宫颈癌（CC）患者的预后与 Ki-67 增殖指数（PI）密切相关。然而，通过活检获得的Ki-67 PI具有一定的局限性。磁共振成像（MRI）的非高斯分布扩散模型可能在表征组织异质性方面发挥重要作用。目前，关于使用基于非高斯扩散分布的直方图特征的模型预测 Ki-67 PI 的可用数据有限。本研究旨在确定多个非高斯弥散加权成像模型的术前直方图特征是否可以预测 CC 患者的 Ki-67 PI。我们的横断面前瞻性研究共招募了 53 名疑似患有 CC 的患者，这些患者接受了2022年1月至2023年1月在中山大学孙逸仙纪念医院进行的3.0-T MRI。使用15个b值（0-4,000 s/mm2）进行弥散加权成像。总共使用了来自四种非高斯扩散加权成像模型的九个参数，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）和体素内不相干运动（IVIM） 。然后获得这些参数的全肿瘤体积直方图分析。在逻辑回归中，对两组的显着直方图特征进行统计检查，以构建最终的预测模型。为了评估所提出的模型在 Ki-67 PI 诊断中的诊断参数，以及四个模型中这些不同参数的敏感性、特异性和诊断准确性，应用了接受者操作特征分析。在 53 名患者中（55.3 ±9.6 岁，范围从 23 岁到 79 岁），其中 15 人的 Ki-67 PI ≤ 50%，38 人的 Ki-67 PI > 50%。单变量分析确定两组之间的 12 个直方图特征存在统计学差异。在多变量逻辑回归中，我们最终选择了6个直方图特征来构建最终的预测模型，其中CTRW_α_第10个百分位[优势比（OR）=0.955； 95%置信区间（CI）：0.92-0.99； P=0.019]、CTRW_α_稳健平均绝对偏差（OR =0.893；95% CI：0.81-0.99；P=0.028）和 CTRW_α_均匀性（OR =0.000，95% CI：0.00-0.90，P=0.047）为独立预测变量。组合预测模型的曲线下面积为0.845（95% CI：0.74-0.95），敏感性为78.9%（95% CI：0.63-0.90），特异性为86.7%（95% CI：0.60- 0.98），准确度为 81.1%（95% CI：0.68-0.91），阳性预测值为 93.8%（95% CI：0.79-0.99），阴性预测值为 61.9%（95% CI：0.38- 0.82)。多重非高斯扩散加权成像的直方图特征有助于预测CC的Ki-67 PI，为CC.2024 AME出版公司的关键生物学特征的无创评估提供了新方法。版权所有。

The prognosis for patients with cervical cancer (CC) is strongly correlated with the Ki-67 proliferation index (PI). However, the Ki-67 PI obtained through biopsy has certain limitations. The non-Gaussian distribution diffusion model of magnetic resonance imaging (MRI) may play an important role in characterizing tissue heterogeneity. At present, there are limited data available concerning the prediction of Ki-67 PI using models based on histogram features of non-Gaussian diffusion distribution. This study aimed to determine whether preoperative histogram features from multiple non-Gaussian models of diffusion-weighted imaging can predict the Ki-67 PI in patients with CC.Our cross-sectional prospective study recruited a total of 53 patients suspected of having CC who underwent 3.0-T MRI at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between January 2022 and January 2023. Fifteen b values (0-4,000 s/mm2) were used for diffusion-weighted imaging. A total of nine parameters from four non-Gaussian diffusion-weighted imaging models, including continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM), were used. Whole-tumor volumetric histogram analysis of these parameters was then obtained. In logistic regression, significant histogram characteristics were statistically examined across two groups to build the final prediction model. To assess diagnostic parameters of the proposed model in the diagnosis of the Ki-67 PI, along with the sensitivity, specificity, and diagnostic accuracy of these various parameters from the four models, receiver operating feature analysis was applied.Among the 53 patients (55.3±9.6 years, ranging from 23 to 79 years) included in the study, 15 had a Ki-67 PI ≤50% and 38 had a Ki-67 PI >50%. Univariable analysis determined that 12 histogram features were statistically different between the two groups. In multivariable logistic regression, we ultimately selected 6 histogram features to construct the final prediction model, with CTRW_α_10th percentile [odds ratio (OR) =0.955; 95% confidence interval (CI): 0.92-0.99; P=0.019], CTRW_α_robust mean absolute deviation (OR =0.893; 95% CI: 0.81-0.99; P=0.028), and CTRW_α_uniformity (OR =0.000, 95% CI: 0.00-0.90, P=0.047) being the independent predictive variables. The area under the curve of the combined prediction model was 0.845 (95% CI: 0.74-0.95), with a sensitivity of 78.9% (95% CI: 0.63-0.90), a specificity of 86.7% (95% CI: 0.60-0.98), an accuracy of 81.1% (95% CI: 0.68-0.91), a positive predictive value of 93.8% (95% CI: 0.79-0.99), and a negative predictive value of 61.9% (95% CI: 0.38-0.82).The histogram features of multiple non-Gaussian diffusion-weighted imaging can help to predict the Ki-67 PI of CC, providing a new method for the noninvasive evaluation of critical biological features of CC.2024 AME Publishing Company. All rights reserved.