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Neutrophil extracellular trap formation (NETosis), previously described as an effector host defense mechanism, is more frequently associated with cancer-driven inflammation and tumor development. In the case of breast cancer, NETosis assists with several oncogenic processes, including epithelial-mesenchymal transition, immune evasion, organotropic metastasis, angiogenesis, and resistance to therapy. The current study summarizes the evidence of the mechanistic role of NETs in breast cancer and their potential to act as a diagnostic, prognostic, or therapeutic biomarker. Like many other promising candidates for novel prognostic or diagnostic biomarkers, citrullinated histone H3 (citH3), myeloperoxidase-DNA (MPO-DNA) complexes, and circulating cell-free DNA (cfDNA) need further validation to be functional. However, there is some evidence suggesting clinical relevance. Like many candidate therapeutic indices, several therapies are exploring targeting NETosis, including deoxyribonuclease I (DNase I) and peptidyl arginine deiminase 4 (PAD4) inhibitors, as a clinical intervention. However, methodological differences across studies and a lack of standardized detection of NETs are likely impeding future findings. Future insight into the efficiency of detection methods and consistency of experimental design will be beneficial for transferring NETosis to the clinic.

Background Machine learning (ML) models have gained traction for classifying carotid artery plaques via ultrasound imaging to differentiate high-risk (unstable) from low-risk (stable) plaques, a critical step for stroke risk prediction and guiding clinical interventions such as endarterectomy. However, prior studies report inconsistent diagnostic performance attributed to variations in algorithms, cohort diversity, and imaging protocols. This systematic review and meta-analysis aim to evaluate the pooled diagnostic accuracy of ML models for carotid plaque classification, addressing these inconsistencies to inform standardized clinical applications. Methods Five electronic databases were systematically searched up to February 28, 2025, for studies reporting diagnostic performance metrics of ML-based models in carotid plaque classification. Pooled performance metrics were analyzed using STATA, and the risk of bias was assessed using the PROBAST+AI tool. Results A total of 20 studies met the inclusion criteria, of which 13 provided sufficient data for quantitative synthesis. Sample sizes ranged from 15 to 413 patients, with 115– 81,000 images per study. Mean ages ranged from 27.5 to 75 years, mostly 60–70, and male representation ranged from 47% to 81%, except for one all-female cohort. The pooled sensitivity was 0.84 (95% CI: 0.74–0.90) and specificity was 0.96 (95% CI: 0.89–0.98), with a pooled AUC of 0.95 (95% CI: 0.93–0.97). Substantial heterogeneity was observed (I 2  = 88.8% for sensitivity, 64.1% for specificity, and 68.1% overall). Meta-regression identified sample size and model architecture as significant sources of between-study heterogeneity. No evidence of publication bias was detected ( p  = 0.36). Quality assessment using PROBAST+AI indicated a low overall risk of bias in 70% of studies, moderate in 20%, and high in 10%. The GRADE approach rated the certainty of evidence as moderate, primarily due to inconsistency and study-level bias. Conclusion Machine learning models demonstrate promising diagnostic accuracy for carotid plaque classification, showing high pooled sensitivity and specificity. However, substantial heterogeneity and only moderate certainty of evidence suggest that these findings should be interpreted with caution.

