Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network

Abstract

To achieve a 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 developing a PFS prediction model of NPC patients after IMRT treatment using a deep learning method and comparing that with the traditional texture analysis method. One hundred and fifty‐one NPC patients were included in this retrospective study. T1‐weighted, proton density and dynamic contrast‐enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF‐1α, EGFR, PTEN, Ki‐67, and VEGF) and infection of Epstein–Barr (EB) virus were tested. A residual network was trained to predict PFS from MR images. The output as well as patient characteristics were combined using a linear regression model to provide a final PFS prediction. The prediction accuracy was compared with that of the traditional texture analysis method. A regression model combining the deep learning output with HIF‐1α expression and Epstein–Barr infection provides the best PFS prediction accuracy (Spearman correlation R2 = 0.53; Harrell's C‐index = 0.82; receiver operative curve [ROC] analysis area under the curve [AUC] = 0.88; log‐rank test hazard ratio [HR] = 8.45), higher than a 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). The deep learning method does not require a manually drawn tumor region of interest. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and does not rely on specific kernels or tumor regions of interest, which is needed for the texture analysis method.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis

“…The allocation of articles from each database and the refinement process to identify relevant studies are detailed in Table S2. After comprehensive analysis, 15 studies were included in our meta-analysis [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], with the exclusion rationale documented in Table S3 [8,10,34,.…”

Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma

“…Moreover, the 4-mRNA signature was related to the immune response as well as cell proliferation [98]. Zhang et al used the deep network to predict the prognosis of NPC based on MRI and gene expression, and the AUC was 0.88 [99].…”

Application of Deep Learning in Cancer Prognosis Prediction Model

“…The study confirmed the great potential of DRN and its derived models for predicting survival in patients with rectal cancer. Zhang et al 28 used DRN to develop a prediction model for 1-year progression-free survival after intensity-modulated radiotherapy in patients with nasopharyngeal carcinoma and compared it with the conventional texture analysis. The final AUC of the DRN model based on MRI images was 0.85, and the AUC was improved to 0.88 after combining genetic data, both higher than that of texture analysis at 0.76.…”