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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