基于癌患者计算机断层成像的活跃骨髓细分:基于机器学习的概念验证
C Fiandra1, S Rosati2, F Arcadipane1
1Department of Oncology, University of Turin, Turin, Italy.
概括
机器学习 (ML) 模型使用CT扫描在门癌症患者中准确识别了活跃骨髓 (ActBM). 这种方法对放射治疗规划具有前景,但对临床使用需要进一步优化.
科学领域:
- 放射学和医学成像学 医学成像学
- 在瘤学瘤学.
- 人工智能在医学中的应用
背景情况:
- 鉴定血液形成活性骨髓 (ActBM) 对于放射治疗规划至关重要.
- 目前用于ActBM识别的方法可能耗时,需要专门的设备,这给常规临床应用带来了挑战.
研究的目的:
- 评估一种机器学习 (ML) 方法,使用计算机断层扫描 (CT) 图像的放射性特征来识别门癌症患者的ActBM.
- 评估使用ML作为现有的ActBM检测方法更容易获得的替代方法的可行性.
主要方法:
- 40名患者的队列被用于培训和测试ML分类器.
- 18FDG-PET成像作为定义活跃的骨盆髓 (ActPBM) 亚区域 (阴茎,下骨盆,腰骨) 的基本真相.
- 使用DICE指数,精度和回忆来量化ML预测与FDG-PET数据之间的一致性.
主要成果:
- 在三个骨盆骨髓子区域 (ActIBM和ActLSBM) 中,ML方法与FDG-PET在两个方面取得了令人满意的重叠.
- 平均DICE分数为ActIBM的0.69和ActLSBM的0.76,表明协议良好.
- 下骨盆子区域 (ActLPBM) 的表现显示,DICE的中位数为0.36,表明需要改进的领域.
结论:
- 该ML模型显示了识别ActBM的潜力,特别是在阴茎和腰部神经部区域,为放射治疗提供了一个有前途的工具.
- 需要进一步的研究和优化,以提高模型的准确性和普遍性,以便广泛的临床实施.
- 这项研究强调了CT图像中的放射性特征在癌症患者的自动ActBM细分中的实用性.
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