Abstract #3621

Deep Learning for Prediction of Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features

Ching-Chung Ko^1,2, Yan-Jen Chen^3,4, Hsun-Ping Hsieh³, and Jeon-Hor Chen^5,6

¹Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan, ²Chia Nan University of Pharmacy and Science, Tainan, Taiwan, ³Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ⁴Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan, ⁵Department of Radiological Sciences, University of California, Irvine, CA, United States, ⁶Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

Synopsis

A subset of nonfunctioning pituitary macroadenomas (NFMAs) show early progression/recurrence (P/R) after surgery. In clinical practice, one of the main challenges in the treatment of NFMAs is to determine factors that associated with P/R. This study investigated the role of deep learning for the prediction of P/R in NFMAs. 78 patients diagnosed with NFMAs were included. The hybrid CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy of 84%, precision of 88%, and AUC of 0.87.

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