BACKGROUND AND PURPOSE: An accurate evaluation of the World Health Organization grade and cellular proliferation is particularly important in meningiomas. Our aim was to prospectively evaluate and compare diffusional kurtosis imaging, DTI, and DWI metrics in determining the grade and cellular proliferation of meningiomas. MATERIALS AND METHODS: Ninety-six consecutive patients with histopathologically confirmed meningiomas were included in this study. Mean kurtosis, radial kurtosis, axial kurtosis, fractional anisotropy, mean diffusivity, and ADC were semiautomatically obtained in the solid components of tumors. Each normalized diffusion value was compared between high-grade meningiomas and low-grade meningiomas using the Mann-Whitney U test. Receiver operating characteristic, multiple logistic regression, and Pearson correlation analysis were used for statistical evaluations. RESULTS: Diffusional kurtosis imaging metrics (mean kurtosis, radial kurtosis, and axial kurtosis) were significantly higher in high-grade meningiomas than in low-grade meningiomas (P ≤ .001). Mean diffusivity and ADC were significantly lower in high-grade meningiomas than in low-grade meningiomas (P = .003 and .002). Mean kurtosis had significantly greater area the under curve values than mean diffusivity and fractional anisotropy in differentiating high-grade meningiomas from low-grade meningiomas (P = .038 and .002). Mean kurtosis was the only variable that could be used to independently differentiate high-grade meningiomas and low-grade meningiomas (P < .001). Significant correlations were found between the Ki-67 labeling index and kurtosis metrics (P < .001), as well as for mean diffusivity and ADC (P = .004, and .007). CONCLUSIONS: Compared with other diffusion metrics, mean kurtosis may serve as an optimal parameter for evaluating and predicting the meningioma grade. Moreover, diffusion metrics may potentially reflect cellular proliferation.

Nanoparticle-based tumor therapy strategies have been widely developed, while the therapeutic efficacy is often limited due to poor accumulation of nanoparticles in tumor tissues and low antitumor effect of sole therapeutic modality. In this study, we report the construction of tumor-targeting biomimetic sonosensitizer-conjugated iron oxide (Fe₃O₄) nanocatalysts to mediate combinational action of chemodynamic therapy (CDT) and sonodynamic therapy (SDT) for the treatment of colorectal cancer. Bovine serum albumin (BSA)-modified Fe₃O₄ nanoparticles were synthesized using a basic co-precipitation method, and were conjugated with chlorin e6 (Ce6) as the sonosensitizers, followed by surface camouflage of a CT26 cancer cell membrane to construct the tumor-targeting biomimetic nanocatalysts (MBFC). The obtained MBFC nanocatalysts could present a strong catalysis ability and efficient sonodynamic property to generate an abundant amount of reactive oxygen species (ROS) under ultrasound (US) treatment in the tumor microenvironment. Cellular internalization experiments verified the high cellular uptake efficacy of MBFC due to the cell membrane-mediated homologous targeting mechanism. The MBFC nanocatalysts enabled the combinational action of CDT and SDT, and could markedly induce the apoptosis of CT26 cells in vitro and greatly inhibit the growth of CT26 tumors in living mice. This study thus provides a tumor-targeting biomimetic nanoplatform for the effective therapy of tumors.

Background: The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. Methods: Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student’s t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. Results: DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). Conclusion: Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.