Prediction of muscular-invasive bladder cancer using multi-view fusion self-distillation model based on 3D T2-Weighted images

膀胱癌 人工智能 矢状面 计算机科学 磁共振成像 模式识别(心理学) 医学 放射科 癌症 内科学
作者
Yuan Zou,Jie Yu,Lingkai Cai,Chunxiao Chen,Ruoyu Meng,Yueyue Xiao,Xue Fu,Jing Wang,Peikun Liu,Qiang Lü
出处
期刊:Biomedizinische Technik [De Gruyter]
标识
DOI:10.1515/bmt-2024-0333
摘要

Abstract Objectives Accurate preoperative differentiation between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for surgical decision-making in bladder cancer (BCa) patients. MIBC diagnosis relies on the Vesical Imaging-Reporting and Data System (VI-RADS) in clinical using multi-parametric MRI (mp-MRI). Given the absence of some sequences in practice, this study aims to optimize the existing T2-weighted imaging (T2WI) sequence to assess MIBC accurately. Methods We analyzed T2WI images from 615 BCa patients and developed a multi-view fusion self-distillation (MVSD) model that integrates transverse and sagittal views to classify MIBC and NMIBC. This 3D image classification method leverages z-axis information from 3D MRI volume, combining information from adjacent slices for comprehensive features extraction. Multi-view fusion enhances global information by mutually complementing and constraining information from the transverse and sagittal planes. Self-distillation allows shallow classifiers to learn valuable knowledge from deep layers, boosting feature extraction capability of the backbone and achieving better classification performance. Results Compared to the performance of MVSD with classical deep learning methods and the state-of-the-art MRI-based BCa classification approaches, the proposed MVSD model achieves the highest area under the curve (AUC) 0.927 and accuracy (Acc) 0.880, respectively. DeLong’s test shows that the AUC of the MVSD has statistically significant differences with the VGG16, Densenet, ResNet50, and 3D residual network. Furthermore, the Acc of the MVSD model is higher than that of the two urologists. Conclusions Our proposed MVSD model performs satisfactorily distinguishing between MIBC and NMIBC, indicating significant potential in facilitating preoperative BCa diagnosis for urologists.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
yy完成签到,获得积分10
1秒前
舒适仰发布了新的文献求助30
1秒前
1秒前
ding应助德德采纳,获得10
1秒前
2秒前
ywpzdnb发布了新的文献求助10
2秒前
李爱国应助张英俊采纳,获得10
2秒前
所所应助。。采纳,获得10
2秒前
风中听枫发布了新的文献求助10
4秒前
豌豆完成签到,获得积分10
6秒前
yuyu完成签到,获得积分10
7秒前
啦啦啦123完成签到,获得积分10
7秒前
7秒前
一桶雪碧完成签到,获得积分10
9秒前
Eureka.com完成签到,获得积分10
10秒前
充电宝应助落后幼晴采纳,获得10
10秒前
chloe发布了新的文献求助10
10秒前
12秒前
13秒前
斯文败类应助小栩采纳,获得10
13秒前
13秒前
13秒前
。。发布了新的文献求助10
17秒前
无问东西发布了新的文献求助10
17秒前
我是大兴发布了新的文献求助10
18秒前
18秒前
18秒前
打打应助heady采纳,获得10
19秒前
22秒前
聪明帅哥完成签到,获得积分10
23秒前
23秒前
24秒前
24秒前
研友_VZG7GZ应助琉璃采纳,获得10
25秒前
henry先森完成签到,获得积分10
25秒前
欢呼星星发布了新的文献求助10
25秒前
。。完成签到,获得积分10
25秒前
XW发布了新的文献求助10
25秒前
星辰大海应助Garry采纳,获得10
26秒前
小栩完成签到,获得积分10
26秒前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
Diagnostic immunohistochemistry : theranostic and genomic applications 6th Edition 500
Chen Hansheng: China’s Last Romantic Revolutionary 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3149204
求助须知:如何正确求助?哪些是违规求助? 2800294
关于积分的说明 7839427
捐赠科研通 2457845
什么是DOI,文献DOI怎么找? 1308138
科研通“疑难数据库(出版商)”最低求助积分说明 628436
版权声明 601706