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Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

ABSTRACT: BACKGROUND: The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project. METHODS: With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies. RESULTS: In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmean for outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmean and TLG (0.6 and 0.52, resp.). CONCLUSIONS: CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use at http://code.google.com/p/cgita.

SUBMITTER: Fang YH

PROVIDER: S-EPMC3976812 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

Fang Yu-Hua Dean YH Lin Chien-Yu CY Shih Meng-Jung MJ Wang Hung-Ming HM Ho Tsung-Ying TY Liao Chun-Ta CT Yen Tzu-Chen TC

BioMed research international 20140317

<h4>Background</h4>The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research commu ...[more]

PMID: 24757667

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Dataset Information

Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

Publications

Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

Similar Datasets

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