Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany.
Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, Box 356043, Seattle, WA 98195, United States of America.
EBioMedicine. 2019 Oct;48:332-340. doi: 10.1016/j.ebiom.2019.08.059. Epub 2019 Sep 12.
Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS.
The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects.
Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone.
MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. FUND: The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.
软组织肉瘤（STS）多模态治疗的决策在很大程度上取决于低级别和高级别肿瘤的区分。我们开发了基于 MRI 的放射组学分级模型，用于区分低级别（G1）和高级别（G2/G3）STS。
该研究在 ClinicalTrials.gov 注册（编号 NCT03798795）。从两个独立的回顾性患者队列中收集了术前活检获得的对比增强 T1 加权脂肪饱和（T1FSGd）、脂肪饱和 T2 加权（T2FS）MRI 序列和肿瘤分级，遵循法国癌症中心肉瘤组的标准。手动分割感兴趣体积。预处理后，从每个序列中提取 1394 个放射组学特征。从 21 个独立的多分割中提取不稳定的特征被排除。使用嵌套交叉验证在训练患者队列（122 例）上开发最小绝对收缩和选择算子模型。使用 ComBatHarmonization 评估了校正批次效应的影响。
基于 T2FS、T1FSGd 和联合模型的三个放射组学模型在独立验证集（103 例）上的预测性能分别为接收器操作特征曲线（AUC）下面积为 0.78、0.69 和 0.76。基于 T2FS 的模型显示出最佳的可重复性。基于 T1FSGd 特征的放射组学模型实现了显著的患者分层。将 T1FSGd 基于特征的放射组学模型纳入与临床分期相结合的列线图可提高预后性能，并提高临床分期单独应用的临床净获益。
基于 MRI 的放射组学肿瘤分级模型可有效区分低级别和高级别软组织肉瘤。
作者得到了慕尼黑工业大学医学院和德国癌症协会的支持。
