Shao Xian, An Li, Liu Hui, Feng Hui, Zheng Liyun, Dai Yongming, Yu Bin, Zhang Jin
Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China.
Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Front Oncol. 2022 Apr 5;12:851677. doi: 10.3389/fonc.2022.851677. eCollection 2022.
The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma.
Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann-Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. -value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons.
Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes ( = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, , and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones ( = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829).
The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.
本研究旨在探讨使用分数阶微积分(FROC)模型反映宫颈癌肿瘤亚型和组织学分级的可行性。
60例未经治疗的宫颈癌患者在3.0T磁共振成像(MRI)上进行了多b值扩散加权成像(DWI)。拟合了单指数模型和FROC模型。采用曼-惠特尼U检验评估组织学亚型和分级的差异。进行受试者操作特征(ROC)分析以评估诊断性能,并确定单变量分析和多变量分析的最佳预测指标。使用Hanley和McNeil检验测试ROC曲线之间的差异,同时使用McNemar检验比较敏感性、特异性和准确性。P值<0.05被认为有显著差异。应用Bonferroni校正以减少与多重比较相关的问题。
只有FROC模型得出的参数β能够区分宫颈癌亚型(P = 0.03),且鳞状细胞癌(SCC)病变的β值显著低于腺癌(ACA)病变。FROC模型得出的所有个体参数,即表观扩散系数(ADC)、β、γ和μ,均能区分低级别宫颈癌和高级别宫颈癌(P分别为0.022、0.009、0.004和0.015)。所有FROC参数的组合显示出最佳的总体性能,提供了最高的敏感性(81.2%)和曲线下面积(AUC,0.829)。
FROC模型得出的参数能够区分宫颈癌的亚型和分级。