A variety of tracer kinetic methods have been employed to assess tumor angiogenesis. The Standard two-Compartment model (SC) used in cervix carcinoma was less frequent, and Adiabatic Approximation to the Tissue Homogeneity (AATH) and Distributed Parameter (DP) model are lacking. This study compares two-compartment exchange models (2CXM) (AATH, SC, and DP) for determining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in cervical cancer, with the aim of investigating the potential of various parameters derived from 2CXM for tumor diagnosis and exploring the possible relationship between these parameters in patients with cervix cancer. Parameters (tissue blood flow, F p; tissue blood volume, V p; interstitial volume, V e; and vascular permeability, PS) for regions of interest (ROI) of cervix lesions and normal cervix tissue were estimated by AATH, SC, and DP models in 36 patients with cervix cancer and 17 healthy subjects. All parameters showed significant differences between lesions and normal tissue with a P value less than 0.05, except for PS from the AATH model, F p from the SC model, and V p from the DP model. Parameter V e from the AATH model had the largest AUC (r = 0.85). Parameters F p and V p from SC and DP models and V e and PS from AATH and DP models were highly correlated, respectively, (r > 0.8) in cervix lesions. Cervix cancer was found to have a very unusual microcirculation pattern, with over-growth of cancer cells but without evident development of angiogenesis. V e has the best performance in identifying cervix cancer. Most physiological parameters derived from AATH, SC, and DP models are linearly correlated in cervix cancer.

Purpose: To evaluate whether the analysis of high-temporal resolution DCE-MRI by various tracer kinetic models could yield useful radiomic features in discriminating cervix carcinoma and normal cervix tissue. Methods: Forty-three patients (median age 51 yr; range 26-78 yr) diagnosed with cervical cancer based on postoperative pathology were enrolled in this study with informed consent. DCE-MRI data with temporal resolution of 2 s were acquired and analyzed using the Tofts (TOFTS), extended Tofts (EXTOFTS), conventional two-compartment (CC), adiabatic tissue homogeneity (ATH), and distributed parameter (DP) models. Ability of all kinetic parameters in distinguishing tumor from normal tissue was assessed using Mann-Whitney U test and receiver operating characteristic (ROC) curves. Repeatability of parameter estimates due to sampling of arterial input functions (AIFs) was also studied using intraclass correlation (ICC) analysis. Results: Fractional extravascular, extracellular volume (Ve) of all models were significantly smaller in cervix carcinoma than normal cervix tissue, and were associated with large values of area under ROC curve (AUC 0.884-0.961). Capillary permeability PS derived from the ATH, CC, and DP models also yielded large AUC values (0.730, 0.860, and 0.797). Transfer constant Ktrans derived from TOFTS and EXTOFTS models yielded smaller AUC (0.587 and 0.701). Repeatability of parameters derived from all models was robust to AIF sampling, with ICC coefficients typically larger than 0.80. Conclusions: With the use of high-temporal resolution DCE-MRI, all tracer kinetic models could reflect pathophysiological differences between cervix carcinoma and normal tissue (with significant differences in Ve and PS) and potentially yield radiomic features with diagnostic value.

Background Most commonly used diffusion-weighted imaging (DWI) models include intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and mono-exponential model (MEM). Previous studies of the four models were inconsistent on which model was more effective in distinguishing cervical cancer from normal cervical tissue. Purpose To assess the performance of four DWI models in characterizing cervical cancer and normal cervical tissue. Material and Methods Forty-seven women with suspected cervical carcinoma underwent DWI using eight b-values before treatment. Imaging parameters, calculated using IVIM, SEM, DKI, and MEM, were compared between cervical cancer and normal cervical tissue. The diagnostic performance of the models was evaluated using independent t-test, Mann–Whitney U test, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression analysis. Results All parameters except pseudo-diffusion coefficient (D*) differed significantly between cervical cancer and normal cervical tissue ( P < 0.001). Through logistic regression analysis, all combined models showed a significant improvement in area under the ROC curve (AUC) compared to individual DWI parameters. The model with combined IVIM parameters had a larger AUC value compared to those of other combined models ( P < 0.05). Conclusion All four DWI models are useful for differentiating cervical cancer from normal cervical tissue and IVIM may be the optimal model.