Zu-Yao Yang

Background: Nasopharyngeal carcinoma (NPC) can be detected early on MRI, but adoption for screening is limited by a shortage of experienced specialists. MRI artificial intelligence (AI) algorithms for diagnosing NPC in the literature may address this, but most studies focus on routine clinical care, and their applicability in screening requires close examination. Purpose: To (i) evaluate the diagnostic performance of AI for NPC detection, (ii) analyze the impact of study protocols, and (iii) assess the applicability of existing studies to NPC screening. Study Type: Systematic review. Population: Thirty‐eight studies were included, including a total of 23,398 patients (20,693 NPC and 2705 non‐NPC). Fieldstrength: 1.5 T to 3.0 T. Assessment: Following PRISMA guidelines, PubMed, Scopus, and Embase records from January 1, 2009 to August 16, 2025 were screened by two independent reviewers for studies on NPC detection, localization, and/or diagnosis using MRI. Included studies were reviewed for applicability and risk of bias. Lesion‐localization performance (Dice similarity score [DSC]) and NPC vs. non‐NPC discrimination (sensitivity/specificity) were extracted and summarized. Subgroup analyses assessed the impact of intravenous contrast on AI performance. Statistical Tests: Meta‐analysis used standard random‐effects univariate restricted maximum likelihood model and hierarchical summary receiver operator characteristics for performance pooling and subgroup analysis, taking p value < 0.05 as significance. Results: Pooled performance from 30 localization and 6 discrimination studies was DSC = 0.80 (CI: 0.78–0.82) and sensitivity/specificity = 97.1% (CI: 88.0%–99.3%)/87.8% (CI: 78.9%–93.2%), respectively. Intravenous contrast did not significantly affect AI performance for either task ( p > 0.05). Only seven studies were fully applicable to screening. Data Conclusion: AI‐driven NPC screening using MRI is feasible at high sensitivity. Using non‐contrast MRI did not significantly impact AIs' performance. However, applicability and the suboptimal ratio of NPC: non‐NPC patients remain the weaknesses of existing studies, warranting further investigation with cohorts that better reflect the screening scenario. Systematic Review Registration: The systematic review protocol of this system is registered with the Inplasy registry (Ref. INPLASY202570076).

Gastrointestinal (GI) diseases represent a significant global health burden, and emerging evidence suggests air pollution may be a risk factor. We reviewed relevant literature and performed a meta-analysis. We searched original studies investigated the associations between long- and short-term exposure to particulate matter (diameter ≤ 2.5 µm [PM2.5], ≤ 10 µm [PM10]) and gaseous pollutants (nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone [O3]) and GI diseases. DerSimonian and Laird random-effects meta-analyses were conducted when at least 2 studies were available for a given pollutant (per 10 μg m−3 increment) and a specific GI disease. A total of 70 studies were included. Long-term exposure to PM2.5 was significantly associated with increased mortality rate from all GI cancers (OR = 1.128; 95% CI, 1.019-1.248), with the strongest association observed for colorectal cancer (OR = 1.214; 95% CI, 1.061-1.389). For non-cancer GI diseases, long-term exposure to PM2.5 was significantly associated with increased risks of duodenal ulcer (OR = 1.073; 95% CI, 1.057-1.089) and irritable bowel syndrome (OR = 1.353; 95% CI, 1.222-1.497); PM10 was also linked to the latter (OR = 1.108; 95% CI, 1.012-1.213). Short-term exposure, particularly to PM2.5, PM10, and O3, was significantly associated with appendicitis hospitalization (OR = 1.003 [95% CI, 1.001-1.004]; OR = 1.017 [95% CI, 1.015-1.019]; and OR = 1.001 [95% CI, 1.000-1.003], respectively). PM2.5 was also associated with peptic ulcer bleeding (OR = 1.006; 95% CI, 1.001-1.011). Long- and short-term exposures to air pollution, especially PM2.5, are associated with increased risks of GI diseases.

Supplementary Figure S15 shows subgroup analyses for the association between glucosamine use and incidence of other male cancer.

Supplementary Figure S11 shows subgroup analyses for the association between glucosamine use and incidence of stomach cancer.

Supplementary Figure S3 shows subgroup analyses for the association between glucosamine use and incidence of brain cancer.

Supplementary Figure S16 shows subgroup analyses for the association between glucosamine use and incidence of other female cancer.

Supplementary Figure S2 shows subgroup analyses for the association between glucosamine use and incidence of lung cancer.

Supplementary Figure S14 shows subgroup analyses for the association between glucosamine use and incidence of hepatobiliary cancer.

Supplementary Figure S1 shows subgroup analyses for the association between glucosamine use and incidence of prostate cancer in male participants.

Supplementary Figure S6 shows subgroup analyses for the association between glucosamine use and incidence of esophageal cancer.