Background Heart failure is a significant complication in diabetic patients, and machine learning algorithms offer potential for early prediction. This systematic review and meta‐analysis evaluated the diagnostic performance of ML models in predicting HF among diabetic patients. Methods We searched PubMed, Web of Science, Embase, ProQuest, and Scopus, identifying 2830 articles. After deduplication and screening, 16 studies were included, with 7 providing data for meta‐analysis. Study quality was assessed using PROBAST+AI. A bivariate random‐effects model (Stata, midas, metadta) pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio (DOR) for best‐performing algorithms, with subgroup analyses. Heterogeneity (I2) and publication bias were assessed. Results This meta‐analysis of seven studies evaluating machine learning models for heart failure detection demonstrated a pooled sensitivity of 84% (95% CI: 0.75–0.90), specificity of 86% (95% CI: 0.56–0.97), and an area under the ROC curve of 0.90 (95% CI: 0.87–0.93). The pooled positive likelihood ratio was 6.6 (95% CI: 1.2–35.9), and the negative likelihood ratio was 0.17 (95% CI: 0.08–0.36), with a diagnostic odds ratio of 39 (95% CI: 4–423). Significant heterogeneity was observed, primarily related to differences in study populations, machine learning algorithms, dataset sizes, and validation methods. No significant publication bias was detected. Conclusion Machine learning models demonstrate promising diagnostic accuracy for heart failure detection and have the potential to support early diagnosis and risk assessment in clinical practice. However, considerable heterogeneity across studies and limited external validation highlight the need for standardised development, prospective validation, and improved interpretability of ML models to ensure their effective integration into healthcare systems. Machine learning models demonstrate high diagnostic accuracy for predicting heart failure in patients with type 2 diabetes, outperforming traditional risk models. Their ability to integrate diverse clinical data highlights their potential for early detection and risk stratification in clinical practice.

Pneumonia, including influenza, COVID-19, and community-acquired pneumonia, is a major global health burden associated with high morbidity, mortality, and frequent progression to respiratory failure requiring intubation. Early identification of patients at risk of endotracheal intubation is essential to improve outcomes and optimize ICU resource allocation, yet existing prognostic tools remain limited in predicting this need. This study evaluated the performance of machine learning (ML) algorithms in predicting endotracheal intubation among patients with pneumonia during hospital stay. We systematically searched five databases to evaluate the diagnostic accuracy of ML models. Pooled estimates of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity were calculated. Subgroup analysis and meta-regression were conducted. Risk of bias was assessed using PROBAST+AI and certainty of evidence with GRADE. This systematic review of 34 studies (26 in meta-analysis) included 195,214 pneumonia patients. The pooled AUROC was 0.79 (95% CI: 0.75-0.82), with sensitivity of 0.74 (95% CI: 0.61-0.84), specificity of 0.71 (95% CI: 0.50-0.86), and a DOR of 7 (95% CI: 2-20), indicating moderate diagnostic accuracy. Heterogeneity was substantial across analyses (I = 90.45% for sensitivity and 94.58% for specificity). Risk of bias was lowest in development (59%) and highest in application domains (41% high risk). Despite a nonsignificant Deeks' test (p = 0.252), the funnel plot suggests selective publication of positive results, likely inflating the pooled AUROC. GRADE rated the evidence as moderate to low due to heterogeneity and imprecision. ML algorithms demonstrate a modest and highly variable accuracy in predicting the need for endotracheal intubation among pneumonia patients. High heterogeneity and methodological variability highlight the need for standardized ML approaches before clinical adoption.

Background and aim Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This systematic review and meta-analysis aim to evaluate the effectiveness of AI-based models, which refers to systems utilizing artificial intelligence to analyze data and make predictions, in predicting immunotherapy responses in gastrointestinal cancers using genetic mutation features . Methods This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. A search in PubMed, WOS, and Scopus identified relevant studies. Data extraction and quality assessment were conducted, and statistical analysis included pooled estimates for sensitivity, specificity, accuracy, and AUC. Regression models and imputation methods addressed missing values, ensuring accurate and robust results. STATA version 18 was used to analyze the data. Result A total of 45 studies, all published in 2024, involving 14,047 participants in training sets and 10,885 participants in test sets, were included. The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86–0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). Heterogeneity was low to moderate, and no publication bias was detected. Subgroup analysis showed higher AUC for gastric cancer models (AUC: 0.87) and lower for pancreatic cancer models (AUC: 0.52). Conclusion AI networks demonstrate promising potential in predicting immunotherapy outcomes for gastrointestinal cancers based on genetic mutation features. This systematic review highlights their effectiveness in stratifying patients and optimizing treatment decisions. However, further large-scale studies are needed to validate AI models and integrate them into clinical practice for improved precision in cancer immunotherapy.

