Zheng Yuemei, Wang Hong, Li Qiong, Sun Haoran, Guo Li
School of Medical Imaging, Tianjin Medical University, No. 1 Guangdong Road, Tianjin 300203, China.
Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
Acad Radiol. 2023 May;30(5):814-822. doi: 10.1016/j.acra.2022.06.007. Epub 2022 Jul 7.
To develop and validate a combined model integrating clinical and radiomic features to non-invasive discriminate between the benign and malignant solid ovarian tumors.
A total of 148 patients with 156 solid ovarian tumors (86 benign and 70 malignant tumors) were included in this study. The dataset was split into the training and the test set with a ratio of 8:2 using stratified random sampling. 12 clinical features and 1612 radiomic features were extracted from each tumor. These features were selected by least absolute shrinkage and selection operator (Lasso). Three classification models were built using extreme gradient boosting (XGB) algorithm: clinical model, radiomic model, combined model. The area under the receiver operating characteristic curve (AUC), accuracy, precision and sensitivity were analyzed to evaluate the performance of these models.
All of the three models obtained good performances in differentiating benign with malignant solid ovarian tumors in both training and test sets. The AUC, accuracy, precision, sensitivity of clinical model and radiomic model in test set were 0.847 (95% confidence interval (CI), 0.707-0.986, p <0.01), 0.774, 0.769, 0.714, and 0.807 (95%CI, 0.652-0.961, p <0.05), 0.677, 0.643, 0.643, respectively. Combined model had the best prediction results, the AUC, accuracy, precision and sensitivity were 0.954 (95%CI, 0.862-1.0, p <0.01), 0.839, 0.909 and 0.714 in test set.
Radiomics based on machine learning can be helpful for radiologists in differentiating the benign and malignant solid ovarian tumors.
开发并验证一种整合临床和影像组学特征的联合模型，用于无创鉴别卵巢实性肿瘤的良恶性。
本研究共纳入148例患有156个卵巢实性肿瘤的患者（86个良性肿瘤和70个恶性肿瘤）。使用分层随机抽样将数据集按8:2的比例分为训练集和测试集。从每个肿瘤中提取12个临床特征和1612个影像组学特征。这些特征通过最小绝对收缩和选择算子（Lasso）进行选择。使用极端梯度提升（XGB）算法构建了三个分类模型：临床模型、影像组学模型、联合模型。分析受试者工作特征曲线（AUC）下面积、准确性、精确性和敏感性，以评估这些模型的性能。
在训练集和测试集中，这三个模型在鉴别卵巢实性肿瘤的良恶性方面均表现良好。测试集中临床模型和影像组学模型的AUC、准确性、精确性、敏感性分别为0.847（95%置信区间（CI），0.707 - 0.986，p <0.01）、0.774、0.769、0.714，以及0.807（95%CI，0.652 - 0.961，p <0.05）、0.677、0.643、0.643。联合模型具有最佳预测结果，测试集中的AUC、准确性、精确性和敏感性分别为0.954（95%CI，0.862 - 1.0，p <0.01）、0.839、0.909和0.714。
基于机器学习的影像组学有助于放射科医生鉴别卵巢实性肿瘤的良恶性。
