Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma

列线图 鼻咽癌 医学 接收机工作特性 一致性 置信区间 人工智能 内科学 深度学习 肿瘤科 无进展生存期 放射科 总体生存率 放射治疗 计算机科学
作者
Bingxin Gu,Mingyuan Meng,Mingzhen Xu,David Dagan Feng,Lei Bi,Jinman Kim,Shaoli Song
出处
期刊:European Journal of Nuclear Medicine and Molecular Imaging [Springer Science+Business Media]
卷期号:50 (13): 3996-4009 被引量:1
标识
DOI:10.1007/s00259-023-06399-7
摘要

Abstract Purpose Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. Methods A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [ 18 F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan–Meier method and compared with the observed PFS probability. Results Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785–0.851), 0.752 (95% CI: 0.638–0.865), and 0.717 (95% CI: 0.641–0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822–0.895), 0.769 (95% CI: 0.642–0.896), and 0.730 (95% CI: 0.634–0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. Conclusion Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.
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