Huang Wen-Peng, Liu Si-Yun, Han Yi-Jing, Li Li-Ming, Liang Pan, Gao Jian-Bo
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Pharmaceutical Diagnostics, General Electric Company (GE) Healthcare, Beijing, China.
Front Oncol. 2021 May 17;11:677814. doi: 10.3389/fonc.2021.677814. eCollection 2021.
It is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN.
Eighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong's test were mainly used for model assessment and AUC comparison.
It retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong's test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs.
The arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively.
传统CT征象预测胰腺实性假乳头状瘤（pSPN）的侵袭性具有挑战性。我们旨在开发并评估基于CT的放射组学特征，以术前预测pSPN的侵袭行为。
回顾性分析我院85例经病理证实为pSPN且术前行增强CT成像的患者（侵袭性：24例；非侵袭性：61例）。在动脉期和静脉期从勾画的二维或三维感兴趣区（ROI）中分别提取1316个放射组学特征。使用200%合成少数过采样技术（SMOTE）为每个阶段生成平衡数据集（侵袭性：72例，非侵袭性：96例），用于特征选择和建模。该模型在原始数据集中进行内部验证。主要应用观察者间一致性分析、斯皮尔曼相关性分析、单因素分析、套索回归和向后逐步逻辑回归来筛选特征，并基于二维或三维分割的多期特征建立6个逻辑回归模型。主要使用ROC分析和德龙检验进行模型评估和AUC比较。
构建三维动脉期、三维静脉期、二维动脉期和二维静脉期模型分别保留了11个、8个、7个和7个特征。基于三维ROI，动脉期模型（AUC：0.914）比静脉期模型（AUC：0.815）表现更好，动脉期-静脉期联合模型略有改善（AUC：0.918）。基于二维ROI，动脉期模型（AUC：0.814）比静脉期模型（AUC：0.768）表现更好，但动脉期-静脉期联合模型（AUC：0.893）比任何单期模型表现更好。此外，三维动脉期模型比最佳的二维联合模型表现更好。德龙检验表明，比较二维和三维ROI时，原始数据集中动脉期模型的AUC存在显著差异（p = 0.019），而动脉期-静脉期联合模型不存在显著差异（p = 0.49）。
由三维ROI特征构建的动脉期放射组学模型具有术前预测pSPN侵袭性的潜力。
