal injury and lateral ankle ligament complex tears account for 850,000 cases annually in the United States with a relapse rate of 70%. Clinical examination is limited due to its subjectivity and the difficulty of identifying a specific involvement of the ligament; therefore, US and MRI are frequently requested. Therefore, the goal of this study is to analyze the available literature on the use of ultrasound (US) and magnetic resonance imaging (MRI) to diagnose injuries to the anterior talofibular ligament (ATFL) with a meta-analytic approach. Methods: According to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, all studies regarding the diagnostic accuracy of ultrasound and magnetic resonance imaging ATFL injuries were searched and assessed. The data were obtained from two independent reviewers with 12 and 3 years of experience in meta-analysis. A QUADAS-2 (Quality Assessment of Studies of Diagnostic Accuracy Studies) checklist was carried out to assess the risk of biases. From the selected studies, the sensitivity, specificity, and accuracy data were extracted. Results: Nine studies were included. The results of the meta-analysis demonstrate a greater sensitivity for ultrasound [96.88 (95% CI: 94–99) (fixed effects); 97 (95% CI: 94–99) (random effects)] compared to MRI [88.50 (95% CI: 85–91) (fixed effects); 86.98 (95% CI: 77–94) (random effects)], p < 0.05. The result of this meta-analysis shows that the less expensive diagnostic technique is also the most sensitive for the diagnosis of ATFL tears. Ultrasound articles resulted to have non-heterogeneity [(p = 0.2816; I° = 21.4607%)]. Conclusion: This meta-analysis demonstrates that US appears to be a highly sensitive diagnostic technique for diagnosing tears of the ATFL. Compared to MRI, the sensitivity of US result was higher.

Objectives: To report final results of the MUSTT trial, which has been designed to independently compare magnetic resonance imaging (MRI) and ultrasound (US) for local recurrences of non-metastatic patients operated for malignant soft tissue tumors (STT). Methods: Magnetic resonance imaging or ultrasound in soft tissue tumors (MUSTT) is a prospective monocentric study recruiting asymptomatic, non-metastatic patients operated on for localized soft tissue sarcomas between 2015 and April. Eligible patients had MRI and physician-performed ultrasound (US) with an independent interpretation of imaging. Outcome measures were compared using ROC analysis and the X² test. An analysis of all patients was performed on a per-follow-up event basis. Results: A total of n = 51 patients who met the inclusion criteria agreed to participate. Among them, n = 8 were lost to follow-up, n = 6 had US and MRI acquired after a time frame > 7 days and were therefore excluded. Complete data available for 37 patients with 232 MRI and 232 US scan were finally considered (men/women: 18/20; age range, 18–84 years). Recurrences within 5 years occurred in 10/37 patients (27%). ROC analysis comparing US and MRI showed an AUC with 95% confidence intervals of 0.909 (0.832 to 0.981) for US and 0.966 (0.939 to 0.989) for MRI with Prob > X² = 0. Conclusions: Each of these tests detected local recurrences with suitable accuracy. MRI did not result clearly superior to US in terms of diagnostic accuracy, but US showed some false positive or negative results.

Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann–Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up.