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来自不同MRI序列的放射性特征的比较,用于预测直肠癌后同步性肝转移

Apekshya Singh¹, Sheng-Ming Shi¹, Han Liu¹

¹Department of Magnetic Resonance Imaging, The Second Affiliated Hospital, Harbin Medical University, Harbin 150086, Heilongjiang Province, China.

Current medical imaging

|December 2, 2025

概括

放射学和随机森林模型对检测直肠癌患者同步肝转移有希望. 这种方法有助于早期检测和个性化治疗计划.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
医疗成像医学成像

背景情况:

同步性肝转移 (SLM) 显著影响直肠癌的预后.
精确的手术前检测SLM对于有效的治疗计划至关重要.
这项研究评估了MRI的放射性特征,用于SLM检测.

研究的目的:

为了比较T2加权成像 (T2WI) 和扩散加权成像 (DWI) MRI的放射性特征的预测性能.
开发和评估用于早期SLM检测直肠癌的机器学习模型.

主要方法:

对137名直肠癌患者 (71名有SLM,66名没有) 的回顾性分析.
使用Pyradiomics从T2WI和DWI提取3,452个放射性特征.
使用后勤回归 (LR),支持矢量机 (SVM) 和随机森林 (RF) 进行预测模型的开发.

主要成果:

随机森林 (RF) 模型的表现优于LR和SVM.
最优的射频模型实现了0.82的AUC和0.71的SLM检测的准确性.
组合最佳射频模型也表现出强的性能 (AUC = 0.76).

结论:

放射学与射频模型相结合,为早期SLM检测和风险分层提供了一个有希望的非侵入性工具.
这种方法可以改善临床决策,并指导直肠癌患者的个性化治疗.
使用射频的最佳特征集基于预测模型显示出卓越的准确性和平衡的诊断性能.

关键词:

扩散权重成像技术的使用.这是ITK-SNAP.拉索·拉索 (Lasso) 是一个机器学习是机器学习.预测模型是一个预测模型.无线电学 (Radiomics) 是一种无线电学.随机的森林随机的森林这是直肠癌.同步性肝脏转移.用T2加权成像技术进行成像.

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