Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China.
The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China.
Abdom Radiol (NY). 2020 May;45(5):1524-1533. doi: 10.1007/s00261-020-02506-6.
To investigate the value of texture analysis on unenhanced computed tomography (CT) to potentially differentiate mass-forming pancreatitis (MFP) from pancreatic ductal adenocarcinoma (PDAC).
A retrospective study consisting of 109 patients (30 MFP patients vs 79 PDAC patients) who underwent preoperative unenhanced CT between January 2012 and December 2017 was performed. Synthetic minority oversampling technique (SMOTE) algorithm was adopted to reconstruct and balance MFP and PDAC samples. A total of 396 radiomic features were extracted from unenhanced CT images. Mann-Whitney U test and minimum redundancy maximum relevance (MRMR) methods were used for the purpose of dimension reduction. Predictive models were constructed using random forest (RF) method, and were validated using leave group out cross-validation (LGOCV) method. Diagnostic performance of the predictive model, including sensitivity, specificity, accuracy, positive predicting value (PPV), and negative predicting value (NPV), was recorded.
We applied 200% of SMOTE to MFP and PDAC patients, resulting in 90 MFP patients compared with 120 PDAC patients. Dimension reduction steps yielded 30 radiomic features using Mann-Whitney U test and MRMR methods. Ten radiomic features were retained using RF method. Four most predictive parameters, including GreyLevelNonuniformity_angle90_offset1, VoxelValueSum, HaraVariance, and ClusterProminence_AllDirection_offset1_SD, were used to generate the predictive model with preferable 92.2% sensitivity, 94.2% specificity, 93.3% accuracy, 92.2% PPV, and 94.2% NPV. Finally, in LGOCV analysis, a high pooled mean sensitivity, specificity, and accuracy (82.6%, 80.8%, and 82.1%, respectively) indicate a relatively reliable and stable predictive model.
Unenhanced CT texture analysis can be a promising noninvasive method in discriminating MFP from PDAC.
探究在未增强 CT 上进行纹理分析以鉴别肿块型胰腺炎（MFP）与胰腺导管腺癌（PDAC）的价值。
回顾性分析 2012 年 1 月至 2017 年 12 月期间行术前未增强 CT 的 109 例患者（30 例 MFP 患者和 79 例 PDAC 患者）。采用合成少数过采样技术（SMOTE）算法对 MFP 和 PDAC 样本进行重建和平衡。从未增强 CT 图像中提取了 396 个放射组学特征。采用 Mann-Whitney U 检验和最小冗余最大相关性（MRMR）方法进行降维。采用随机森林（RF）方法构建预测模型，并采用留组外交叉验证（LGOCV）方法进行验证。记录预测模型的诊断性能，包括敏感性、特异性、准确性、阳性预测值（PPV）和阴性预测值（NPV）。
对 MFP 和 PDAC 患者应用 200%SMOTE，得到 90 例 MFP 患者和 120 例 PDAC 患者。Mann-Whitney U 检验和 MRMR 方法的降维步骤得到 30 个放射组学特征。RF 方法保留了 10 个放射组学特征。使用 GreyLevelNonuniformity_angle90_offset1、VoxelValueSum、HaraVariance 和 ClusterProminence_AllDirection_offset1_SD 这四个最具预测性的参数生成预测模型，该模型具有较好的敏感性（92.2%）、特异性（94.2%）、准确性（93.3%）、PPV（92.2%）和 NPV（94.2%）。最后，在 LGOCV 分析中，较高的平均 pooled 敏感性、特异性和准确性（分别为 82.6%、80.8%和 82.1%）表明该预测模型具有相对可靠和稳定的性能。
未增强 CT 纹理分析有望成为鉴别 MFP 与 PDAC 的一种有前途的无创方法。
