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Abstract #2848

Diffusion Tensor and Conventional Imaging Radiomics Features to Differentiate the EGFR Mutation Status of Brain Metastases from Non-Small Cell Lung Cancer

Yae Won Park1, Seng Chan You2, Jongmin Oh1, Sang Wook Kim3, Kyunghwa Han4, Sung Soo Ahn4, Sung Jun Ahn5, Hwiyoung Kim4, Jong Hee Chang4, Se Hoon Kim4, and Seung-Koo Lee4

1Ewha Womans University College of Medicine, Seoul, Korea, Republic of, 2Ajou University School of Medicine, Suwon, Korea, Republic of, 3Korea University, Seoul, Korea, Republic of, 4Yonsei University College of Medicine, Seoul, Korea, Republic of, 5Gangnam Severance Hospital, Seoul, Korea, Republic of

We assessed whether radiomics features on diffusion tensor imaging and postcontrast T1-weighted (T1C) images differentiates the epidermal growth factor receptor (EGFR) status in brain metastases from non-small cell lung cancer (NSCLC). Radiomics features (n=5046) were extracted from 54 brain metastases patients with NSCLC (29 EGFR-wildtype, 25 EGFR-mutant). After feature selection, radiomics models were constructed by various machine learning algorithms. Diagnostic performances were compared between multiparametric and single MRI radiomics models. The best performing multiparametric radiomics model (AUC 0.97) showed better performance than any single radiomics model using ADC (AUC 0.79, p=0.007), FA (AUC 0.75, p=0.001), or T1C (AUC 0.96, p=0.678).

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