Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People's Republic of China.
Department of Plastic Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
J Magn Reson Imaging. 2023 Aug;58(2):534-545. doi: 10.1002/jmri.28518. Epub 2022 Nov 3.
Ki-67 expression has been shown to be an important risk factor associated with prognosis in patients with soft tissue sarcomas (STSs). Its assessment requires fine-needle biopsy and its accuracy can be influenced by tumor heterogeneity.
To develop and test an MRI-based radiomics nomogram for identifying the Ki-67 status of STSs.
Retrospective.
A total of 149 patients at two independent institutions (training cohort [high Ki-67/low ki-67]: 102 [52/50], external validation cohort [high Ki-67/low ki-67]: 47 [28/19]) with STSs.
FIELD STRENGTH/SEQUENCE: Fat-saturated T2-weighted imaging (FS-T2WI) with a fat-suppressed fast spin/turbo spin echo sequence at 1.5 T or 3 T.
After radiomics feature extraction, logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) were used to construct radiomics models to distinguish between high and low Ki-67 status. Clinical-MRI characteristics included age, gender, location, size, margin, and MRI morphological features (size, margin, signal intensity, and peritumoral hyperintensity) were assessed. Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by radiomics signature and risk factors.
Model performances (discrimination, calibration, and clinical usefulness) were validated in the validation cohort. The nomogram was assessed using the Harrell index of concordance (C-index), calibration curve analysis. The clinical utility of the model was assessed by decision curve analysis (DCA).
LR, RF, SVM, and KNN models represented AUCs of 0.789, 0.755, 0.726, and 0.701 in the validation cohort (P > 0.05). The nomogram had a C-index of 0.895 (95% CI: 0.837-0.953) in the training cohort and 0.852 (95% CI: 0.796-0.957) in the validation cohort and it demonstrated good calibration and clinical utility (P = 0.972 for the training cohort and P = 0.727 for the validation cohort).
This MRI-based radiomics nomogram developed showed good performance in identifying Ki-67 expression status in STSs.
Ki-67 表达已被证明是软组织肉瘤（STS）患者预后的重要危险因素。其评估需要进行细针活检，其准确性可能受到肿瘤异质性的影响。
开发和测试一种基于 MRI 的放射组学列线图，以识别 STS 的 Ki-67 状态。
回顾性。
来自两个独立机构的共 149 名 STS 患者（培训队列[高 Ki-67/低 Ki-67]：102[52/50]，外部验证队列[高 Ki-67/低 Ki-67]：47[28/19]）。
磁场强度/序列：1.5T 或 3T 上的脂肪饱和 T2 加权成像（FS-T2WI），带有脂肪抑制快速自旋/涡轮自旋回波序列。
在提取放射组学特征后，使用逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）和 k-最近邻（KNN）构建放射组学模型，以区分高 Ki-67 和低 Ki-67 状态。临床 MRI 特征包括年龄、性别、位置、大小、边界和 MRI 形态特征（大小、边界、信号强度和瘤周高信号）。应用单变量和多变量逻辑回归分析筛选显著的危险因素。通过放射组学特征和危险因素构建放射组学列线图。
在验证队列中验证模型性能（鉴别力、校准和临床实用性）。通过一致性指数（C 指数）、校准曲线分析评估列线图。通过决策曲线分析（DCA）评估模型的临床实用性。
LR、RF、SVM 和 KNN 模型在验证队列中的 AUC 分别为 0.789、0.755、0.726 和 0.701（P>0.05）。列线图在训练队列中的 C 指数为 0.895（95%CI：0.837-0.953），在验证队列中的 C 指数为 0.852（95%CI：0.796-0.957），且具有良好的校准和临床实用性（训练队列中 P=0.972，验证队列中 P=0.727）。
本研究开发的基于 MRI 的放射组学列线图在识别 STS 中的 Ki-67 表达状态方面表现出良好的性能。
2 期。
