使用高b值非高斯连续时间随机步行模型进行乳腺癌诊断和预后
1Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Clinical radiology
|June 21, 2023
概括
连续时间随机步行 (CTRW) 模型在区分恶性乳腺病变方面显示出前景. 来自CTRW和表面扩散系数 (ADC) 模型的综合扩散参数可以识别激素受体状态,并可以预测预后.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 在瘤学瘤学.
背景情况:
- 准确区分恶性乳腺病变对于患者管理至关重要.
- 扩散加权成像 (DWI) 参数,如明显扩散系数 (ADC),用于评估组织微观结构.
- 先进的扩散模型为改善诊断性能提供了潜力.
研究的目的:
- 为了比较单指数ADC和连续时间随机步行 (CTRW) 模型参数 (Dm,α,β) 在区分乳腺病变的诊断性能.
- 调查这些扩散参数与与预后相关的免疫组织化学指数之间的关联.
主要方法:
- 追溯分析了85名乳腺病变患者 (51名恶性,34名良性).
- 表面扩散系数 (ADC) 采用单指数模型;Dm,α和β采用CTRW模型.
- 接收器操作特征 (ROC) 分析差异化性能和斯皮尔曼的相关性与免疫组织化学指数的关联.
主要成果:
- ADC和Dm显示了类似的诊断性能 (AUC ≈0.93).
- Dm,α和β的组合改善了病变歧视的AUC,达到0.969.
- 综合扩散参数区分ER+/ER-和PR+/PR-患者;α与PR表达相关.
结论:
- CTRW模型扩散参数有效地区分乳腺病变恶性.
- 综合扩散参数可以区分激素受体状态,并可能反映预后.
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