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Abstract #2463

Machine learning of DCE MRI intensity histogram radiomic features for pulmonary lesion classification

Wei Wu1, Chunyan Duan2, Nina A Mayr2, William T Yuh3, Liming Xia1, Daniel S Hippe3, and Stephen R Bowen2

1Radiology, Tongji Medical college affiliated to Huazhong University of Science and Technology, Wuhan, China, 2Radiation Oncology, University of Washington, Seattle, WA, United States, 3Radiology, University of Washington, Seattle, WA, United States

To classify malignant/benign lesions can be challenging and non-invasive means to further improve the diagnostic accuracy would have major impact on management in patients with pulmonary lesions. 62 patients with histologically confirmed pulmonary lesions were retrospectively reviewed. Intensity voxel histogram (IH) features were extracted from DCE-MRI. The efficacy of IH features to classify pulmonary lesions were assessed by correlation with pathology. Under cross-validation, a support vector machine algorithm achieved a diagnostic accuracy, sensitivity and specificity of 95%, 99 and 86%. Our results demonstrate that machine learning of DCE-MRI IH features has potential for accurately classifying pulmonary lesions for clinical translation.

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