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The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview.

Sarthak Pati¹, Ashish Singh¹, Saima Rathore^1,2

¹Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.

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Summary

The Cancer Imaging Phenomics Toolkit (CaPTk) is an open-source platform for analyzing cancer images. It translates research into clinical tools for cancer prediction, diagnosis, and prognosis.

Keywords:

Brain tumor Breast cancer CaPTk Cancer Deep learning Glioblastoma Glioma ITCR Imaging Lung cancer Phenomics Radiogenomics Radiomics Radiophenotype Segmentation Toolkit

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Area of Science:

Medical imaging
Computational biology
Oncology

Background:

Translating academic research into clinical practice is challenging.
Advanced image analysis tools are crucial for cancer research and clinical decision-making.
Existing software may lack user-friendliness or extensibility for clinical applications.

Purpose of the Study:

To introduce the Cancer Imaging Phenomics Toolkit (CaPTk), a cross-platform, open-source software.
To detail the technical specifications and architecture of CaPTk.
To highlight CaPTk's role in bridging research and clinical applications for cancer imaging analysis.

Main Methods:

CaPTk is built upon established toolkits like Insight Toolkit (ITK) and OpenCV.
It integrates specialized and general-purpose image analysis algorithms.
The platform offers an image viewer with algorithm integration capabilities and a library for batch processing.

Main Results:

CaPTk provides a user-friendly interface for complex algorithms, accessible to clinical experts.
It enables computational scientists to integrate new algorithms and process multiple subjects efficiently.
The toolkit facilitates the translation of cutting-edge research into clinically relevant tools.

Conclusions:

CaPTk aims to become a widely-used technology for quantitative imaging analytics in cancer.
It supports cancer prediction, diagnosis, and prognosis through advanced computational functionality.
The platform fosters a better understanding of cancer development mechanisms via imaging data.