Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, China.
Hell J Nucl Med. 2024 May-Aug;27(2):141-148. doi: 10.1967/s002449912732.
To analyze the diagnostic value of computed tomography (CT) radiomics models in differentiating gastrointestinal stromal tumors (GIST) and other mesenchymal tumors.
A retrospective analysis of clinical data from 153 patients with pathologically confirmed gastrointestinal mesenchymal tumors treated in our hospital from July 2019 to March 2024 was conducted, including 107 cases of GIST, 18 cases of leiomyoma, and 28 cases of schwannoma. LASSO regression was used for feature selection. Logistic regression and Random Forest (RF) models were established based on selected features using machine learning algorithms, with the dataset divided into training (107 cases) and validation sets (46 cases) at a 7:3 ratio. The diagnostic performance of the models was evaluated using receiver operating characteristic (ROC) curves.
In the training set, there were significant differences between GIST and non-GIST in terms of enhancement degree, age, maximum diameter, and tumor location distribution (P<0.05). A total of 180 radiomics features were extracted using A.K software. LASSO regression reduced the high-dimensional data to 13 radiomics features. Logistic regression and RF models were established based on these 13 features. The AUC for the Logistic regression model was 0.753 in the training set and 0.582 in the validation set, while the AUC for the RF model was 0.941 in the training set and 0.746 in the validation set. The RF model showed higher diagnostic performance than the Logistic regression model (P<0.05). Decision curve analysis showed that the net benefit of the RF model in differentiating GIST was superior to classifying all patients as either GIST or non-GIST and also superior to the Logistic regression model within a probability threshold range of 20%-90%.
The machine learning models based on radiomics features have good diagnostic value in predicting the pathological classification of GIST and other mesenchymal tumors, with the RF model showing superior diagnostic value compared to the Logistic regression model.
分析计算机断层扫描（CT）放射组学模型在鉴别胃肠道间质瘤（GIST）和其他间质肿瘤中的诊断价值。
回顾性分析了 2019 年 7 月至 2024 年 3 月我院收治的 153 例经病理证实的胃肠道间质肿瘤患者的临床资料，包括 GIST 107 例、平滑肌瘤 18 例、神经鞘瘤 28 例。采用 LASSO 回归进行特征选择。基于所选特征，使用机器学习算法建立逻辑回归和随机森林（RF）模型，数据集以 7:3 的比例分为训练集（107 例）和验证集（46 例）。使用受试者工作特征（ROC）曲线评估模型的诊断性能。
在训练集中，GIST 与非 GIST 之间在增强程度、年龄、最大直径和肿瘤位置分布方面存在显著差异（P<0.05）。使用 A.K 软件共提取 180 个放射组学特征。LASSO 回归将高维数据简化为 13 个放射组学特征。基于这 13 个特征建立了逻辑回归和 RF 模型。逻辑回归模型在训练集的 AUC 为 0.753，在验证集的 AUC 为 0.582；RF 模型在训练集的 AUC 为 0.941，在验证集的 AUC 为 0.746。RF 模型的诊断性能优于逻辑回归模型（P<0.05）。决策曲线分析显示，RF 模型在鉴别 GIST 方面的净获益优于将所有患者分为 GIST 或非 GIST 两类，也优于逻辑回归模型在 20%-90%概率阈值范围内的获益。
基于放射组学特征的机器学习模型对预测 GIST 和其他间质肿瘤的病理分类具有良好的诊断价值，RF 模型的诊断价值优于逻辑回归模型。
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