利用机器学习进行定量纹理分析,从肺栓塞患者的非对比计算机断层扫描中预测可解释的肺灌注。

IF 5.8 2区 医学 Q1 Medicine Respiratory Research Pub Date : 2024-10-28 DOI:10.1186/s12931-024-03004-9
Zihan Li, Meixin Zhao, Zhichun Li, Yu-Hua Huang, Zhi Chen, Yao Pu, Mayang Zhao, Xi Liu, Meng Wang, Kun Wang, Martin Ho Yin Yeung, Lisheng Geng, Jing Cai, Weifang Zhang, Ruijie Yang, Ge Ren
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引用次数: 0

摘要

背景:肺栓塞(PE)危及生命,需要及时、准确的诊断,但目前的成像方法,如计算机断层扫描肺血管造影术,存在局限性,尤其是对有碘造影剂禁忌症的患者。我们的目标是基于非对比胸部计算机断层扫描(CT),利用机器学习(ML)技术开发一种定量纹理分析管道,以发现与区域肺灌注(Q)生理和病理相关的强度和纹理特征,并合成体素Q替代物来协助PE诊断:我们回顾性地收集了疑似 PE 患者的 99mTc 标记大聚集白蛋白 Q-SPECT/CT 扫描图像,包括 76 例患者的内部数据集(64 例用于训练,12 例用于测试)和 49 例患者的外部测试数据集。从分割的肺部亚区提取定量 CT 特征,并进行两阶段特征选择。先验知识驱动的预选阶段筛选稳健、非冗余的灌注相关特征,而数据驱动的选择阶段则通过拟合 ML 模型进行分类,进一步筛选特征。最终的分类模型是用表现最好的 PE 相关特征组合训练出来的,在测试队列中根据曲线下面积(AUC)评估子区域级预测性。然后使用最终选定的特征图(FM)和模型得分图(MSM)合成体素Q代用值,以研究空间分布。斯皮尔曼相关系数(SCC)和骰子相似性系数(DSC)用于评估 FMs 或 MSMs 与 Q-SPECT 扫描之间的空间一致性:在内部测试中,最佳模型的 AUC 为 0.863,在外部测试队列中为 0.828。该模型确定了包含 14 个强度和纹理特征的组合,这些特征是非冗余的、稳健的,能够区分高功能和低功能肺区。内部测试队列的空间一致性评估显示,MSM 与参考 Q-SPECT 扫描之间的一致性为中度到高度一致,高功能区和低功能区的 SCC 中位数分别为 0.66,DSC 中位数分别为 0.86 和 0.64:这项研究验证了使用定量纹理分析和数据驱动的ML管道生成体素肺灌注替代物的可行性,为管理肺血管疾病提供了一种无辐射、可广泛使用的肺功能成像替代方法:临床试验编号:不适用。
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Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients.

Background: Pulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.

Methods: We retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.

Results: The optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.

Conclusions: This study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.

Clinical trial number: Not applicable.

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来源期刊
Respiratory Research
Respiratory Research RESPIRATORY SYSTEM-
CiteScore
9.70
自引率
1.70%
发文量
314
审稿时长
4-8 weeks
期刊介绍: Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.
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