In Cancer science
To achieve better treatment regimen and follow-up assessment design for intensity-modulated radiotherapy (IMRT) treated nasopharyngeal carcinoma (NPC) patients, an accurate progression free survival (PFS) time prediction algorithm is needed. We propose to develop PFS prediction model of NPC patients after IMRT treatment using deep learning method, and to compare that with traditional texture analysis method. 151 NPC patients were included in this retrospective study. T1 weighted, proton density and dynamic contrast enhanced MR images were acquired. Expression level of five genes (HIF-1α, EGFR, PTEN, Ki-67, VEGF) and infection of Epstein-Barr virus were tested. A residual network was trained to predict PFS from magnetic resonance (MR) images. The output as well as patient characteristics were combined using linear regression model to give a final PFS prediction. The prediction accuracy was compared with traditional texture analysis method. Regression model combining deep learning output with HIF-1α expression and EB infection gives the best PFS prediction accuracy (Spearman correlation R2 =0.53; Harrell's C-index = 0.82; ROC analysis AUC=0.88; log rank test HR= 8.45), higher than regression model combining texture analysis with HIF-1α expression (Spearman correlation R2 =0.14; Harrell's C-index =0.68; ROC analysis AUC=0.76; log rank test HR= 2.85). Deep learning method doesn't require manually drawn tumor ROI, thus is fully automatic. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and doesn't rely on specific kernels or tumor ROI as texture analysis method.
Zhang Qihao, Wu Gang, Yang Qianyu, Dai Ganmian, Li Tiansheng, Chen Pianpian, Li Jiao, Huang Weiyuan
2022-Dec-21
deep neural network, intensity-modulated radiotherapy, multi-contrast MRI, nasopharyngeal carcinoma, progression-free survival prediction
