A comparison of four non-Gaussian diffusion models combined with whole-tumor histogram analysis for evaluating cervical cancer

Motivation: Non-Gaussian diffusion models can effectively characterize the microstructure of tissues. Goal(s): To investigate the potential predictive value of multiple non-Gaussian diffusion models for assessing cervical cancer (CC). Approach: Diffusion parameters derived from continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC) and intravoxel incoherent motion (IVIM) were calculated. The most significant histogram features selected by univariate analysis and multivariate logistic regression were used to establish predictive models. The predictive performance was evaluated by receiver operating characteristic (ROC) analyses. Results: The combination of multiple non-Gaussian diffusion models and whole-tumor histogram analysis could distinguish pathological types and differentiation degree in CC. Impact: Predicting pathological types and differentiation degree of cervical cancer is crucial for appropriate treatment and prognosis. The use of multiple non-Gaussian diffusion models combined with whole-tumor histogram analysis offers a precise and non-invasive solution to this clinical issue.