Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mu
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DIAGNOSTIC NEURORADIOLOGY
Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer Yae Won Park 1 & Chansik An 2 & JaeSeong Lee 3 & Kyunghwa Han 1 & Dongmin Choi 4 & Sung Soo Ahn 1 Hwiyoung Kim 1 & Sung Jun Ahn 5 & Jong Hee Chang 6 & Se Hoon Kim 7 & Seung-Koo Lee 1
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Received: 28 June 2020 / Accepted: 16 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC). Methods A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC). Results EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively. Conclusions Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC. Keywords Epidermal growth factor receptor . Imaging . Diffusion tensor . Machine learning . Magnetic resonance imaging . Radiomics
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00234-020-02529-2) contains supplementary material, which is available to authorized users. * Sung Soo Ahn [email protected] 1
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Department of Mechanical Engineering, Yonsei University, Seoul, Korea
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Department of Computer Science, Yonsei University, Seoul, Korea
Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
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Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
Research and Analysis Team, National Health Insurance Service Ilsan Hospital, Goya
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