Sonka, Milan

Objective: Vestibular schwannomas (VSs) treated with stereotactic radiosurgery (SRS) often show transient loss of central tumor enhancement. We hypothesized that this loss of enhancement may correlate with better tumor control on follow-up imaging. Methods: We evaluated 198 consecutive patients from a single center who met eligibility criteria. Tumor volumes on the pre-SRS and post-SRS scans were quantified along with regions of loss of enhancement on the first post-SRS study. The latter was correlated with tumor volumes at follow-up, radiation isocenter density, and loss of serviceable hearing. Results: For the entire cohort, the median loss of enhancement on the first post-SRS scan was 14% (IQR: 6 to 25) and median rate of change in tumor volume was −4.5% per year (IQR: −10.5 to 1.5). Percent loss of central enhancement showed significant negative correlation with tumor growth not only for the entire cohort (correlation coefficient: −0.18, P =0.01), but also for the subset of patients who had available imaging until at least 2 years (n=185) and 4 years (n=179) post-SRS. Loss of enhancement also strongly correlated positively with tumor volumes and number of radiation isocenters and negatively with isocenter density. Finally, patients with greater percent loss of enhancement had an increased likelihood of progression to non-serviceable hearing, although this association was not statistically significant. Conclusions: These findings suggest that loss of central tumor enhancement in VSs post-SRS may be associated with improved tumor control.

Abstract Aims Syncope remains a diagnostic challenge despite advancements in testing and treatment. Cardiac syncope is an independent predictor of mortality and can be difficult to distinguish from other causes of transient loss of consciousness (TLOC). This paper explores whether artificial intelligence (AI) can improve the evaluation and management of patients with syncope. Methods and results We conducted a literature review and incorporated the opinions of experts in the fields of syncope and AI. The cause of TLOC is often unclear, hospitalization criteria are ambiguous, diagnostic tests are frequently non-informative, and assessments are costly. Patients are left with unanswered questions and limited guidance. Artificial intelligence (AI) has the potential to optimize syncope evaluation by processing large data sets, detecting imperceptible patterns, and assisting clinicians. However, AI has limitations, including errors, lack of human empathy, and uncertain clinical utility. Liability issues further complicate its integration. We present three viewpoints: (i) AI is crucial for advancing syncope management; (ii) AI can enhance the patient experience; and (iii) AI in syncope care is inevitable. Conclusion Artificial intelligence may improve syncope diagnosis and management, particularly through machine learning–based test interpretation and wearable device data. However, it has yet to surpass human clinical judgment in complex decision-making. Current challenges include gaps in understanding syncope mechanisms, AI interpretability, generalizability, and clinical integration. Standardized diagnostic approaches, real-world validation, and curated data sets are essential for progress. Artificial intelligence may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.