Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images

• Published in: BioMed research international Vol. 2014; no. 2014; pp. 1 - 9
• Main Authors: Lin, Chien-Yu, Wang, Hung-Ming, Fang, Yu-Hua Dean, Yen, Tzu-Chen, Liao, Chun-Ta, Ho, Tsung-Ying, Shih, Meng-Jung
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2014; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Adult; Algorithms; Biomarkers; Female; Freeware; Heterogeneity; Hospitals; Humans; Image Interpretation, Computer-Assisted - methods; Information industry; Internet; Male; Middle Aged; Molecular Imaging - methods; Mouth Neoplasms - diagnosis; Nuclear medicine; Oncology; Open source software; Otolaryngology; Pattern Recognition, Automated - methods; Positron-Emission Tomography - methods; Reproducibility of Results; Sensitivity and Specificity; Software; Software Design; Software packages; Source code; Surface layer; Texture; Tumors; User-Computer Interface; Taiwan
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2014/248505

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.