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Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs.
Computer Methods and Programs in Biomedicine ( IF 6.1 ) Pub Date : 2020-08-02 , DOI: 10.1016/j.cmpb.2020.105684
Bingzhong Jing 1 , Yishu Deng 1 , Tao Zhang 2 , Dan Hou 2 , Bin Li 1 , Mengyun Qiang 3 , Kuiyuan Liu 3 , Liangru Ke 4 , Taihe Li 5 , Ying Sun 6 , Xing Lv 3 , Chaofeng Li 1
Affiliation  

Background

Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients.

Objective

To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients.

Methods

In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient.

Result

A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610).

Conclusions

The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.



中文翻译:

使用多参数MRI进行深度学习以预测鼻咽癌患者的风险。

背景

磁共振图像(MRI)是鼻咽癌(NPC)风险分层和治疗决策的主要诊断工具。但是,临床医生尚未充分利用多参数MRI的整体特征信息来准确评估患者。

目的

为了帮助临床医生充分利用错过的信息对患者进行重组,我们建立了端到端的深度学习模型,以从多参数MRI中提取特征信息,以预测和分层NPC患者的风险评分。

方法

在本文中,我们提出了一种端到端的多模式深度生存网络(MDSN),以精确预测NPC患者疾病进展的风险。从3D密集网络扩展,此提议的MDSN从多参数MRI(T1w,T2w和T1c)中提取了深度表示。此外,通过MDSN整合了深层特征和临床分期,可以更准确地预测单个NPC患者的总体风险评分(ORS)。

结果

纳入了2012年1月至2014年12月之间接受治疗的1,417个人,以培训和验证端到端MDSN。然后在同一机构的429例患者的回顾性队列中测试了结果。所提出的有或没有临床分期方法的C指数在测试集上分别为0.672和0.651,高于分期分组的C指数(0.610)。

结论

整合了具有较深特征的临床分期的模型的C指数比单独分期分组的C指数高0.062(0.672对0.610)。我们得出的结论是,从基于MDSN的多参数MRI提取的特征可以很好地帮助患者重组。

更新日期:2020-08-02
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