Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine learning (ML) offers a novel approach, leveraging advanced algorithms to analyze complex imaging data with high precision. A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Google Scholar identified eligible studies. The sensitivity, specificity, accuracy, precision, F1 score, and the (area under the curve) AUC items were extracted from the included studies. After screening 1077 records, seven studies met the eligibility criteria for the systematic review, of which five were included in the meta-analysis. ML algorithm, particularly Support Vector Machines (SVM), demonstrated promising performance. A meta-analysis of five studies revealed a pooled sensitivity of 0.95 (95% CI: 0.84-0.99) and specificity of 0.80 (95% CI: 0.69-0.88). Additionally, the positive diagnostic likelihood ratio (DLR) was 4.75 (95% CI: 2.91-7.76), the negative DLR was 0.06 (95% CI: 0.02-0.21), and the diagnostic odds ratio was 80.97 (95% CI: 17.5-374.61). The diagnostic score was calculated as 4.39 (95% CI: 2.86-5.93), and the AUC was 0.86 (95% CI: 0.83-0.89). Subgroup analyses showed SVM-based models with higher sensitivity (0.98 vs. 0.87) and specificity (0.82 vs. 0.77) than non-SVM (p = 0.13). Comparing glioblastoma and Grade 3 tumors, sensitivities were 94 % vs. 97 %, and specificities were 79 % vs. 83 %, with no significant heterogeneity. These findings suggest that ML models, particularly SVM, offer promising diagnostic performance in distinguishing true tumor recurrence from treatment-related changes.

Ischemic stroke, a leading cause of mortality, necessitates understanding its mechanism for effective prevention. Echocardiography, especially transesophageal echocardiography (TEE), is the gold standard for detection of cardiac sources of stroke including left atrial thrombus, although its invasiveness, operator skill dependence, and limited availability in some centers prompt exploration of alternatives, such as cardiac CT (CCT). We conducted a systematic review and meta-analysis assessing the ability of CCT in the detection of intracardiac thrombus compared with echocardiography. We searched 4 databases up through September 8, 2023. Major search terms included a combination of the terms "echocardiograph," "CT," "TEE," "imaging," "stroke," "undetermined," and "cryptogenic." The current systematic literature review of the English language literature was reported in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. We assessed risk of bias using the QUADAS-2 tool and used random-effects meta-analysis to calculate different diagnostic metrics. The meta-analysis investigating CCT vs echocardiography for intracardiac thrombus detection yielded a total of 43 studies of 9,552 patients. Risk-of-bias assessment revealed a predominantly low risk of bias in the flow and timing, index test, and patient selection domains and a predominantly unclear risk of bias in the reference standard domain. The analysis revealed an overall sensitivity of 98.38% (95% CI 89.2-99.78) and specificity of 96.0% (95% CI 92.55-97.88). Subgroup analyses demonstrated that delayed-phase, electrocardiogram-gated CCT had the highest sensitivity (100%; 95% CI 0-100) while early-phase, nongated CCT exhibited a sensitivity of 94.31% (95% CI 28.58-99.85). The diagnostic odds ratio was 98.59 (95% CI 44.05-220.69). Heterogeneity was observed, particularly in specificity and diagnostic odds ratio estimates. CCT demonstrates high sensitivity, specificity, and diagnostic odds ratios in detecting intracardiac thrombus compared with traditional echocardiography. Limitations include the lack of randomized controlled studies, and other cardioembolic sources of stroke such as valvular disease, cardiac function, and aortic arch disease were not examined in our analysis. Large-scale studies are warranted to further evaluate CCT as a promising alternative for identifying intracardiac thrombus and other sources of cardioembolic stroke.