Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China.
Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China.
Magn Reson Imaging. 2022 Sep;91:91-99. doi: 10.1016/j.mri.2022.05.003. Epub 2022 May 4.
To develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors.
A total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA).
Six features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907-1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901-1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871-0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram.
Our study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.
开发并验证一种基于多参数磁共振成像的放射组学列线图，用于区分良恶性软组织肿瘤。
本研究纳入了 2017 年 1 月至 2020 年 10 月期间经病理证实的 91 例软组织肿瘤患者。2020 年 11 月至 2022 年 3 月期间连续纳入 48 例患者作为时间独立队列。所有患者均在 3.0T 磁共振扫描仪上进行了增强 T1 加权和 T2 加权脂肪抑制扫描。从两种模态中提取并选择放射组学特征，以开发放射组学特征。使用多变量逻辑回归分析确定显著的临床/形态学特征。应用最小绝对值收缩和选择算子回归确定有区别的特征。基于临床/形态学特征和放射组学特征构建临床放射组学列线图。最后，使用受试者工作特征和决策曲线分析（DCA）验证列线图的性能。
选择了 6 个特征来建立联合 RS。大小、边缘和肿瘤周围水肿被确定为最重要的临床和形态学因素。在 AUC 和敏感性方面，放射组学特征优于临床模型。整合联合 RS、大小、边缘和肿瘤周围水肿的列线图具有良好的预测效果，在训练（n=60）、验证（n=31）和时间独立（n=48）队列中的 AUC 分别为 0.954（95%置信区间[CI]：0.907-1.000，Sen=0.861，Spe=0.917）、0.962（95%CI：0.901-1.000，Sen=0.944，Spe=0.923）和 0.935（95%CI：0.871-0.998，Sen=0.815，Spe=0.952）。DCA 曲线表明该列线图具有良好的临床实用性。
本研究表明，基于多参数 MRI 的放射组学在区分良恶性软组织肿瘤方面具有临床应用潜力，可作为一种用于个体化治疗管理的非侵入性工具。
