Abstract：
OBJECTIVE: To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression.
METHODS: Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses.
RESULTS: 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05).
CONCLUSIONS: Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.
METHODS: Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses.
RESULTS: 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05).
CONCLUSIONS: Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.
摘要：
目的：评估多种非高斯扩散模型的全肿瘤直方图分析对区分宫颈癌（CC）侵袭性病理类型的诊断潜力，分化程度，舞台,和p16表达。
方法：患者于2022年3月至2023年7月参加了这项前瞻性单中心研究。获得包括15个b值（0〜4000s/mm2）的扩散加权图像（DWI）。扩散参数来自四个非高斯扩散模型，包括连续时间随机游走(CTRW)，扩散峰度成像（DKI），分数阶微积分(FROC)，并计算了体素内不相干运动(IVIM)，并对其直方图特征进行了分析。选择最显著的特征并建立预测模型，进行单因素分析和多因素logistic回归.最后,我们使用受试者工作特征(ROC)分析评估了我们模型的诊断性能.
结果：89名女性(平均年龄，55±11年）的CC纳入我们的研究。组合模型，合并了CTRW，DKI,FROC,和IVIM扩散模型，提供了明显高于任何单个模型的AUC(0.836vs.分别为0.664、0.642、0.651、0.649；p<0.05）在区分宫颈鳞状细胞癌和宫颈腺癌中。区分肿瘤分化程度，除了组合模型与DKI模型相比显示出更好的预测性能(AUC，0.839vs.分别为0.697；p<0.05），在其他个体模型和组合模型中,AUC无显著差异.预测国际妇产科联合会（FIGO）阶段，仅建立DKI和FROC模型,不同模型间预测性能无显著差异。在预测p16表达方面，DKI模型的预测能力明显低于FROC和组合模型(AUC，0.693vs.分别为0.850、0.859；p<0.05）。
结论：具有全肿瘤直方图分析的多个非高斯扩散模型显示出评估CC侵袭性状态的巨大前景。
方法：患者于2022年3月至2023年7月参加了这项前瞻性单中心研究。获得包括15个b值（0〜4000s/mm2）的扩散加权图像（DWI）。扩散参数来自四个非高斯扩散模型，包括连续时间随机游走(CTRW)，扩散峰度成像（DKI），分数阶微积分(FROC)，并计算了体素内不相干运动(IVIM)，并对其直方图特征进行了分析。选择最显著的特征并建立预测模型，进行单因素分析和多因素logistic回归.最后,我们使用受试者工作特征(ROC)分析评估了我们模型的诊断性能.
结果：89名女性(平均年龄，55±11年）的CC纳入我们的研究。组合模型，合并了CTRW，DKI,FROC,和IVIM扩散模型，提供了明显高于任何单个模型的AUC(0.836vs.分别为0.664、0.642、0.651、0.649；p<0.05）在区分宫颈鳞状细胞癌和宫颈腺癌中。区分肿瘤分化程度，除了组合模型与DKI模型相比显示出更好的预测性能(AUC，0.839vs.分别为0.697；p<0.05），在其他个体模型和组合模型中,AUC无显著差异.预测国际妇产科联合会（FIGO）阶段，仅建立DKI和FROC模型,不同模型间预测性能无显著差异。在预测p16表达方面，DKI模型的预测能力明显低于FROC和组合模型(AUC，0.693vs.分别为0.850、0.859；p<0.05）。
结论：具有全肿瘤直方图分析的多个非高斯扩散模型显示出评估CC侵袭性状态的巨大前景。
