ABSTRACT
BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy of iMRI and 5-aminolevulinic acid (5-ALA), questioning the continued justification of iMRI because of its associated costs and extended surgical duration. Nonetheless, drawing from our clinical observations, we postulated that a subset of intricate HGGs may continue to benefit from the adjunctive application of iMRI. METHODS: In a prospective study of 73 patients with HGG, 5-ALA was the primary technique for tumor delineation, complemented by iMRI to detect residual contrast-enhanced regions. Suboptimal 5-ALA efficacy was defined when (1) iMRI detected contrast-enhanced remnants despite 5-ALA's indication of a gross total resection or (2) surgeons observed residual fluorescence, contrary to iMRI findings. Radiomic features from preoperative MRIs were extracted using a U2-Net deep learning algorithm. Binary logistic regression was then used to predict compromised 5-ALA performance. RESULTS: Resections guided solely by 5-ALA achieved an average removal of 93.14% of contrast-enhancing tumors. This efficacy increased to 97% with iMRI integration, albeit not statistically significant. Notably, for tumors with suboptimal 5-ALA performance, iMRI's inclusion significantly improved resection outcomes (P-value: .00013). The developed deep learning-based model accurately pinpointed these scenarios, and when enriched with radiomic parameters, showcased high predictive accuracy, as indicated by a Nagelkerke R2 of 0.565 and a receiver operating characteristic of 0.901. CONCLUSION: Our machine learning-driven radiomics approach predicts scenarios where 5-ALA alone may be suboptimal in HGG surgery compared with its combined use with iMRI. Although 5-ALA typically yields favorable results, our analyses reveal that HGGs characterized by significant volume, complex morphology, and left-sided location compromise the effectiveness of resections relying exclusively on 5-ALA. For these intricate cases, we advocate for the continued relevance of iMRI.
ABSTRACT
OBJECTIVE: Non-valvular atrial fibrillation (NVAF) is a major risk factor for ischemic stroke (IS) and a powerful predictor of mortality. This study investigates early and long-term outcome among patients with IS secondary to NVAF and identify the main factors associated with poor outcome, recurrence, and death. METHODS: We analyzed the data from our consecutive NVAF acute IS database, over a period of 23 years. The endpoints were bad outcome (Modified Rankin Score ≥3), recurrence, and mortality at discharge, after 6 months, 12 months, and final follow-up. Multivariate Cox and Kaplan-Meier analysis were used to estimate the probability of death. RESULTS: 129 consecutive acute IS patients were included (77 [59.7%] females, mean age 70.2 ± 10.1 years). Discharge, 6 and 12 months bad outcome was 62%, 63%, and 61%, respectively. After a median follow-up of 17 months (IQR 6-54.5), 35.6% patients had bad outcome, 21.7% had recurrence and 36.4% died. The recurrence and death annual rates were 19.1% and 6.32%. The absence of oral anticoagulation (OAC) and NIHSS score > 12 were the strongest predictors of mortality. CONCLUSIONS: IS secondary to NVAF has a high rate of stroke recurrence and mortality in our population, with the absence of OAC and major stroke as the main risk factors.