Background Gastric cancer (GC) remains a significant global health burden, with high mortality due to delayed diagnosis. Advances in microbiome profiling and artificial intelligence (AI) have opened new frontiers in non-invasive cancer risk prediction. However, the methodological landscape of AI-driven microbiome-based GC prediction remains fragmented and poorly standardized. Objective To systematically review and critically evaluate artificial intelligence (AI) and machine learning (ML) models developed for gastric cancer prediction using microbial and non-invasive biomarkers, spanning gut, gastric mucosal, and oral ecosystems as well as tongue-based imaging proxies. We aimed to map methodological rigor, translational readiness, and biomarker convergence across these domains. Methods We systematically searched PubMed, Scopus, and Web of Science for peer-reviewed studies published up to March 2025. Eligible studies applied ML or deep learning models to microbiome datasets for GC diagnosis, risk classification, or treatment response. Data extraction included sample source, sequencing method, taxonomic resolution, ML model type, validation strategy, performance metrics, interpretability tools, and reported microbial taxa. Descriptive synthesis, thematic clustering, and readiness scoring were conducted using structured visual analytics. Results Nine studies met the inclusion criteria. Sample sources included gastric mucosa, feces, saliva, tongue coating, and tumor tissue. 16S rRNA sequencing was most common, with models primarily trained on genus-level data. Random Forest was the most frequently used algorithm (44.4%), followed by LASSO, LightGBM, and deep learning. AUC values ranged from 0.88 to 0.97 in validated models. However, only 33.3% of studies employed external validation, and interpretability and reporting standards varied widely. A Clinical Readiness Matrix and Validation Quality Assessment highlighted key translational gaps. Recurrent microbial biomarkers included Veillonella , Fusobacterium , Prevotella , and Porphyromonas . Conclusion AI-based microbiome models, including non-invasive diagnostics, show high potential for gastric cancer prediction. Yet, reproducibility, external validation, and reporting transparency remain critical barriers to clinical implementation. Standardized pipelines, multi-omics integration, and prospective validation are needed to transition this field from proof-of-concept to precision oncology.

Colorectal cancer (CRC) remains a major global health burden and a leading cause of cancer-related morbidity and mortality. It ranks as the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths worldwide. These statistics underscore the urgent need for improved diagnostic, prognostic, and therapeutic strategies to combat the disease. The development of CRC is driven by a combination of genetic predisposition, environmental exposures, and lifestyle-related factors. Major risk factors include a family history of CRC, unhealthy diet, smoking, excessive alcohol consumption, and chronic intestinal inflammation. Effective CRC management relies heavily on early detection through colonoscopy, imaging modalities, and biomarker-based analyses. Standard treatment options include surgery, chemotherapy, radiotherapy, and targeted therapy, selected according to disease stage and patient condition. Emerging evidence identifies long non-coding RNAs (lncRNAs) as pivotal regulators of CRC progression, modulating gene expression and tumor biology. Among these, the lncRNA colon cancer-associated transcript 2 (CCAT2) has recently been recognized as a critical driver of CRC growth and metastasis. CCAT2 regulates gene expression and preserves chromosomal stability by modulating key oncogenic signaling pathways, including Wnt/β-catenin and MYC. Preliminary studies propose CCAT2 as a potential therapeutic target in CRC; however, further investigation is required to validate its feasibility, especially regarding delivery strategies and molecular specificity. This review provides a comprehensive overview of lncRNA CCAT2 in CRC development, emphasizing its underlying molecular mechanisms. It further discusses potential of CCAT2 as a diagnostic and prognostic biomarker, highlighting its relevance for future clinical application.

