Clinical utility of arterial spin labeling for preoperative grading of glioma

Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis

BACKGROUND

Bone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologists in the detection of BM. This meta-analysis was undertaken to evaluate the AI diagnostic accuracy for BM.

METHODS

Two reviewers performed an exhaustive search of several databases, including Wei Pu (VIP) database, China National Knowledge Infrastructure (CNKI), Web of Science, Cochrane Library, Ovid-Embase, Ovid-Medline, Wan Fang database, and China Biology Medicine (CBM), from their inception to December 2024. This search focused on studies that developed and/or validated AI techniques for detecting BM in magnetic resonance imaging (MRI) or computed tomography (CT). A hierarchical model was used in the meta-analysis to calculate diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC), specificity (SP), and pooled sensitivity (SE). The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), while the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis-artificial intelligence (TRIPOD-AI) was employed for evaluating the quality of evidence.

RESULT

This review covered 20 articles, among them, 16 studies were included in the meta-analysis. The results revealed a pooled SE of 0.88 (0.82-0.92), a pooled SP of 0.89 (0.84-0.93), a pooled AUC of 0.95 (0.92-0.96), PLR of 8.1 (5.57-11.80), NLR of 0.14 (0.09-0.21) and DOR of 58 (31-109). When focusing on imaging algorithms. Based on ML, a pooled SE of 0.88 (0.77-0.92), SP 0.88 (0.82-0.92), and AUC 0.93 (0.91-0.95). Based on DL, a pooled SE of 0.89 (0.81-0.95), SP 0.89 (0.81-0.94), and AUC 0.95 (0.93-0.97).

CONCLUSION

This meta-analysis underscores the substantial diagnostic value of AI in identifying BM. Nevertheless, in-depth large-scale prospective research should be carried out for confirming AI's clinical utility in BM management.

3D pCASL-perfusion in preoperative assessment of brain gliomas in large cohort of patients

The aim of the study was to evaluate the role of pseudocontinuous arterial spin labeling perfusion (pCASL-perfusion) in preoperative assessment of cerebral glioma grades. The study group consisted of 253 patients, aged 7-78 years with supratentorial gliomas (65 low-grade gliomas (LGG), 188 high-grade gliomas (HGG)). We used 3D pCASL-perfusion for each patient in order to calculate the tumor blood flow (TBF). We obtained maximal tumor blood flow (maxTBF) in small regions of interest (30 ± 10 mm²) and then normalized absolute maximum tumor blood flow (nTBF) to that of the contralateral normal-appearing white matter of the centrum semiovale. MaxTBF and nTBF values significantly differed between HGG and LGG groups (p < 0.001), as well as between patient groups separated by the grades (grade II vs. grade III) (p < 0.001). Moreover, we performed ROC-analysis which demonstrated high sensitivity and specificity in differentiating between HGG and LGG. We found significant differences for maxTBF and nTBF between grade III and IV gliomas, however, ROC-analysis showed low sensitivity and specificity. We did not observe a significant difference in TBF for astrocytomas and oligodendrogliomas. Our study demonstrates that 3D pCASL-perfusion as an effective diagnostic tool for preoperative differentiation of glioma grades.

Utility of Serum EB Virus Zta Antibody in the Diagnostic of Nasopharyngeal Carcinoma: Evidences From 2,126 Cases and 15,644 Controls

We collected previous published data and performed a systematical assessment for the diagnostic value of serum Zta antibody in NPC patients. Using bivariate-mixed effect model, we calculated the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnosis odds ratio (DOR), and summary receiver operating characteristics curve (AUC) and their 95% confidence intervals (CIs). We also performed subgroup analysis to explore the heterogeneity. We included 23 studies including 24 pieces of data and 17,770 study subjects (2,126 cases and 15,644 controls). The overall combined sensitivity was 0.85 (95%CI: 0.80-0.89) and the combined specificity was 0.90 (95%CI: 0.87-0.93). The summarized AUC was 0.94 with 95%CI of 0.92-0.96. The PLR was 8.9 (95%CI: 6.4-12.2) and the NLR was 0.17(95%CI: 0.12-0.23). The diagnostic odds ratio was 53 (95%CI: 32-87). For publication year, the sensitivity was 0.88 (95%CI: 0.84-0.91) and the specificity was 0.90 (95%CI: 0.84-0.93) for ≤2006. The AUC, PLR, NLR and DOR were 0.94, 8.8, 0.13, and 64. The pooled results were similar for >2006 group. For different sample size, the pooled AUC was 0.94 for ≤Median and was 0.95 for >Median that were very close to the overall estimations. For different population setting, no overlap was found in the sensitivity (0.84 vs. 0.87), specificity (0.90 vs. 0.84), PLR (8.7 vs. 5.5), NLR (0.16 vs. 0.08-0.33), DOR (49 vs. 35), and AUC (0.94 vs. 0.92) between Asian and others. The serum EBV antibody examination has high diagnostic accuracy for early-stage NPC. The diagnostic accuracy seems not to be influenced by sample size, publication year, and ethnic. Considering the few numbers of study with non-Asian population, the present results need to be confirmed in other population setting.

Clinical utility of contrast-enhanced ultrasonography in the diagnosis of benign and malignant small renal masses among Asian population

We assessed the clinical utility of contrast-enhanced ultrasonography (CEUS) in the diagnosis of benign and malignant small renal masses using a meta-analysis of diagnostic test. We performed a comprehensive online search in the following database including PubMed, Embase, Web of Science, Wanfang, and Chinese National Knowledge Infrastructure from the inception to August 25, 2019. the following index were calculated for assessing the diagnostic ability, including sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC) with 95% confidence intervals (CIs). Seventeen studies were included in the qualitative and quantitative analyses. The overall sensitivity was 0.93 with 95% CI of 0.88-0.95. The specificity was 0.71 and the 95% CI was 0.60-0.80. The pooled AUC was 0.91 (95% CI: 0.88-0.93). The diagnostic odds ratio was 31 (95% CI: 21-45). The NLR and PLR were 0.10 (95% CI: 0.07-0.15) and 3.2(95% CI: 2.3-4.4), respectively. There is a slight heterogeneity within studies. The subgroup analysis was also performed. For retrospective and perspective, the sensitivity and specificity were 0.93, 0.92 and 0.71, 0.73; For different diameter lesions, the sensitivity and specificity were 0.93, 0.94 and 0.64, 0.74; For sample size (≤median vs. >median), the sensitivity and specificity were 0.94, 0.93 and 0.67, 0.77. The Deek's funnel plot asymmetry test in indicated no publication bias. The CEUS have a high diagnostic ability in differentiating benign and malignant small renal masses among Asian population.