Deep Learning for Prediction of Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features
Ching-Chung Ko1,2, Yan-Jen Chen3,4, Hsun-Ping Hsieh3, and Jeon-Hor Chen5,6
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
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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