Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

Prediction of neoadjuvant chemotherapy efficacy in breast cancer: integrating multimodal imaging and clinical features

OBJECTIVES

To assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma.

METHODS

We conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy.

RESULTS

NAC responders had higher DCE-MRI-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-MRI-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status (OR, 0.187; 95% CI: 0.038, 0.914; P = 0.038) and tumor margin (OR, 20.643; 95% CI: 2.892, 147.365; P = 0.003) were identified as independent factors influencing the lack of significant efficacy of neoadjuvant chemotherapy (NAC) in breast cancer.

CONCLUSIONS

Combining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights.

CLINICAL TRIAL NUMBER

Not applicable.

Histogram Analysis of Advanced Diffusion-weighted MRI Models for Evaluating the Grade and Proliferative Activity of Meningiomas

RATIONALE AND OBJECTIVES

To explore the value of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) magnetic resonance imaging histogram analysis in evaluating the grade and proliferative activity of meningiomas.

MATERIALS AND METHODS

A total of 134 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumor histogram parameters were extracted from multiple functional maps. Mann-Whitney U test was used to compare the histogram parameters of high- and low-grade meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analysis were used to evaluate the diagnostic efficacy. The correlation between histogram parameters and the Ki-67 index was analyzed. The diffusion model was further validated with an independently validation set (n = 33).

RESULTS

Among single histogram parameters, the variance of NODDI-ISOVF (isotropic volume fraction) showed the highest AUC of 0.829 in grading meningiomas. For the combined models, the DKI model had the best performance in the diagnosis (AUC=0.925). Delong test showed the DKI combined model showed superior diagnostic performance to those of DTI, NODDI and MAP models (P < 0.05 for all). Moreover, moderate to weak correlations were found between various diffusion parameters and the Ki-67 labeling index (rho=0.20-0.45, P < 0.05 for all). In the validation set, the DKI model still showed higher performance (AUC, 0.85) than other diffusion models, thus demonstrating robustness.

CONCLUSIONS

Whole-tumor histogram analyses of DTI, DKI, NODDI, and MAP are useful for evaluating the grade and cellular proliferation of meningiomas. DKI combined model has higher diagnostic accuracy than DTI, NODDI and MAP in meningioma grading.

Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging

OBJECTIVES

To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas.

METHODS

Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed.

RESULTS

The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05).

CONCLUSIONS

Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models.

KEY POINTS

• Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging

PURPOSE

To investigate the potential of histogram analysis based on diffusion kurtosis imaging (DKI) in evaluating renal function and fibrosis associated with chronic kidney disease (CKD).

MATERIALS AND METHODS

Thirty-six CKD patients were enrolled, and DKI was performed in all patients before the renal biopsy. The histogram parameters of diffusivity (D) and kurtosis (K) were obtained using FireVoxel. The histogram parameters between the stable [estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m²] and impaired (eGFR < 60 ml/min/1.73 m²) eGFR group were compared. Besides, patients were classified into mild, moderate, and severe fibrosis group using a semi-quantitative standard. The correlations of histogram parameters with eGFR and fibrosis scores were investigated and the diagnostic performances of histogram parameters in assessing renal dysfunction and fibrosis were analyzed. The added value of combination of most significant parameter with 24 h urinary protein (24 h-UPRO) in evaluating fibrosis was also explored.

RESULTS

Seven D histogram parameters in cortex (mean, median, 10th, 25th, 75th, 90th percentiles and entropy), two D histogram parameters in medulla (75th, 90th percentiles), seven K histogram parameters in cortex (mean, min, median, 10th, 25th, 75th, 90th percentiles) and three K histogram parameters in medulla (mean, median, 25th percentile) were significantly different between the two groups. The D_mean of cortex was the most relevant parameter to eGFR (r = 0.648, P < 0.001) and had the largest area under the curve (AUC) for differentiating the stable from impaired eGFR group [AUC = 0.889; 95% confidence interval (CI) 0.728-0.970]. The K_90th of cortex presented the strongest correlation with fibrosis scores (r = 0.575, P < 0.001) and achieved the largest AUC for distinguishing the mild from moderate to severe fibrosis group (AUC = 0.849, 95% CI 0.706-0.993). Combining the K_90th in cortex with 24 h-UPRO gained statistically higher AUC value (AUC = 0.880, 95% CI 0.763-0.996).

CONCLUSION

Histogram analysis based on DKI is practicable for the noninvasive assessment of renal function and fibrosis in CKD patients.

Grading of Pediatric Intracranial Tumors: Are Intravoxel Incoherent Motion and Diffusional Kurtosis Imaging Superior to Conventional DWI?

BACKGROUND AND PURPOSE

An accurate evaluation of the World Health Organization grade is critical in pediatric intracranial tumors. Our aim was to explore the correlations between parameters derived from conventional DWI, intravoxel incoherent motion, and diffusional kurtosis imaging with histopathologic features to evaluate the accuracy of diffusion parameters for grading of pediatric intracranial tumors.

MATERIALS AND METHODS

Fifty-four pediatric patients with histologically proved intracranial tumors who underwent conventional DWI, intravoxel incoherent motion, and diffusional kurtosis imaging were recruited. The conventional DWI (ADC), intravoxel incoherent motion (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], perfusion fraction [f], diffusional kurtosis imaging [K], and diffusion coefficient [Dk]) parameters in the solid component of tumors were measured. The cellularity, Ki-67, and microvessel density were measured. These parameters were compared between the low- and high-grade pediatric intracranial tumors using the Mann-Whitney U test. Spearman correlations and receiver operating characteristic analysis were performed.

RESULTS

The ADC, D, and Dk values were lower, whereas the K value was higher in high-grade pediatric intracranial tumors than in low-grade tumors (all, P < .001). The K value showed positive correlations (r = 0.674-0.802; all, P < .05), while ADC, D, and Dk showed negative correlations with cellularity and Ki-67 (r = -0.548 to -0.740; all, P < .05). The areas under the curve of ADC_VOI, D_VOI, Dk_VOI, and K_VOI were 0.901, 0.894, 0.863, and 0.885, respectively, for differentiating high- from low-grade pediatric intracranial tumors. The area under the curve difference in grading pediatric intracranial tumors was not significant (all, P > .05).

CONCLUSIONS

Intravoxel incoherent motion- and diffusional kurtosis imaging-derived parameters have similar performance compared with conventional DWI in predicting pediatric intracranial tumor grade. The diffusion metrics may potentially reflect tumor cellularity and Ki-67 in pediatric intracranial tumors.