Moraes, Fábio Ynoe de

BACKGROUND AND OBJECTIVES: Meningiomas are the most common primary central nervous system tumors, with cavernous sinus meningiomas (CSMs) making up a small fraction (1% of intracranial tumors). CSMs are challenging to treat due to their location and potential invasion. Therapy methods have shifted from aggressive resections to less invasive techniques such as stereotactic radiosurgery, reducing morbidity and improving survival. This study reviews the effectiveness and safety of radiosurgery for CSMs, blending historical and contemporary approaches to guide future treatments. METHODS: A search was performed in MEDlINE, Embase, and Cochrane databases, following Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Eligible studies included randomized or observational studies with ≥4 patients reporting on radiosurgery for CSM. The random-effects model was used to calculate a single proportion analysis with 95% CIs. Statistical analyses were performed using RStudio. RESULTS: Twenty seven studies, encompassing 1851 patients, were included in this analysis, with a population median age of 27 years. Outcomes were assessed as follows: the 10-year progression-free survival rate was 87% (323 patients; 95% CI: 73%-100%; I 2 = 89.6%), clinical deterioration occurred in 6% of cases (957 patients; 95% CI: 3%-8%; I 2 = 71.3%), clinical improvement was observed in 35% of patients (1106 patients; 95% CI: 27%-43%; I 2 = 86.6%), major complications rate was 4% (688 patients; 95% CI: 1%-6%; I 2 = 71.8%), and minor complications was 2% (653 patients; 95% CI: 0%-3%; I 2 = 68.6%). CONCLUSION: This study reveals stereotactic radiosurgery as an effective treatment of CSMs, showing high long-term progression-free survival and significant tumor control with few complications.

Objectives Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles. Design and setting International online Delphi study. Participants International experts in medicine and artificial intelligence. Main outcome measures The primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research). Results The development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool’s role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions. Conclusion GAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.

Objective Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. Methods PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. Results Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655–0.711, I² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882–0.979, I² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684–0.723, I² = 97.5%, p < 0.01). Conclusions The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources.