摘要
一位新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-中枢神经系统疾病和状况的新研究-脑出血是一篇报道的主题。根据NewsRx Edi Tors对潍坊的新闻报道，研究表明：“脑出血患者早期识别血肿扩大和持续性血肿扩大(HE)对于确定临床治疗越来越重要，但由于缺乏临床有效的工具，放射组学已逐渐被引入血肿扩大的早期诊断。”我们的新闻记者引用了潍坊医科大学的一篇研究文章:“然而,由于程序的不同,放射组学的预测精度有限。因此,我们进行了系统回顾和荟萃分析,以探讨放射组学在脑出血早期发现HE中的价值。”符合条件的研究在PubMed,Embase,Cochrane和Web of Science从一开始到2024年4月8日。英文文章被认为是合格的。使用放射组学质量评分(RQS)工具对纳入的研究进行评估。共鉴定出34项研究，样本量从108到3016不等。涉及11种模型，建模类型主要包括临床、放射学、放射学和放射学。放射组学模型在区分HE方面的表现（训练队列和验证队列分别为0.77和0.73 C指数）似乎优于临床模型（训练队列为0.69 C指数和验证队列为0.70 C指数）。然而，在训练队列（0.82）和验证队列（0.79）中，联合模型的C指数最高。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Cerebral Hemorrhage is the subject of a report. Accor ding to news reporting out of Weifang, People's Republic of China, by NewsRx edi tors, research stated, "Early identification of hematoma enlargement and persist ent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinic ally effective tools, radiomics has been gradually introduced into the early ide ntification of hematoma enlargement." Our news journalists obtained a quote from the research from Weifang Medical Uni versity, "Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to ex plore the value of radiomics in the early detection of HE in patients with cereb ral hemorrhage. Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles a re considered eligible. The radiomics quality scoring (RQS) tool was used to eva luate included studies. A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical featur es. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical mo dels (0.69 C-index in the training cohort and 0.70 C-index in the validation coh ort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts."
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