Wang Bo, Guo Hongwei, Zhang Meng, Huang Yonghua, Duan Lisha, Huang Chencui, Xu Jun, Wang Hexiang
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Department of Operation Center, Women and Children's Hospital, Qingdao University, Qingdao, Shandong, China.
Front Oncol. 2024 Dec 11;14:1433196. doi: 10.3389/fonc.2024.1433196. eCollection 2024.
Accurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS).
To construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral habitats (subregions of clusters of voxels containing similar features) and peritumoral features for the preoperative prediction of the pathological grade of STS.
The MRI data of 145 patients with STS (74 low-grade and 71 high-grade) from 4 hospitals were retrospectively collected, including enhanced T1-weighted and fat-suppressed-T2-weighted sequences. The patients were divided into training cohort (n = 102) and validation cohort (n = 43). K-means clustering was used to divide intratumoral voxels into three habitats according to signal intensity. A number of radiomics features were extracted from tumor-related regions to construct radiomics prediction signatures for seven subgroups. Logistic regression analysis identified peritumoral edema as an independent risk factor. A nomogram was created by merging the best radiomics signature with the peritumoral edema. We evaluated the performance and clinical value of the model using area under the curve (AUC), calibration curves, and decision curve analysis.
A multi-layer perceptron classifier model based on intratumoral habitats and peritumoral features combined gave the best radiomics signature, with an AUC of 0.856 for the validation cohort. The AUC of the nomogram in the validation cohort was 0.868, which was superior to the radiomics signature and the clinical model established by peritumoral edema. The calibration curves and decision curve analyses revealed good calibration and a high clinical application value for this nomogram.
The MRI-based nomogram is accurate and effective for predicting preoperative grading in patients with STS.
术前准确识别病理分级有助于指导软组织肉瘤（STS）的临床治疗决策并改善预后。
构建并评估一种基于磁共振成像（MRI）的影像组学列线图，该列线图纳入瘤内特征（包含相似特征的体素簇的子区域）和瘤周特征，用于术前预测STS的病理分级。
回顾性收集来自4家医院的145例STS患者（74例低级别和71例高级别）的MRI数据，包括增强T1加权序列和脂肪抑制T2加权序列。患者被分为训练队列（n = 102）和验证队列（n = 43）。采用K均值聚类根据信号强度将瘤内体素分为三种特征区域。从肿瘤相关区域提取了多个影像组学特征，以构建七个亚组的影像组学预测特征。逻辑回归分析确定瘤周水肿为独立危险因素。通过将最佳影像组学特征与瘤周水肿合并创建列线图。我们使用曲线下面积（AUC）、校准曲线和决策曲线分析评估了该模型的性能和临床价值。
基于瘤内特征区域和瘤周特征联合的多层感知器分类器模型给出了最佳影像组学特征，验证队列的AUC为0.856。验证队列中列线图的AUC为0.86首，优于影像组学特征和由瘤周水肿建立的临床模型。校准曲线和决策曲线分析显示该列线图具有良好的校准性和较高的临床应用价值。
基于MRI的列线图在预测STS患者术前分级方面准确有效。
