用于预测软组织肉瘤肺转移的多参数 MRI 放射组学:可行性研究。
Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study.
发表日期:2024 Sep 05
作者:
Yue Hu, Xiaoyu Wang, Zhibin Yue, Hongbo Wang, Yan Wang, Yahong Luo, Wenyan Jiang
来源:
CANCER IMAGING
摘要:
旨在探讨基于多参数 MRI 的放射组学对于术前预测软组织肉瘤 (STS) 肺转移的价值。总共 122 名临床病理学确诊的 STS 患者接受了治疗前 T1 加权对比增强 (T1-CE) 和2017 年 7 月至 2021 年 3 月期间登记了 T2 加权脂肪抑制 (T2FS) MRI 扫描。通过计算和选择两个序列的放射组学特征来建立放射组学特征。通过统计分析评估临床独立预测因素。放射组学列线图是通过多变量逻辑回归根据边缘和放射组学特征构建的。最后,该研究使用受试者工作特征(ROC)和校准曲线来评估放射组学模型的性能。进行决策曲线分析(DCA)来评估模型的临床实用性。边缘被认为是独立的预测因子(p<<0.05)。总共选择了 4 个 MRI 特征并用于开发放射组学特征。通过合并边缘和放射组学特征,开发的列线图在训练(AUC,边缘与放射组学特征与列线图,0.609 vs. 0.909 vs. 0.910)和验证(AUC,边缘与放射组学特征与列线图,AUC,边缘与放射组学特征与列线图,0.666 vs. 0.841 vs. 0.894) 队列。 DCA 表明了列线图模型的潜在用途。这项可行性研究评估了多参数 MRI 对肺转移预测的预测价值,并提出了列线图模型,以潜在促进 STS 的个体化治疗决策。© 2024。作者( s)。
To investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS).In total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models.The margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model.This feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.© 2024. The Author(s).