Phillips Iain, Ezhil Veni, Hussein Mohammad, South Christopher, Nisbet Andrew, Alobaidli Sheaka, Prakash Vineet, Ajaz Mazhar, Wang Helen, Evans Philip
Royal Surrey County Hospital, Guildford, UK.
National Physical Laboratory, Teddington, London, UK.
BJR Open. 2019 Apr 29;1(1):20180001. doi: 10.1259/bjro.20180001. eCollection 2019.
This study tested the hypothesis that shows advanced image analysis can differentiate fit and unfit patients for radical radiotherapy from standard radiotherapy planning imaging, when compared to formal lung function tests, FEV1 (forced expiratory volume in 1 s) and TLCO (transfer factor of carbon monoxide).
An apical region of interest (ROI) of lung parenchyma was extracted from a standard radiotherapy planning CT scan. Software using a grey level co-occurrence matrix (GLCM) assigned an entropy score to each voxel, based on its similarity to the voxels around it.
Density and entropy scores were compared between a cohort of 29 fit patients (defined as FEV1 and TLCO above 50 % predicted value) and 32 unfit patients (FEV1 or TLCO below 50% predicted). Mean and median density and median entropy were significantly different between fit and unfit patients ( = 0.005, 0.0008 and 0.0418 respectively; two-sided Mann-Whitney test).
Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging.
This study shows that a novel assessment can generate further data from standard CT imaging. These data could be combined with existing studies to form a multiorgan patient fitness assessment from a single CT scan.
本研究检验了这样一个假设，即与正式的肺功能测试、一秒用力呼气量（FEV1）和一氧化碳弥散量（TLCO）相比，先进的图像分析能否从标准放疗计划成像中区分出适合和不适合接受根治性放疗的患者。
从标准放疗计划CT扫描中提取肺实质的顶部感兴趣区域（ROI）。使用灰度共生矩阵（GLCM）的软件根据每个体素与其周围体素的相似性为其分配一个熵值。
比较了29名适合患者（定义为FEV1和TLCO高于预测值的50%）和32名不适合患者（FEV1或TLCO低于预测值的50%）队列之间的密度和熵值。适合和不适合患者之间的平均密度、中位数密度和中位数熵存在显著差异（分别为P = 0.005、0.0008和0.0418；双侧曼-惠特尼检验）。
使用标准CT成像，密度和熵评估可以区分适合和不适合接受根治性放疗的患者。
本研究表明，一种新的评估方法可以从标准CT成像中生成更多数据。这些数据可以与现有研究相结合，通过单次CT扫描形成多器官患者适合度评估。
