JPRN-jRCT1020210021已完成未知
PREDICTION OF POSTOPERATIVE RECURRENCE IN INTRAHEPATIC CHOLANGIOCARCINOMA USING A DEEP LEARNING
Wakiya Taiichi0 个研究点目标入组 41 人开始时间: 2021年7月9日最近更新:
适应症
试验速览
- 阶段
- 未知
- 状态
- 已完成
- 发起方
- 入组人数
- 41
研究概览
简要总结
This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence in intrahepatic cholangiocarcinoma patients through the use of preoperative CT images. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Our CT-based DL model exhibited high predictive performance.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- >= 20age old 至 ot applicable（—）
- 性别
- All
入选标准
- •Patients undergoing liver surgery for intrahepatic cholangiocarcinoma at Hirosaki university hospital between 2000 and 2019.
- •Subjects must meet the following criteria to be enrolled in this study.
- •1. Age >20 years
- •2. Pathologically diagnosed with intrahepatic cholangiocarcinoma
排除标准
- •Patients who lacking clinicopathological findings
研究者
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