Simulation to become a better neurosurgeon. An international prospective controlled trial: The Passion study.

Introduction: Surgical training traditionally adheres to the apprenticeship paradigm, potentially exposing trainees to an increased risk of complications stemming from their limited experience. To mitigate this risk, augmented and virtual reality have been considered, though their effectiveness is difficult to assess. Research question: The PASSION study seeks to investigate the improvement of manual dexterity following intensive training with neurosurgical simulators and to discern how surgeons' psychometric characteristics may influence their learning process and surgical performance. Material and methods: Seventy-two residents were randomized into the simulation group (SG) and control group (CG). The course spanned five days, commencing with assessment of technical skills in basic procedures within a wet-lab setting on day 1. Over the subsequent core days, the SG engaged in simulated procedures, while the CG carried out routine activities in an OR. On day 5, all residents' technical competencies were evaluated. Psychometric measures of all participants were subjected to analysis. Results: The SG demonstrated superior performance (p < 0.0001) in the brain tumour removal compared to the CG. Positive learning curves were evident in the SG across the three days of simulator-based training for all tumour removal tasks (all p-values <0.05). No significant differences were noted in other tasks, and no meaningful correlations were observed between performance and any psychometric parameters. Discussion and conclusion: A brief and intensive training regimen utilizing 3D virtual reality simulators enhances residents' microsurgical proficiency in brain tumour removal models. Simulators emerge as a viable tool to expedite the learning curve of in-training neurosurgeons.

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages of the diagnostic and therapeutic process. Although the histological investigation will remain difficult to replace, in the near future the radiomic approach will allow a complementary, repeatable and non-invasive characterization of the lesion, assisting oncologists and neurosurgeons in selecting the best therapeutic option and the correct molecular target in chemotherapy. AI-driven tools are already playing an important role in surgical planning, delimiting the extent of the lesion (segmentation) and its relationships with the brain structures, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life. Finally, AI-assisted models allow the prediction of complications, recurrences and therapeutic response, suggesting the most appropriate follow-up. Looking to the future, AI-powered models promise to integrate biochemical and clinical data to stratify risk and direct patients to personalized screening protocols.

Filling the gap between the OR and virtual simulation: a European study on a basic neurosurgical procedure.

BACKGROUND: Currently available simulators are supposed to allow young neurosurgeons to hone their technical skills in a safe environment, without causing any unnecessary harm to their patients caused by their inexperience. For this training method to be largely accepted in neurosurgery, it is necessary to prove simulation efficacy by means of large-scale clinical validation studies. METHODS: We correlated and analysed the performance at a simulator and the actual operative skills of different neurosurgeons (construct validity). We conducted a study involving 92 residents and attending neurosurgeons from different European Centres; each participant had to perform a virtual task, namely the placement of an external ventricular drain (EVD) at a neurosurgical simulator (ImmersiveTouch). The number of attempts needed to reach the ventricles and the accuracy in positioning the catheter were assessed. RESULTS: Data suggests a positive correlation between subjects who placed more EVDs in the previous year and those who get better scores at the simulator (p = .008) (fewer attempts and better surgical accuracy). The number of attempts to reach the ventricle was also analysed; senior residents needed fewer attempts (mean = 2.26; SD = 1.11) than junior residents (mean = 3.12; SD = 1.05) (p = .007) and staff neurosurgeons (mean = 2.89, SD = 1.23). Scoring results were compared by using the Fisher's test, for the analysis of the variances, and the Student's T test. Surprisingly, having a wider surgical experience overall does not correlate with the best performance at the simulator. CONCLUSION: The performance of an EVD placement on a simulator correlates with the density of the neurosurgical experience for that specific task performed in the OR, suggesting that simulators are able to differentiate neurosurgeons according to their surgical ability. Namely this suggests that the simulation performance reflects the surgeons' consistency in placing EVDs in the last year.