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Abstract #2659

External validation of a machine learning algorithm for differentiating between myelodysplastic syndromes and aplastic anemia

Miyuki Takasu1, Takashi Abe2, Shogo Maeda1, Yasutaka Baba1, Yuji Akiyama1, Yuji Takahashi1, Hideaki Kakizawa3, and Kazuo Awai1
1Department of Diagnostic Radiology, Hiroshima University Hospital, Hiroshima, Japan, 2Department of Radiology, Tokushima University, Tokushima, Japan, 3Department of Radiology, Hiroshima Red Cross Hospital, Hiroshima, Japan

An MRI-based predictive model was built to differentiate between myelodysplastic syndrome (MDS) and aplastic anemia (AA). The conventional multiparametric MRI provided correct diagnosis with a support vector machine model at accuracies up to 78.0% with a combination of age, fat fraction, and platelet count. In an external validation, the LeNet model achieved an accuracy of 80.0%, sensitivity of 80.0%, specificity of 81.7%, and AUC of 0.860 for T1WI and an accuracy of 65.6%, sensitivity of 65.6%, specificity of 65.3%, and AUC of 0.667 for STIR images. The machine learning algorithm proved effective for differentiating MDS from AA.

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