Department of Gynecology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China.
Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China.
Biomed Res Int. 2022 Feb 17;2022:5952296. doi: 10.1155/2022/5952296. eCollection 2022.
Accurate identification of ovarian tumors as benign or malignant is highly crucial. Radiomics is a new branch of imaging that has emerged in recent years to replace the traditional naked eye qualitative diagnosis.
This study is aimed at exploring the difference in the application potential of two- (2D) and three-dimensional (3D) radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors.
A retrospective analysis was performed on 140 patients with ovarian tumors confirmed by surgery and pathology in our hospital from July 2017 to August 2020. These 140 patients were divided into benign group and malignant group according to the pathological results. The ITK-SNAP software was used to outline the regions-of-interest (ROI) of 2D or 3D tumors on the CT plain scan image of each patient; the texture features were extracted through analysis kit (AK), and the cases were randomly divided into training groups ( = 99) and validation group ( = 41) in a ratio of 7 : 3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to perform dimensionality reduction, followed by the construction of the radiomics nomogram model using the logistic regression method. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve and decision curve analysis (DCA) were used to evaluate and verify the results of the radiomics nomogram and compare the differences between 2D and 3D diagnostic performance.
There were 396 quantitative radiomics feature parameters extracted from 2D group and the 3D group, respectively. The area under the curve (AUC) of the radiomics nomogram of the 2D training group and the validation group were 0.96 and 0.97, respectively. The accuracy, specificity, and sensitivity of the training set were 92.9%, 88.9%, and 96.3%, respectively, and those of the validation set were 90.2%, 82.6%, and 100.0%, respectively. The AUCs of the radiomics nomogram of the 3D training group and validation group were 0.96% and 0.99%, respectively. The accuracy, sensitivity, and specificity of the training set were 92.9%, 96.3%, and 88.9%, respectively, and those of the validation set were 97.6%, 95.7%, and 100.0%, respectively. DeLong's test indicated that there was no statistical significance between the two sets ( > 0.05).
For the differential diagnosis of benign and malignant ovarian tumors, the 2D and 3D radiomics nomogram models exhibited comparable diagnostic performance. Considering that the 2D model was cost-effective and time-efficient, it was more recommended to use 2D features in future research.
准确识别卵巢肿瘤的良恶性具有重要意义。放射组学是近年来新兴的影像学分支，用于取代传统的肉眼定性诊断。
本研究旨在探讨基于 CT 平扫的二维（2D）和三维（3D）放射组学模型在鉴别良恶性卵巢肿瘤中的应用潜力差异。
回顾性分析了 2017 年 7 月至 2020 年 8 月我院经手术和病理证实的 140 例卵巢肿瘤患者的资料。根据病理结果，将这 140 例患者分为良性组和恶性组。采用 ITK-SNAP 软件对每位患者 CT 平扫图像上的 2D 或 3D 肿瘤感兴趣区（ROI）进行勾画；通过分析工具包（AK）提取纹理特征，并将病例以 7∶3 的比例随机分为训练组（n=99）和验证组（n=41）。采用最小绝对收缩和选择算子（LASSO）算法进行降维，然后采用逻辑回归法构建放射组学列线图模型。绘制受试者工作特征（ROC）曲线，并使用校准曲线和决策曲线分析（DCA）评估和验证放射组学列线图的结果，并比较 2D 和 3D 诊断性能的差异。
从 2D 组和 3D 组分别提取了 396 个定量放射组学特征参数。2D 训练组和验证组的列线图曲线下面积（AUC）分别为 0.96 和 0.97。训练集的准确率、特异度和敏感度分别为 92.9%、88.9%和 96.3%，验证集的分别为 90.2%、82.6%和 100.0%。3D 训练组和验证组的列线图 AUC 分别为 0.96%和 0.99%。训练集的准确率、敏感度和特异度分别为 92.9%、96.3%和 88.9%，验证集的分别为 97.6%、95.7%和 100.0%。DeLong 检验表明两组之间无统计学差异（>0.05）。
对于良恶性卵巢肿瘤的鉴别诊断，2D 和 3D 放射组学列线图模型具有相似的诊断性能。考虑到 2D 模型具有成本效益和高效的特点，在未来的研究中更推荐使用 2D 特征。
