AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights [0.03%]
基于人工智能的STEMI心脏骤停预测模型：真实世界数据用于早期风险评估和预后洞察
Elena Stamate,Anisia-Luiza Culea-Florescu,Mihaela Miron et al.
Elena Stamate et al.
Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion str...
Predicting Early Outcomes of Prostatic Artery Embolization Using n-Butyl Cyanoacrylate Liquid Embolic Agent: A Machine Learning Study [0.03%]
使用n-丁基氰基丙烯酸盐液体栓塞剂进行前列腺动脉栓塞早期结局的机器学习预测研究
Burak Berksu Ozkara,David Bamshad,Ramita Gowda et al.
Burak Berksu Ozkara et al.
Machine learning models have the potential to provide valuable prognostic insights for patients undergoing PAE.
Competing Subclones and Fitness Diversity Shape Tumor Evolution Across Cancer Types [0.03%]
竞争的亚克隆和适应度多样性塑造癌症类型间的肿瘤进化路径
Hai Chen,Jingmin Shu,Rekha Mudappathi et al.
Hai Chen et al.
Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.
The relationship between estimated pulse wave velocity and 28-day mortality in patients with sA-AKI: a retrospective cohort analysis of the MIMIC-IV database [0.03%]
基于MIMIC-IV数据库的回顾性队列研究：估计脉搏波速度与sA-AKI患者28天死亡率之间的关系
Lingchen Wei,Xinhai Cui,Yue Lv et al.
Lingchen Wei et al.
This metric offers prognostic insights to help clinicians identify high-risk patients early, enabling timely interventions and better clinical outcomes.
Longitudinal changes in body composition during palliative systemic chemotherapy and survival outcomes in metastatic colorectal cancer [0.03%]
晚期结直肠癌患者姑息性全身化疗期间体质变化及生存结局分析
Hyehyun Jeong,Yousun Ko,Kyung Won Kim et al.
Hyehyun Jeong et al.
Comprehensive body composition assessment offers valuable prognostic insights without requiring additional testing.
From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy [0.03%]
从获取到预后：人工智能在缺血性心肌病心脏磁共振成像评估中的作用
Giuseppe Muscogiuri,Nicola Pegoraro,Alberto Cossu et al.
Giuseppe Muscogiuri et al.
Moreover, AI-driven analysis provides robust prognostic insights by predicting adverse outcomes, such as heart failure and arrhythmias, through comprehensive data integration and pattern recognition.
Preoperative scoring system for predicting microvascular invasion in intrahepatic cholangiocarcinoma using gadoxetate-enhanced MRI [0.03%]
基于钆塞酸增强MRI的肝内胆管细胞癌微血管侵犯评分系统及其预测价值（术前）
Rohee Park,Dong Hwan Kim,Sang Hyun Choi et al.
Rohee Park et al.
Clinical releveance This scoring system enables the accurate assessment of MVI risk in ICCA and provides valuable prognostic insights for patients undergoing curative surgery.
The prognostic potential of RNA in stage II colon cancer: Insights from a screened multicenter population-based cohort study [0.03%]
II期结肠癌中RNA的预后价值：来自一项基于人群的多中心筛查队列研究的新见解
Ulrik Korsgaard,Maria P Kristensen,Juan L García-Rodríguez et al.
Ulrik Korsgaard et al.
In conclusion, this four-gene expression score demonstrates a strong association with TTR in stage II colon cancer patients, providing valuable prognostic insights that extend beyond conventional clinical risk markers.
Biological and prognostic insights into the prostaglandin D2 signaling axis in lung adenocarcinoma [0.03%]
肺腺癌中前列腺素D2信号轴的生物学和预后研究见解
Qiang Liu,Huiguo Chen,Dongfang Tang et al.
Qiang Liu et al.
Background: Tumor metabolism reprogramming is a hallmark of cancer, but metabolite-mediated intercellular communication remains poorly understood. To address this gap, we estimated and explored communication events explor...
Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer [0.03%]
整合多模态数据以预测雄激素敏感前列腺癌的进展
Xiangfu Lu,Chenxi Pan,Luhan Yao et al.
Xiangfu Lu et al.
This work has highlighted important prognostic insights based on proteomics data, magnetic resonance imaging (MRI) and histopathological specimens. We retrospectively developed a multi-omics-based model based on 77 patients with HSPC.
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