Department of Radiology, Ministry of Health Recep Tayyip Erdoğan University Training and Research Hospital, Rize, Turkey.
Department of Radiology, Ministry of Health Ankara Training and Research Hospital, Ankara, Turkey.
Jpn J Radiol. 2024 Aug;42(8):872-879. doi: 10.1007/s11604-024-01558-8. Epub 2024 Mar 27.
To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model.
One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Six radiomic features were selected with LASSO. In the clinical model, six features (age, gender, thickness, fat stranding, symmetry, and lymph node) were included. Six radiomic and six clinical features were used in the combined model. Classification was done using two machine learning algorithms: Support Vector Machine (SVM) and Logistic Regression (LR). The data sets were divided into 80% training set and 20% test set. Then, training took place with all three datasets. The model's success was tested with the test set consisting of features not used during training.
In the training set, the combined model had the best performance with the area under the curve (AUC) value of 0.99 for SVM and 0.95 for LR. In the radiomic-derived model, the AUC value is 0.87 in SVM and 0.79 in LR. In the clinical model, SVM made this distinction with 0.95 AUC and LR with 0.92 AUC value. In the test set, the classifier with the highest success distinguishing malignant wall thickening is SVM in the radiomic-derived model with an AUC value of 0.90. In other models, the AUC value is in the range of 0.75-0.86, and the accuracy values are in the range of 0.72-0.84.
In conclusion, radiomic-based machine learning has shown high success in distinguishing malignant and benign BWT and may improve diagnostic accuracy compared to clinical features only. The results of our study may help ensure early diagnosis and treatment of colorectal cancers by facilitating the recognition of malignant BWT.
利用基于机器学习（ML）模型的计算机断层扫描（CT）纹理特征来区分恶性和良性肠壁增厚（BWT），并比较其与临床模型和联合模型的成功程度。
本回顾性研究纳入了 122 例经增强腹部 CT 检查并接受结肠镜检查的 BWT 患者。使用 LifeX 软件从 CT 图像中提取纹理特征。使用最小绝对收缩和选择算子（LASSO）进行特征选择和降维。LASSO 选择了 6 个放射组学特征。在临床模型中，包括 6 个特征（年龄、性别、厚度、脂肪条纹、对称性和淋巴结）。联合模型中使用了 6 个放射组学和 6 个临床特征。使用两种机器学习算法：支持向量机（SVM）和逻辑回归（LR）进行分类。数据集分为 80%的训练集和 20%的测试集。然后，用所有三个数据集进行训练。使用未在训练中使用的特征的测试集测试模型的成功。
在训练集中，联合模型的表现最好，SVM 的曲线下面积（AUC）值为 0.99，LR 的 AUC 值为 0.95。在放射组学衍生模型中，SVM 的 AUC 值为 0.87，LR 的 AUC 值为 0.79。在临床模型中，SVM 的 AUC 值为 0.95，LR 的 AUC 值为 0.92。在测试集中，区分恶性壁增厚成功率最高的分类器是放射组学衍生模型中的 SVM，其 AUC 值为 0.90。在其他模型中，AUC 值在 0.75-0.86 之间，准确率在 0.72-0.84 之间。
总之，基于放射组学的机器学习在区分恶性和良性 BWT 方面取得了很高的成功率，并且可能比仅基于临床特征的方法提高诊断准确性。我们的研究结果可能有助于通过识别恶性 BWT 来促进早期诊断和治疗结直肠癌。
