Software

Sharing of software and data is an essential part of our research activities. We will make available most of our developed software at this webpage and other websites, such as MLDataAnalytics (github.com) and NITRC: User Profile.

We will also make our software tools available on the Image Processing Portal of CBICA (IPP; https://ipp.cbica.upenn.edu/), which is freely available to the research community.

Our team also contributed to the development of CaPTk, particularly its lung cancer radiomics component, including lung field/tumor segmentation and radiomics analysis:

Cancer Imaging Phenomics Toolkit (CaPTk) | CBICA | Perelman School of Medicine at the University of Pennsylvania (upenn.edu)

Li H, Galperin-Aizenberg M, Pryma D, Simone CB 2nd, Fan Y. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol. 2018 Nov;129(2):218-226. doi: 10.1016/j.radonc.2018.06.025. Epub 2018 Jul 4. PMID: 30473058; PMCID: PMC6261331.
Li H, Galperin-Aizenberg M, Pryma D, Simone CB 2nd, Fan Y. Unsupervised Machine Learning of Radiomic Features for Predicting Treatment Response and Survival of Early-Stage Nonsmall Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy, International Journal of Radiation Oncology, Biology, Physics, Volume 99, Issue 2, S34
Davatzikos C, Rathore S, Bakas S, Pati S, Bergman M, Kalarot R, Sridharan P, Gastounioti A, Jahani N, Cohen E, Akbari H, Tunc B, Doshi J, Parker D, Hsieh M, Sotiras A, Li H, Ou Y, Doot RK, Bilello M, Fan Y, Shinohara RT, Yushkevich P, Verma R, Kontos D. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham). 2018 Jan;5(1):011018. doi: 10.1117/1.JMI.5.1.011018. Epub 2018 Jan 11. PMID: 29340286; PMCID: PMC5764116.