通过机器学习从CT扫描与临床元数据相结合的CT扫描进行癌诊断和手术选择
Sakib Mahmud1, Tariq O Abbas2,3,4, Adam Mushtak5
1Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
Cancers
|June 28, 2023
概括
这项研究使用机器学习和CT扫描来准确分类癌,并推最佳的外科手术程序,改善患者的治疗结果. 人工智能模型在区分癌症类型和指导切除术决策方面取得了很高的准确性.
科学领域:
- 医疗成像医学成像
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 诊断癌和手术决定是复杂的,受瘤特征的影响.
- 目前选择部分或彻底切除术的方法可能导致患者的治疗结果不足于最佳.
- 机器学习有潜力通过整合多样化的患者数据来改善外科决策.
研究的目的:
- 开发和评估一种机器学习模型,使用CT图像和临床数据对四种主要癌类型进行分类.
- 评估该模型在指导细胞癌 (RCC) 手术选择过程中的有效性.
- 确定影响外科推的关键临床特征.
主要方法:
- 利用公开可用的KiTS数据集,对比度增强的CT图像和患者元数据.
- 应用深度机器学习模型用于数据合理化,FoV克服和ROI提取后的分类.
- 结合CT图像特征与临床数据,以实现全面的预测方法.
主要成果:
- 结合方法在分类癌方面取得了85.66%的准确率,84.18%的精度,85.66%的回忆率和84.92%的F1评分.
- 对于选择恶性瘤 (RCC) 的外科手术程序,该模型以90.63%的准确率显示出更高的可靠性.
- 瘤体积和癌症阶段被确定为预测手术干预的最关键临床特征.
结论:
- 机器学习与CT成像和临床数据的整合可以提高癌分类和手术规划.
- 拟议的方法显示出有很大的潜力,可以帮助外科医生选择最佳切除术程序.
- 准确的分类和特征识别可以导致更个性化和更有效的癌治疗策略.
关键词:
癌症手术 癌症手术 手术这是分类分类的分类.计算机断层扫描 (CT) 扫描脏 脏 脏 脏机器学习是机器学习.恶性瘤是一种恶性瘤.对象检测检测对象检测对象检测部分脏切除术 部分脏切除术进行激进性切除术.更多相关视频
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