Davatzikos Christos, Rathore Saima, Bakas Spyridon, Pati Sarthak, Bergman Mark, Kalarot Ratheesh, Sridharan Patmaa, Gastounioti Aimilia, Jahani Nariman, Cohen Eric, Akbari Hamed, Tunc Birkan, Doshi Jimit, Parker Drew, Hsieh Michael, Sotiras Aristeidis, Li Hongming, Ou Yangming, Doot Robert K, Bilello Michel, Fan Yong, Shinohara Russell T, Yushkevich Paul, Verma Ragini, Kontos Despina
Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States.
Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States.
J Med Imaging (Bellingham). 2018 Jan;5(1):011018. doi: 10.1117/1.JMI.5.1.011018. Epub 2018 Jan 11.
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.
多参数成像协议的发展为定量成像表型铺平了道路，这些表型可预测治疗反应和临床结果，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规划。这种发展凸显了在这个新兴的综合精准诊断时代，需要高效的定量分析来得出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），这是一个新的且不断发展的软件平台，用于分析癌症的放射图像，目前专注于脑癌、乳腺癌和肺癌。CaPTk基于两级功能，利用定量成像分析的价值以及机器学习来得出表型成像特征。首先，图像分析算法用于提取各种不同且互补的特征的综合面板，例如多参数强度直方图分布、纹理、形状、动力学、连接组学和空间模式。在第二级，这些定量成像特征被输入到多变量机器学习模型中，以产生诊断、预后和预测生物标志物。展示了三个领域临床研究的结果：（i）脑胶质瘤的计算神经肿瘤学，用于精准诊断、结果预测和治疗规划；（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。
