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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
yaowenjun完成签到,获得积分10
1秒前
2秒前
puff完成签到,获得积分10
2秒前
学术通zzz完成签到,获得积分10
2秒前
zzz发布了新的文献求助10
2秒前
认真草丛完成签到,获得积分10
2秒前
虚幻初之完成签到,获得积分10
2秒前
zcx970206完成签到,获得积分10
3秒前
简单发布了新的文献求助10
3秒前
内向小熊猫完成签到,获得积分10
4秒前
自行车v完成签到,获得积分10
4秒前
小马甲应助徐木木采纳,获得10
4秒前
4秒前
完美蚂蚁发布了新的文献求助10
4秒前
Noora完成签到,获得积分10
5秒前
pp996发布了新的文献求助10
5秒前
小蘑菇应助飞云采纳,获得10
5秒前
白衬衫不好洗完成签到,获得积分10
5秒前
活力惜海完成签到,获得积分20
5秒前
6秒前
研究啥完成签到,获得积分10
6秒前
6秒前
gqp完成签到,获得积分10
6秒前
桃子完成签到 ,获得积分10
6秒前
所所应助过柱菜鸟采纳,获得10
6秒前
myjf完成签到,获得积分20
6秒前
6秒前
但行好事完成签到,获得积分10
7秒前
无辜的朋友应助研友_ZAxQqn采纳,获得10
7秒前
7秒前
慕青应助蓝桉采纳,获得10
8秒前
舒适的藏花完成签到 ,获得积分10
8秒前
Owen应助顺利的奇异果采纳,获得10
8秒前
纵马长歌完成签到,获得积分10
8秒前
分析完成签到 ,获得积分10
8秒前
努力小温完成签到,获得积分10
8秒前
王小嘻完成签到,获得积分10
9秒前
佛了欢喜完成签到,获得积分10
9秒前
平常囧完成签到,获得积分10
9秒前
9秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968844
求助须知:如何正确求助?哪些是违规求助? 3513769
关于积分的说明 11169920
捐赠科研通 3249095
什么是DOI,文献DOI怎么找? 1794630
邀请新用户注册赠送积分活动 875278
科研通“疑难数据库(出版商)”最低求助积分说明 804755