Radiologists' memory as a data protection risk: a worst-case stress test for chest radiograph re-identification [0.03%]
放射学家的记忆作为数据保护风险：胸部X光片再识别的最坏情况压力测试
Matthias A Fink,Christian Römer,Matthis Ebel et al.
Matthias A Fink et al.
Objectives: To estimate an upper bound of memory-based re-identification risk for chest radiographs by testing radiologists under conditions that favor recognition. ...
A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer [0.03%]
一种预测非小细胞肺癌PD-L1表达和免疫治疗反应的影像病理融合模型
Dingpin Huang,Fangyi Xu,Yi Gan et al.
Dingpin Huang et al.
Objective: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1 expression in non-small cell lung cancer (NSCLC) and to explore its ad...
Sustainability of contrast-enhanced breast imaging: a review of current evidence [0.03%]
对比增强乳腺影像的可持续性：现有证据综述
Iva Biondić Špoljar,Elisabetta Giannotti
Iva Biondić Špoljar
Breast cancer is the most commonly diagnosed malignancy among women worldwide. Contrast-enhanced imaging is central to diagnosis, staging, and treatment monitoring, yet its increasing use raises important environmental concerns. This review...
Imaging findings in the temporomandibular joint complex following radiation therapy for head and neck cancer: a review [0.03%]
放射治疗后头颈部癌症颞下颌关节影像学表现综述
Andrew Nalley,Madeline Yon,Kuo Feng Hung et al.
Andrew Nalley et al.
Objectives: This study aimed to identify and synthesize imaging findings in the temporomandibular joint (TMJ) complex following radiation therapy (RT) for head and neck cancer (HNC). ...
Left ventricular strain and hemodynamic forces from cine CMR for detecting constrictive physiology in pericarditis [0.03%]
应用稳态自由进动心血管磁共振检测心包炎缩窄生理的左室应变和血流动力学力研究
Thomas M Vollbrecht,Annemarie Proff,Sophia Juraschitz et al.
Thomas M Vollbrecht et al.
Objectives: Constrictive physiology (CP) may complicate pericarditis and requires timely recognition, yet CMR assessment is largely qualitative. This study evaluated the diagnostic value of cine-based strain and hemodynam...
Pablo Rodríguez-Belenguer,Paula Doria-Borrell,Ángel Alberich-Bayarri et al.
Pablo Rodríguez-Belenguer et al.
Introduction: Imaging biomarkers are quantifiable features extracted from medical images that indicate health status, disease characteristics, or treatment response. Their value depends on rigorous standardization and val...
Correction: How should we interpret excessive left ventricular trabeculation? Update on controversies from the cardiac imaging perspective [0.03%]
纠正：我们应如何解读过度的左心室肌小梁化？从心血管影像学角度更新争论点
Roque Oca Pernas,Nerea Hormaza Aguirre,Marcos Mestas Nuñez et al.
Roque Oca Pernas et al.
Published Erratum
Insights into imaging. 2026 Jun 9;17(1):157. DOI:10.1186/s13244-026-02325-1 2026
Cascade mentoring: a proposal for transforming radiology education in line with the European training curriculum [0.03%]
级联辅导：一种与欧洲培训计划一致的放射学教育转型建议
Alberto Vieira,Ana Catarina Vieira
Alberto Vieira
Although mentoring remains a fundamental part of medical training, the traditional approach of a senior mentor guiding a junior trainee often fails to meet the diverse and evolving needs of learners in radiology. While peer and near-peer me...
Precise zonal diagnosis: multi-b-value DWI model reveals differential predictors of clinically significant prostate cancer in peripheral and transition zones [0.03%]
精确分区诊断：多b值DWI模型揭示外周带和过渡区临床显著前列腺癌的差异性预测因素
Kangwen He,Yinsong Chen,Zhen Kang et al.
Kangwen He et al.
Objectives: To evaluate the diagnostic performance of multi-b-value DWI models for clinically significant prostate cancer (csPCa), identify zone-specific predictors in the peripheral (PZ) and transition zones (TZ), and va...
An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population [0.03%]
一种在中国人群中用于早期乳腺癌的基于超声图像的可解释深度学习系统
Siqi Wu,Shenwen Wang,Di Liang et al.
Siqi Wu et al.
Objectives: Current deep learning models for early breast cancer lack interpretability and multimodal integration, limiting their clinical acceptance. This study aimed to develop and evaluate a deep learning system that a...