Dynamic contrast-enhanced and diffusion-weighted MRI of cervical carcinoma: Correlations with Ki-67 proliferation status.

OBJECTIVES: To investigate the association of quantitative parameter (apparent diffusion coefficient [ADC]) from diffusion-weighted imaging (DWI) and various quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with Ki-67 proliferation index (PI) in cervical carcinoma (CC). METHODS: A total of 102 individuals with CC who received 3.0 T MRI examination (DWI and DCE MRI) between October 2016 and December 2022 were enrolled in our investigation. Two radiologists separately assessed the ADC parameter and various quantitative and semiquantitative parameters including (volume transfer constant [Ktrans], rate constant [kep], extravascular extracellular space volume fraction [ve], volume fraction of plasma [vp], time to peak [TTP], maximum concentration [MaxCon], maximal slope [MaxSlope] and area under curve [AUC]) for each tumor. Their association with Ki-67 PI was analyzed by Spearman association analysis. Ki-67 PI discrepancy between low-proliferation and high-proliferation groups was subsequently analyzed. The receiver operating characteristic (ROC) curve analysis utilized to identify optimal cut-off points for significant parameters. RESULTS: Both ADC (ρ = -0.457, p < 0.001) and Ktrans (ρ = -0.467, p < 0.001) indicated a strong negative association with Ki-67 PI. Ki-67 PI showed positive correlations with TTP, MaxCon, MaxSlope and AUC (ρ = 0.202, 0.231, 0.309, 0.235, respectively; all p values<0.05). Compared with the low-proliferation group, high-Ki-67 group presented a significantly lower ADC (0.869 ±â€¯0.125 × 10-3 mm2/s vs. 1.149 ±â€¯0.318 × 10-3 mm2/s; p < 0.001) and Ktrans (1.314 ±â€¯1.162 min-1vs. 0.391 ±â€¯0.390 min-1; p < 0.001), also significantly higher MaxCon values (0.756 ±â€¯0.959 vs. 0.422 ±â€¯0.341; p < 0.05) and AUC values (2.373 ±â€¯3.012 vs. 1.273 ±â€¯1.000; p < 0.05). The cut-offs of ADC, Ktrans, MaxCon and AUC for discrimating low- and high-Ki-67 groups were 0.920 × 10-3 mm2/s, 0.304 min-1, 0.209 and 1.918, respectively. CONCLUSIONS: ADC, Ktrans, TTP, MaxCon, MaxSlope and AUC are associated with Ki-67 PI. ADC and Ktrans exhibited high performance to discriminate low and high Ki-67 status of CC.

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer.

PURPOSE: 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). CONCLUSION: Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.

Evaluation of Quantitative Dual-Energy Computed Tomography Parameters for Differentiation of Parotid Gland Tumors.

RATIONALE AND OBJECTIVES: To assess the diagnostic performance of quantitative parameters from dual-energy CT (DECT) in differentiating parotid gland tumors (PGTs). MATERIALS AND METHODS: 101 patients with 108 pathologically proved PGTs were enrolled and classified into four groups: pleomorphic adenomas (PAs), warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). Conventional CT attenuation and DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), electron density (Rho), double energy index (DEI), and the slope of the spectral Hounsfield unit curve (λHU), were obtained and compared between benign tumors (BTs) and MTs, and further compared among the four subgroups. Logistic regression analysis was used to assess the independent parameters and the receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance. RESULTS: Attenuation, Zeff, DEI, IC, NIC, and λHU in the arterial phase (AP) and venous phase (VP) were higher in MTs than in BTs (p < 0.001-0.047). λHU in VP and Zeff in AP were independent predictors with an area under the curve (AUC) of 0.84 after the combination. Furthermore, attenuation, Zeff, DEI, IC, NIC, and λHU in the AP and VP of MTs were higher than those of PAs (p < 0.001-0.047). Zeff and NIC in AP and λHU in VP were independent predictors with an AUC of 0.93 after the combination. Attenuation and Rho in the precontrast phase; attenuation, Rho, Zeff, DEI, IC, NIC, and λHU in AP; and the Rho in the VP of PAs were lower than those of WTs (p < 0.001-0.03). Rho in the precontrast phase and attenuation in AP were independent predictors with an AUC of 0.89 after the combination. MTs demonstrated higher Zeff, DEI, IC, NIC, and λHU in VP and lower Rho in the precontrast phase compared with WTs (p < 0.001-0.04); but no independent predictors were found. CONCLUSION: DECT quantitative parameters can help to differentiate PGTs.