Cancer Imaging Phenomics Toolkit (CaPTk)  1.3.0

CaPTk is a software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk integrates advanced, validated tools performing various aspects of medical image analysis, that have been developed in the context of active clinical research studies and collaborations toward addressing real clinical needs. With emphasis given in its use as a very lightweight and efficient viewer, and with no prerequisites for substantial computational background, CaPTk aims to facilitate the swift translation of advanced computational algorithms into routine clinical quantification, analysis, decision making, and reporting workflow.

The package 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 a rich panel of diverse and complementary features, such as multi-parametric intensity histograms, textural, morphologic, and kinetic variables, connectomics, and spatial patterns. At the second level, these radiomic features are fed into multivariate machine learning models to produce diagnostic, prognostic and predictive biomarkers (specific examples are given in “Science”, in the next section). Results from clinical studies in three areas are shown in Fig. 1:

  1. Computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome and subsequent treatment planning
  2. Prediction of treatment response for breast and lung cancer
  3. Risk assessment for breast cancer

CaPTk is developed and maintained by the Center for Biomedical Image Computing and Analytics (CBICA) at the University of Pennsylvania, and draws upon research from several groups within the Center. This software platform is distributed with the long-term goal of providing widely used technology that leverages the value of advanced imaging analytics in cancer prediction, diagnosis and prognosis, as well as in better understanding the biological mechanisms of cancer development.

Fig.1. Overview of all functions and applications of CaPTk, in its two-level architecture

Supporting Grant

This work is supported by the NIH/NCI/ITCR* grant U24-CA189523.
* National Institutes of Health / National Cancer Institute / Informatics Technology for Cancer Research



Contact CBICA Software for more information.

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