Cancer Imaging Phenomics Toolkit (CaPTk)
CaPTk is a software platform for analysis of radiographic cancer images, 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. Its long-term goal is 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.
- SNO 2018 (November 15-18): Presentation
- RSNA 2018 (November 25-30): Live Demo
- 2016 RSNA (Nov. 28-30) - Chicago, Illinois, USA
Live Software Demo @ the 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America
- 2017 RSNA (Nov.27-30) - Chicago, Illinois, USA (more details)
Live Software Demo @ the 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America
Meet-The-Experts session in the Quantitative Imaging Reading Room of Learning Center (Booth QRR018).
- 2018 SPIE (Feb. 10-15) - Houston, TX, USA. (more details)
Live Software Demo @ the SPIE Medical Imaging Conference
- 2018 ISBI (Apr. 4-7) - Washington, D.C., USA. [Hands-on Tutorial] (more details)
Half-Day Tutorial @ the 15th IEEE International Symposium on Biomedical Imaging (covered by the Computer Vision News)
(Featured by the RSIP Vision magazine as one of the Best of ISBI 2018)
- 2018 ITCR (23-24 May) - Natcher Conference Center, Bethesda, MD, USA. [Demo] (more details)
Presentation @ the Annual Meeting of the Informatics Technology for Cancer Research
- 2018 MICCAI (Sep.16-20) - Granada Conference Center, Granada, Spain [Tutorial] (more details)
Tutorial @ the Medical Image Computing and Computer Assisted Intervention conference
- 2018 ECOG-ACRIN (Fall)
- 2018 SNO - New Orleans, USA
Presentation in the Special Session on Computational Neuro-Oncology
Nov.15-18 @ the 23rd Annual Scientific Meeting of the Society for Neuro-Oncology
- 2018 RSNA - Chicago, Illinois, USA
Meet-The-Experts session in the Quantitative Imaging Reading Room of Learning Center (Booth: TBA).
Nov.25-30 @ the 104th Scientific Assembly and Annual Meeting of the Radiological Society of North America
The core functionality of CaPTk is based on a two-level architecture (Fig.1). The first level targets image interaction, (pre)processing, and extraction of extensive panels of features capturing different aspects of local, regional and global imaging patterns, resulting in comprehensive quantitative imaging phenomic signatures. The second level focuses on specialized diagnostic analysis, by use of advanced computational methods to integrate these features into non-invasive diagnostic, prognostic and predictive models with clinically-oriented goals. Example applications include, i) precision diagnostics and risk assessment for developing cancer, ii) predictive models of patient survival and treatment response, and iii) detection of specialized imaging biomarkers of underlying cancer molecular characteristics (specific examples are given in “Science”, below). CaPTk has been designed as a modular adaptable platform, allowing for further extension, interoperability, and cross-platform usability.
For more details, please visit our NIH-supported Project Page.
Here are examples of scientific findings utilizing CaPTk:
- Non-invasive imaging biomarker of EGFRvIII
- Prediction of survival/prognostic stratification
- Probability maps of recurrence
- Imaging biomarkers related to cancer risk and development
- S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O’Rourke, C.Davatzikos. "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the φ index", Clin Cancer Res, 2017. [Epub ahead of print] DOI: 10.1158/1078-0432.CCR-16-1871
- S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos, "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol, 17(Suppl.5):V154, 2015. DOI: 10.1093/neuonc/nov225.05
- L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane, G.Biros, R.L.Wolf, M.Bilello, D.M.O’Rourke, C.Davatzikos. “Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques”, Neuro Oncol, 18(3):417-25, 2016 DOI: 10.1093/neuonc/nov127
- H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. “Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma”, Neurosurgery 78(4):572-580, 2016 DOI: 10.1227/NEU.0000000000001202
- A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. “Mammographic breast density decreases after bariatric surgery” Breast Cancer Res Treat, 2017 DOI: 10.1007/s10549-017-4361-y
- E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. “Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures”, Radiology 283(3):673-80, 2017 DOI: 10.1148/radiol.2016161286
- A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. “Racial differences in quantitative measures of area and volumetric breast density”, J Natl Cancer Inst 108(10), 2016 DOI: 10.1093/jnci/djw104
- A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. “Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations”, Med Phys 43(11):5862-77, 2016 DOI: 10.1118/1.4963810
Please cite the following when you use CaPTk in your research:
- Sarthak Pati, Spyridon Bakas, Aristeidis Sotiras, Ratheesh Kalarot, Patmaa Sridharan, Mark Bergman, Saima Rathore, Hamed Akbari, Paul Yushkevich, Taki Shinohara, Yong Fan, Despina Kontos, Ragini Verma, Christos Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26–Dec.1, 2017, Chicago IL.
- Sarthak Pati, Saima Rathore, Ratheesh Kalarot, Patmaa Sridharan, Mark Bergman, Taki Shinohara, Paul Yushkevich, Yong Fan, Ragini Verma, Despina Kontos, Christos Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
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.
New applications, written in any programming language, can be integrated into CaPTk at different levels. These applications can then run within CaPTk, while having direct access to the full breadth of CaPTk’s interactive capabilities.
- Source level integration:
At this level, the new application source code (C++) is compiled alongside CaPTk, ensuring the most optimized integration. Source-level integration is straight-forward (only requiring additions to relevant CMake files and minor additions to the interactive base) if the new application relies on a subset of CaPTk’s dependencies (i.e., ITK, VTK, OpenCV, Qt).
- Executable level integration:
This level provides a graphical interface to an existing command-line application (not necessarily developed in C++), allowing users to leverage CaPTk’s functionality (e.g., interaction, feature extraction). Executable-level integration requires only minor additions to CaPTk to create a menu option for the new application.
Almost every application of CaPTk has an accompanying command-line executable. Those programs can be called directly, making the CaPTk applications available as components within a larger pipeline or for efficient batch processing of large numbers of images.
For detailed instructions and more information please visit the "For Developers" section in our NIH-supported NITRC page.
CaPTk is distributed as source code and pre-compiled installers for Windows 64-bit, Linux 64-bit (compiled under Ubuntu 12.04) and we are working on producing an installable package for macOS as well.
All the installer packages are self-contained and do not need administrative privileges for installation. Please direct any queries to firstname.lastname@example.org.
- Windows Installer
- Linux Installer
- Sample Data (available as download links via the package)
- Source Code (via GitHub)
Supporting Grant: This work is developed through the NIH/NCI/ITCR-supported grant U24-CA189523.
Disclaimer: The software has been designed for research purposes and is not FDA-approved.
Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following paper:
- C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018. DOI:10.1117/1.JMI.5.1.011018
In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:
- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017. DOI:10.1007/978-3-319-75238-9_12
- S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
- S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
- Christos Davatzikos
- Despina Kontos
- Ragini Verma
- Yong Fan
- Taki Shinohara
- Paul Yushkevich
- Spyridon Bakas
- Aimilia Gastounioti
- Patmaa Sridaran: 0.0.2 — present
- Ratheesh Kalarot: 0.0.2 — 1.3.0
- Sarthak Pati: 0.0.1 — present
- Spyridon Bakas: 0.0.1
- Saima Rathore: 0.0.1 — present
Contact CBICA Software for questions, etc.