Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients.
An-du ZhangQing-Lei ShiHong-Tao ZhangWen-Han DuanYang LiLi RuanYi-Fan HanZhi-Kun LiuHao-Feng LiJia-Shun XiaoGao-Feng ShiXiang WanRen-Zhi WangPublished in: Abdominal radiology (New York) (2024)
The pairwise NB model, based on pre-treatment enhanced chest CT-based radiomics and clinical factors, can accurately predict LR in elderly patients with ESCC. The esophageal cancer automated diagnostic system embedded with the pairwise NB model holds significant potential for application in clinical practice.
Keyphrases
- machine learning
- deep learning
- contrast enhanced
- clinical practice
- computed tomography
- image quality
- dual energy
- middle aged
- high throughput
- artificial intelligence
- early stage
- magnetic resonance imaging
- radiation therapy
- community dwelling
- positron emission tomography
- big data
- locally advanced
- magnetic resonance
- risk assessment
- free survival
- rectal cancer
- combination therapy
- human health
- replacement therapy