旨在评估多个非高斯扩散模型的全肿瘤直方图分析在区分宫颈癌 (CC) 侵袭状态的病理类型、分化程度、分期和 p16 表达方面的诊断潜力。患者被纳入这项前瞻性单中心研究该研究于 2022 年 3 月至 2023 年 7 月期间进行。获得了包括 15 个 b 值 (0 ~ 4000 s/mm2) 的扩散加权图像 (DWI)。计算了连续时间随机游走（CTRW）、扩散峰度成像（DKI）、分数阶微积分（FROC）和体素内不相干运动（IVIM）四种非高斯扩散模型的扩散参数及其直方图特征进行了分析。为了选择最显着的特征并建立预测模型，进行了单变量分析和多变量逻辑回归。最后，我们通过接受者操作特征 (ROC) 分析评估了我们模型的诊断性能。89 名患有 CC 的女性（平均年龄，55±11 岁）参加了我们的研究。结合了 CTRW、DKI、FROC 和 IVIM 扩散模型的组合模型在区分宫颈癌方面提供了显着高于任何单个模型的 AUC（分别为 0.836 vs. 0.664、0.642、0.651、0.649；p<0.05）。宫颈腺癌的鳞状细胞癌。在区分肿瘤分化程度方面，除了组合模型比DKI模型表现出更好的预测性能（AUC分别为0.839 vs. 0.697；p< 0.05）外，其他单个模型和组合模型之间的AUC没有发现显着差异。为了预测国际妇产科联合会（FIGO）分期，仅建立了DKI和FROC模型，不同模型之间的预测性能没有显着差异。在预测p16表达方面，DKI模型的预测能力显着低于FROC和组合模型（AUC分别为0.693 vs. 0.850、0.859；p< 0.05）。全肿瘤的多个非高斯扩散模型直方图分析显示出评估 CC 攻击性状态的巨大前景。© 2024。作者获得 Springer Science Business Media, LLC（Springer Nature 旗下公司）的独家许可。

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.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.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).Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.