Anti-PD-1/PD-L1 inhibitor therapy for melanoma brain metastases: a systematic review and meta-analysis.

Melanoma brain metastases present a major challenge in cancer treatment and reduce overall survival despite advances in managing primary melanoma. Immune checkpoint inhibitors (ICIs) that target PD-1/PD-L1 pathways have shown promise in treating advanced melanoma, but their efficacy for melanoma brain metastases is debated. This systematic review and meta-analysis summarize evidence on anti-PD-1/PD-L1 inhibitors for melanoma brain metastases. This systematic review and meta-analysis followed PRISMA guidelines. PICO criteria targeted melanoma brain metastasis patients treated with PD-1/PD-L1 inhibitors, assessing overall survival, progression-free survival, and complications. Inclusion criteria were English studies on humans using PD-1/PD-L1 inhibitors for melanoma brain metastases with > 10 patients. A total of 22 trials involving 1523 melanoma brain metastase patients treated with anti-PD-1/PD-L1 inhibitors were thoroughly analyzed. Our findings show the 6-month OS rate of 0.75 [95%CI:0.67-0.84], the 6-months PFS rate of 0.42 [95%CI:0.31-0.52], the 1-year OS rate of 0.63 [95%CI:0.52-0.74], the 1-year PFS rate was 0.45 [95%CI:0.32-0.58], the 18-months OS rate of 0.52 [95%CI:0.37-0.67], the 2-year OS rate of 50% [95% CI: (34%-65%)], the 2 year PFS rate of 0.36 (95%CI:0.23-0.50), the 3-year OS rate of 0.42 (95%CI:0.17-0.67), the 4-year PFS rate of 0.35 [95%CI:0.08-0.61], the 4-year OS rate of 0.29 [95%CI:0.01-0.56], the 5-year OS rate of 0.29 (95%CI:0.09-0.50), and the 5-year PFS rate of 0.11 (95%CI:0.03-0.19). The combined disease stability rate was 0.13 [95%CI:0.05-0.20], the progressive disease rate was 0.49 [95%CI:0.37-0.62], the partial response rate was 0.14 [95%CI:0.07-0.20], the object response rate was 0.35 [95%CI:0.24-0.46], and the complete response rate was 0.22 [95%CI:0.12-0.32]. In conclusion, our meta-analysis provides compelling evidence supporting the efficacy of PD-1/PD-L1 inhibitors in patients with melanoma brain tumors, as evidenced by favorable survival outcomes and disease control rates.

Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants.

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.