Abstract #2116

Radiomics and Machine Learning for Prediction of Relapsed and Refractory Primary Central Nervous System Lymphoma

Yan-Lin Liu¹, Ching-Chung Ko^2,3, Yang Zhang¹, Lee-Ren Yeh⁴, Jeon-Hor Chen¹, and Min-Ying Su¹

¹Department of Radiological Sciences, University of California, Irvine, CA, United States, ²Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan, ³Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan, ⁴Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan

Synopsis

Keywords: Tumors, Machine Learning/Artificial IntelligenceA subset of primary central nervous system lymphoma (PCNSL) may show early relapsed/refractory (R/R) disease after treatments. This study investigated the role of radiomics and machine learning for the prediction of R/R in PCNSL after treatments. Total 46 patients with pathologically confirmed PCNSL were included. Total 321 radiomic features were extracted from various pre-treatment MR sequences in each patient to build prediction models. Among various machine learning algorithms, the best predictive performance with accuracy of 82.6%, precision of 80%, and AUC of 0.85 were obtained in support vector machine.

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