Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Nature communications
A deep learning framework for efficient pathology image analysis
Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature extraction and is benchmarked against leading slide- and tile-level foundation models across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response and prognosis. EAGLE outperforms patch aggregation methods by up to 23% and achieves the highest overall classification performance. It processes one slide in 2.27 s, reducing computational time by more than 99% compared with existing models. This efficiency supports rapid and auditable workflows by enabling review of the exact tiles used for each prediction and reducing dependence on high-performance computing. By reliably identifying informative regions and minimizing artifacts, EAGLE provides robust and auditable outputs, supported by systematic negative controls and attention concentration analyses. Its unified embedding enables rapid slide search, integration into multi-omics pipelines and emerging clinical foundation models.
2 July 2026
Read appraisal →Cancer causes & control : CCC
HPV in breast cancer: prevalence and comparison with healthy tissue-a systematic review and meta-analysis
INTRODUCTION: Breast cancer (BC) is the most common cancer among women worldwide, with over 2.3 million new cases annually. Recent studies suggest that Human Papillomavirus (HPV), a known oncogenic virus, may be involved in BC development. This study investigates HPV prevalence in BC samples and its potential role in tumorigenesis. METHODS: Random-effects meta-analyses were conducted to estimate raw proportions and odds ratio (OR), with 95% confidence intervals (CIs). Heterogeneity was assessed using I2. Statistical significance was set at p < 0.05. Analyses were performed in R 4.5.0 RESULTS: Our meta-analysis encompassed 82 studies and evaluated 7,683 breast cancer (BC) tissue samples to assess the presence of HPV. The overall prevalence of HPV in BC specimens was estimated at 23% (95%CI: 19%-28%). When stratified by continent, Oceania exhibited the highest regional prevalence at 38%. Comparative analysis between BC tissues and healthy controls revealed a significantly increased likelihood of HPV detection in the cancer group (OR 5.06; P < 0.001). This association remained statistically robust in both case-control (OR 6.34; P < 0.001) and cross-sectional designs (OR 2.83; P < 0.001). Among continents, South America demonstrated the most pronounced association (OR 11.66; P = 0.005). Subgroup analysis based on economic classification indicated that countries with low-income settings had the highest HPV prevalence (34%; 95%CI: 9%-73%). Evaluation by BC subtype revealed that luminal B had the highest HPV-positive rate (44%; 95%CI: 27%-61%). CONCLUSION: This meta-analysis reveals a global presence of HPV in BC and suggests a possible link. Further well-designed studies are needed to confirm its role in tumorigenesis.
26 June 2026
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