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基于 CT 的放射组学特征用于区分心脏肿瘤和血栓:一项回顾性、多中心研究。

CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study.

机构信息

Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, South Korea.

Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

出版信息

Sci Rep. 2022 May 17;12(1):8173. doi: 10.1038/s41598-022-12229-x.

Abstract

The study aimed to develop and validate whether the computed tomography (CT) radiomics analysis is effective in differentiating cardiac tumors and thrombi. For this retrospective study, a radiomics model was developed on the basis of a training dataset of 192 patients (61.9 ± 13.3 years, 90 men) with cardiac masses detected in cardiac CT from January 2010 to September 2019. We constructed three models for discriminating between a cardiac tumor and a thrombus: a radiomics model, a clinical model, which included clinical and conventional CT variables, and a model that combined clinical and radiomics models. In the training dataset, the radiomics model and the combined model yielded significantly higher differentiation performance between cardiac tumors and cardiac thrombi than the clinical model (AUC 0.973 vs 0.870, p < 0.001 and AUC 0.983 vs 0.870, p < 0.001, respectively). In the external validation dataset with 63 patients (59.8 ± 13.2 years, 26 men), the combined model yielded a larger AUC compared to the clinical model (AUC 0.911 vs 0.802, p = 0.037). CT radiomics analysis is effective in differentiating cardiac tumors and thrombi. In conclusion, the combination of clinical, conventional CT, and radiomics features demonstrated an additional benefit in differentiating between cardiac tumor and thrombi compared to clinical data and conventional CT features alone.

摘要

本研究旨在开发和验证 CT 放射组学分析是否能有效区分心脏肿瘤和血栓。在这项回顾性研究中,我们基于 2010 年 1 月至 2019 年 9 月期间在心脏 CT 上检测到的 192 名患者(61.9±13.3 岁,90 名男性)的心脏肿块训练数据集,开发了一个放射组学模型。我们构建了三个用于区分心脏肿瘤和血栓的模型:一个放射组学模型、一个包含临床和常规 CT 变量的临床模型,以及一个结合临床和放射组学模型的模型。在训练数据集中,放射组学模型和联合模型在区分心脏肿瘤和心脏血栓方面的表现明显优于临床模型(AUC 0.973 与 0.870,p<0.001 和 AUC 0.983 与 0.870,p<0.001)。在包含 63 名患者(59.8±13.2 岁,26 名男性)的外部验证数据集中,联合模型的 AUC 大于临床模型(AUC 0.911 与 0.802,p=0.037)。CT 放射组学分析可有效区分心脏肿瘤和血栓。总之,与临床数据和常规 CT 特征相比,临床、常规 CT 和放射组学特征的结合在区分心脏肿瘤和血栓方面具有额外的益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5760/9114026/f1dda622a4af/41598_2022_12229_Fig6_HTML.jpg

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