结合双参数膀成像报告和数据系统和明显扩散系数指标的决策树模型,用于预测肌肉侵入性膀癌
Daichi Sugawara1, Kento Hatakeyama1, Motoko Konno1
1Department of Radiology, Akita University Graduate School of Medicine, Akita, Japan.
Abdominal radiology (New York)
|January 23, 2026
概括
整体损伤明显扩散系数 (ADC) 组图分析和代表性ADC值在评估膀癌肌肉入侵时显示出类似的准确性. 将双参数膀成像报告和数据系统 (bp VI-RADS) 与决策树模型中的ADC测量相结合,可以提高诊断性能.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 医疗成像医学成像
背景情况:
- 膀癌的肌肉入侵对于治疗决策至关重要.
- 准确评估肌肉侵袭影响患者的结果.
- 核磁共振扫描在膀癌的非侵入性分期中起着至关重要的作用.
研究的目的:
- 为了比较整个病变的明显扩散系数 (ADC) 组图分析与代表的ADC值,以评估膀癌中的肌肉侵袭.
- 开发一个决策树模型,将双参数膀成像报告和数据系统 (bp VI-RADS) 与ADC值集成,以提高诊断准确度.
主要方法:
- 追溯分析了82名膀癌患者接受3T核磁共振.
- 使用T2加权,扩散加权成像和ADC地图对BPVI-RADS进行评分.
- 整体损伤ADC组图分析和从代表性感兴趣区域计算最低平均ADC.
- 肌肉侵入性 (MIBC) 和非肌肉侵入性膀癌 (NMIBC) 之间的ADC参数的比较.
- 使用ROC曲线分析评估诊断性能;使用后勤回归和决策树分析构建的组合模型.
主要成果:
- 第25百分点ADC和最小平均ADC显示了类似的诊断性能,它们之间有很强的相关性.
- 单个参数 (bp VI-RADS,第25百分位数ADC,最小平均ADC) 的准确性相似 (0.74-0.76).
- 将bp VI-RADS与ADC参数相结合的物流回归模型实现了更高的准确性 (0.87-0.88).
- 结合bp VI-RADS和ADC的决策树模型实现了0.80-0.82的精度,特别是对bp VI-RADS 4病变的分层.
结论:
- 第25百分点ADC和最小平均ADC都为预测膀癌的肌肉入侵提供了类似的诊断实用.
- 结合bp VI-RADS和ADC测量的决策树模型提供了一个可解释和临床适用的工具.
- 这种综合方法对于完善对bp VI-RADS 4病变的评估尤其有益.
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