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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\u2010one NPC patients were included in this retrospective study. T1\u2010weighted, proton density and dynamic contrast\u2010enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF\u20101\u03b1, EGFR, PTEN, Ki\u201067, and VEGF) and infection of Epstein\u2013Barr (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\u20101\u03b1 expression and Epstein\u2013Barr infection provides the best PFS prediction accuracy (Spearman correlation <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u00a0=\u00a00.53; Harrell's C\u2010index\u00a0=\u00a00.82; receiver operative curve [ROC] analysis area under the curve [AUC] =\u20090.88; log\u2010rank test hazard ratio [HR]\u00a0=\u00a08.45), higher than a regression model combining texture analysis with HIF\u20101\u03b1 expression (Spearman correlation <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u00a0=\u00a00.14; Harrell's C\u2010index =0.68; ROC analysis AUC\u00a0=\u00a00.76; log\u2010rank test HR\u00a0=\u00a02.85). 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.<\/jats:p>","DOI":"10.1111\/cas.15704","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T12:04:56Z","timestamp":1671624296000},"page":"1596-1605","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Survival rate prediction of nasopharyngeal carcinoma patients based on <scp>MRI<\/scp> and gene expression using a deep neural network"],"prefix":"10.1111","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-5849","authenticated-orcid":false,"given":"Qihao","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology Weill Cornell Medical College  New York New York 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