Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study

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dc.citation.articleNumber12529en_US
dc.citation.journalTitleScientific Reportsen_US
dc.citation.volumeNumber9en_US
dc.contributor.authorTang, Tien T.en_US
dc.contributor.authorZawaski, Janice A.en_US
dc.contributor.authorFrancis, Kathleen N.en_US
dc.contributor.authorQutub, Amina A.en_US
dc.contributor.authorGaber, M. Waleeden_US
dc.date.accessioned2019-11-05T17:30:08Zen_US
dc.date.available2019-11-05T17:30:08Zen_US
dc.date.issued2019en_US
dc.description.abstractMedical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from these images to further personalize treatment. This study aims to evaluate the use of texture features derived from T1-weighted post contrast scans to classify different types of brain tumors and predict tumor growth rate in a preclinical mouse model. To optimize prediction models this study uses varying gray-level co-occurrence matrix (GLCM) sizes, tumor region selection and different machine learning models. Using a random forest classification model with a GLCM of size 512 resulted in 92%, 91%, and 92% specificity, and 89%, 85%, and 73% sensitivity for GL261 (mouse glioma), U87 (human glioma) and Daoy (human medulloblastoma), respectively. A tenfold cross-validation of the classifier resulted in 84% accuracy when using the entire tumor volume for feature extraction and 74% accuracy for the central tumor region. A two-layer feedforward neural network using the same features is able to predict tumor growth with 16% mean squared error. Broadly applicable, these predictive models can use standard medical images to classify tumor type and predict tumor growth, with model performance, varying as a function of GLCM size, tumor region, and tumor type.en_US
dc.identifier.citationTang, Tien T., Zawaski, Janice A., Francis, Kathleen N., et al.. "Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study." <i>Scientific Reports,</i> 9, (2019) Springer Nature: https://doi.org/10.1038/s41598-019-48738-5.en_US
dc.identifier.digitals41598-019-48738-5en_US
dc.identifier.doihttps://doi.org/10.1038/s41598-019-48738-5en_US
dc.identifier.urihttps://hdl.handle.net/1911/107608en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/.en_US
dc.titleImage-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical studyen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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