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基于原生 T1 映射的自动机器学习可以识别肥厚型心肌病患者的心肌纤维化。

Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy.

机构信息

Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Philips Healthcare, Guangzhou, Guangdong, China.

出版信息

Eur Radiol. 2022 Feb;32(2):1044-1053. doi: 10.1007/s00330-021-08228-7. Epub 2021 Sep 3.

Abstract

OBJECTIVES

To investigate the feasibility of automatic machine learning (autoML) based on native T1 mapping to predict late gadolinium enhancement (LGE) status in hypertrophic cardiomyopathy (HCM).

METHODS

Ninety-one HCM patients and 44 healthy controls who underwent cardiovascular MRI were enrolled. The native T1 maps of HCM patients were classified as LGE ( +) or LGE (-) based on location-matched LGE images. An autoML pipeline was implemented using the tree-based pipeline optimization tool (TPOT) for 3 binary classifications: LGE ( +) and LGE (-), LGE (-) and control, and HCM and control. TPOT modeling was repeated 10 times to obtain the optimal model for each classification. The diagnostic performance of the best models by slice and by case was evaluated using sensitivity, specificity, accuracy, and microaveraged area under the curve (AUC).

RESULTS

Ten prediction models were generated by TPOT for each of the 3 binary classifications. The diagnostic accuracy obtained with the best pipeline in detecting LGE status in the testing cohort of HCM patients was 0.80 by slice and 0.79 by case. In addition, the TPOT model also showed discriminability between LGE (-) patients and control (accuracy: 0.77 by slice; 0.78 by case) and for all HCM patients and controls (accuracy: 0.88 for both).

CONCLUSIONS

Native T1 map analysis based on autoML correlates with LGE ( +) or (-) status. The TPOT machine learning algorithm could be a promising method for predicting myocardial fibrosis, as reflected by the presence of LGE in HCM patients without the need for late contrast-enhanced MRI sequences.

KEY POINTS

• The tree-based pipeline optimization tool (TPOT) is a machine learning algorithm that could help predict late gadolinium enhancement (LGE) status in patients with hypertrophic cardiomyopathy. • The TPOT could serve as an adjuvant method to detect LGE by using information from native T1 maps, thus avoiding the need for contrast agent. • The TPOT also detects native T1 map alterations in LGE-negative patients with hypertrophic cardiomyopathy.

摘要

目的

探究基于 native T1 映射的自动机器学习(autoML)预测肥厚型心肌病(HCM)患者延迟钆增强(LGE)状态的可行性。

方法

本研究纳入了 91 例 HCM 患者和 44 例健康对照者,所有患者均接受了心血管 MRI 检查。根据匹配 LGE 图像的位置,将 HCM 患者的 native T1 图谱分为 LGE(+)或 LGE(-)。采用基于树的管道优化工具(TPOT)为 3 个二分类构建了一个 autoML 管道:LGE(+)和 LGE(-)、LGE(-)和对照组、HCM 和对照组。对每种分类,TPOT 模型重复 10 次以获得最佳模型。通过切片和病例评估最佳模型的诊断性能,使用敏感性、特异性、准确性和微平均曲线下面积(AUC)进行评估。

结果

TPOT 为 3 个二分类中的每一个都生成了 10 个预测模型。在 HCM 患者的测试队列中,使用最佳管道检测 LGE 状态的诊断准确性为 0.80 (切片)和 0.79(病例)。此外,TPOT 模型还可以区分 LGE(-)患者和对照组(准确性:切片为 0.77;病例为 0.78),以及所有 HCM 患者和对照组(准确性:均为 0.88)。

结论

基于 autoML 的 native T1 图谱分析与 LGE(+)或(-)状态相关。TPOT 机器学习算法可以作为预测 HCM 患者心肌纤维化的一种有前途的方法,这反映在无需使用对比增强 MRI 序列的情况下,通过存在 LGE 来预测 HCM 患者。

关键点

• 基于树的管道优化工具(TPOT)是一种机器学习算法,可帮助预测肥厚型心肌病患者的延迟钆增强(LGE)状态。• TPOT 可以作为通过 native T1 图谱检测 LGE 的辅助方法,从而避免使用造影剂。• TPOT 还可以检测到肥厚型心肌病患者中存在 native T1 图谱改变的 LGE 阴性患者。

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