DataSheet_1_CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas.pdf

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).
MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.
ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.
ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

研究目的：探讨基于CT的放射组学分析（CT-based radiomics analysis）在术前鉴别胰腺黏液性囊性肿瘤（pancreatic mucinous cystic neoplasms, MCN）与非典型浆液性囊腺瘤（atypical serous cystadenomas, ASCN）中的应用价值。
研究方法：本研究回顾性纳入103例经手术治疗的MCN患者与113例ASCN患者。从术前CT图像中提取共764个放射组学特征，通过曼-惠特尼U检验及最小冗余最大相关性算法筛选最优特征，随后采用随机森林算法构建放射组学评分（Rad-score）。同时收集所有患者的放射学/临床特征，采用多变量logistic回归构建放射学模型。通过十折交叉验证评估Rad-score与放射学模型的性能，评估指标包括受试者工作特征曲线下面积（AUC）、灵敏度、特异度、阳性预测值（PPV）、阴性预测值（NPV）及准确率。
研究结果：共筛选得到10个最优特征，并以此构建Rad-score；放射学模型则基于4项放射学/临床特征构建。十折交叉验证结果显示，Rad-score具有良好的稳健性与可靠性（平均AUC：0.784，灵敏度：0.847，特异度：0.745，PPV：0.767，NPV：0.849，准确率：0.793）。放射学模型的分类性能略逊一筹（平均AUC：0.734，灵敏度：0.748，特异度：0.705，PPV：0.732，NPV：0.798，准确率：0.728）。
研究结论：基于CT的放射组学分析在术前鉴别MCN与ASCN方面表现出优异的诊断性能，可有效提升诊断效能，有望成为指导此类患者临床决策的新型辅助工具。