Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
复制标题
DOI：
10.1117/1.jmi.5.1.011018
复制
发表时间：
2018-01-01
影响因子：
2.4
通讯作者：
Kontos, Despina
中科院分区：
文献类型：
--
作者：
Davatzikos, Christos;Rathore, Saima;Kontos, Despina
多参数成像方案的发展为定量成像表型铺平了道路，定量成像表型可预测治疗反应和临床结局，反映潜在的癌症分子特征和时空异质性，并可指导个性化治疗计划。这种增长强调了对高效定量分析的需求，以在这个新兴的集成精确诊断时代获得具有诊断和预测价值的高维成像特征。本文介绍了癌症成像表型组学工具包（CaPTk），一个新的和动态增长的软件平台，用于分析癌症的放射影像，目前主要集中在脑，乳腺癌和肺癌。CaPTk利用定量成像分析沿着机器学习的价值，基于两级功能导出表型成像特征。首先，图像分析算法被用来提取全面的面板的不同和互补的功能，如多参数强度直方图分布，纹理，形状，动力学，连接，和空间模式。在第二个层次，这些定量成像特征被输入多变量机器学习模型，以产生诊断、预后和预测生物标志物。显示了三个领域的临床研究结果：（i）脑胶质瘤的计算神经肿瘤学，用于精确诊断，预测结果和治疗计划;（ii）乳腺癌和肺癌治疗反应的预测，以及（iii）乳腺癌的风险评估。(c)2018年，摄影光学仪器工程师协会（SPIE）
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)