Background Percutaneous coronary intervention (PCI) is a cornerstone of coronary artery disease management, yet predicting post-procedural outcomes remains challenging. Machine-learning (ML) models applied to cardiac imaging have emerged as tools to improve risk stratification and prognostic assessment. Objective To systematically evaluate the performance of ML models derived from cardiac imaging, primarily coronary CT angiography (CCTA) and single-photon emission computed tomography (SPECT), for predicting PCI-related clinical outcomes, including major adverse cardiovascular events (MACE), mortality, repeat revascularization, procedural success, and functional recovery. Methods PubMed, Scopus, and Web of Science were searched for studies developing or validating ML models using CCTA and/or SPECT to predict PCI-related outcomes. Extracted performance metrics included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Risk of bias was assessed using PROBAST. Where methodologically appropriate, random-effects meta-analyses were performed within modality-specific and endpoint-specific strata. Results Ten studies met inclusion criteria, nine of which contributed quantitative data. Most models were CCTA-based, with fewer SPECT-only or combined-modality approaches. Across pooled analyses, ML models demonstrated strong discriminative performance for PCI-related outcomes, with pooled estimates of accuracy 0.89, sensitivity 0.78, specificity 0.86, and AUC 0.87. CCTA-based models showed more consistent performance across outcomes, while SPECT-based models demonstrated promising but more heterogeneous results. Conclusion ML models applied to cardiac imaging show substantial potential for predicting PCI-related outcomes, particularly for CCTA-based approaches. Evidence for SPECT-based models remains emerging and heterogeneous. Standardized outcome definitions, modality-specific reporting, and prospective validation are essential before routine clinical implementation.

Background Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. These models can provide valuable insights to surgeons and oncologists, helping them tailor personalized treatment plans, enhance patient prognostication, and optimize follow-up strategies. Methods We systematically searched PubMed, Scopus, Embase, Cochrane Library, and Web of Science databases until November 2024, applying PRISMA guidelines. Results Out of 1240 studies screened, six met our eligibility criteria involving ML-based approaches to predict PA recurrence. The studies employed 12 different ML algorithms. Meta-analysis showed a pooled sensitivity of 0.87 [95% CI: 0.78–0.92], specificity of 0.86 [95% CI: 0.67–0.95], positive diagnostic likelihood ratio (DLR) of 6.32 [95% CI: 2.46–16.26], and negative DLR of 0.16 [95% CI: 0.1–0.25]. The diagnostic odds ratio (DOR) was 40.52 [95% CI: 13–126.27], and the diagnostic score was 3.7 [95% CI: 2.57–4.84]. The pooled AUC was 0.89 [95% CI: 0.86–0.92], indicating a high overall diagnostic performance. For the comparison between Logistic Regression (LR) and non-LR algorithms, LR-based algorithms exhibited numerically higher AUC and sensitivity; however, these differences were not statistically significant. Additionally, LR-based algorithms showed lower specificity, positive likelihood ratio, and diagnostic odds ratios, but the statistical tests did not provide strong evidence for meaningful differences. Conclusion AI-based models show strong predictive power for recurrence in both functional and non-functional pituitary adenomas, with an average accuracy above 80%. However, the lack of external validation and the complexity of input data pose challenges, highlighting the need for rigorous validation with multi-center datasets and standardized imaging techniques to enhance clinical applicability.

Background Postoperative cognitive dysfunction (POCD) significantly affects recovery, hospitalization duration, and quality of life following cardiac surgery. Identifying reliable biomarkers for predicting POCD could improve patient outcomes and perioperative care. Among these, S100 calcium-binding protein beta (S100β) and neuron-specific enolase (NSE) have emerged as promising indicators of cerebral injury and neurocognitive dysfunction. Objectives This systematic review and meta-analysis aimed to assess within-subject perioperative changes in S100β and NSE levels among patients who developed POCD after cardiac surgery, to evaluate whether these biomarkers consistently rise in association with POCD. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science up to October 2024. Studies included peer-reviewed articles evaluating S100β and NSE levels in relation to POCD in cardiac surgery patients. Two reviewers independently extracted data and assessed the quality using the ROBINS-I tool. Meta-analyses were conducted using a random-effects model. Results Thirty studies were included. Among patients who developed POCD, both S100β and NSE levels were significantly elevated postoperatively compared to preoperative baselines. The pooled standardized mean difference (SMD) was 1.52 (95% CI 0.57–2.48; I 2  = 93.1%) for S100β and 1.19 (95% CI 0.42–1.96; I 2  = 88.7%) for NSE, indicating large effect sizes. Sensitivity analyses confirmed the robustness of these findings despite substantial heterogeneity. Conclusions Among patients who developed POCD, S100β and NSE levels significantly increased from preoperative to postoperative measurements, indicating a potential association with cerebral injury. However, as non-POCD patients were not analyzed for the same biomarker changes, causality or specificity to POCD cannot be confirmed and future research should be directed toward between group changes.