Cai Wemin, Guo Kun, Chen Yongxian, Shi Yubo, Chen Junkai
Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China; Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Acad Radiol. 2024 Dec;31(12):4974-4984. doi: 10.1016/j.acra.2024.06.036. Epub 2024 Jul 20.
The objective was to assess and examine radiomics models derived from contrast-enhanced CT for their predictive capacity using the sub-regional radiomics regarding the Ki-67 proliferation index (PI) in patients with pathologically confirmed gastrointestinal stromal tumors (GIST).
In this retrospective study, a total of 412 GIST patients across three institutions (223 from center 1, 106 from center 2, and 83 from center 3) was enrolled. Radiomic features were derived from various sub-regions of the tumor region of interest employing the K-means approach. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify features correlated with Ki-67 PI level in GIST patients. A support vector machine (SVM) model was then constructed to predict the high level of Ki-67 (Ki-67 index >8%), drawing on the radiomics features from each sub-region within the training cohort.
After features selection process, 6, 9, 9, 7 features were obtained to construct SVM models based on sub-region 1, 2, 3 and the entire tumor, respectively. Among different models, the model developed by the sub-region 1 achieved an area under the receiver operating characteristic curve (AUC) of 0.880 (95% confidence interval [CI]: 0.830 to 0.919), 0.852 (95% CI: 0.770-0.914), 0.799 (95% CI: 0.697-0.879) in the training, external test set 1, and 2, respectively.
The results of the present study suggested that SVM model based on the sub-regional radiomics features had the potential of predicting Ki-67 PI level in patients with GIST.
目的是评估和检验从增强CT中得出的放射组学模型，利用亚区域放射组学对病理确诊的胃肠道间质瘤（GIST）患者的Ki-67增殖指数（PI）进行预测。
在这项回顾性研究中，纳入了来自三个机构的412例GIST患者（中心1有223例，中心2有106例，中心3有83例）。采用K均值法从感兴趣肿瘤区域的各个亚区域提取放射组学特征。使用最小绝对收缩和选择算子（LASSO）回归来识别与GIST患者Ki-67 PI水平相关的特征。然后构建支持向量机（SVM）模型，根据训练队列中每个亚区域的放射组学特征预测Ki-67高水平（Ki-67指数>8%）。
经过特征选择过程后，分别基于亚区域1、2、3和整个肿瘤获得了6、9、9、7个特征来构建SVM模型。在不同模型中，由亚区域1开发的模型在训练集、外部测试集1和2中的受试者操作特征曲线下面积（AUC）分别为0.880（95%置信区间[CI]：0.830至0.919）、0.852（95% CI：0.770 - 0.914）、0.799（95% CI：0.697 - 0.879）。
本研究结果表明，基于亚区域放射组学特征的SVM模型具有预测GIST患者Ki-67 PI水平的潜力。
