Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters

Abstract

OBJECTIVES: To analyze diffusion-weighted imaging (DWI) findings of meningiomas and to compare them with tumor grade, cell count, and proliferation index and to test a possibility of use of apparent diffusion coefficient (ADC) to differentiate benign from atypical/malignant tumors. METHODS: Forty-nine meningiomas were analyzed. DWI was done using a multislice single-shot echo-planar imaging sequence. A polygonal region of interest was drawn on ADC maps around the margin of the lesion. In all lesions, minimal ADC values (ADCmin) and mean ADC values (ADCmean) were estimated. Normalized ADC (NADC) was calculated in every case as a ratio ADCmean meningioma/ADCmean white matter. All meningiomas were surgically resected and analyzed histopathologically. The tumor proliferation index was estimated on Ki-67 antigen–stained specimens. Cell density was calculated. Collected data were evaluated by means of descriptive statistics. Analyses of ADC/NADC values were performed by means of two-sided t tests. RESULTS: The mean ADCmean value was higher in grade I meningiomas in comparison to grade II/III tumors (0.96 vs 0.80 × 10− 3 mm2s− 1, P = .006). Grade II/III meningiomas showed lower NADC values in comparison to grade I tumors (1.05 vs 1.26, P = .015). There was no significant difference in ADCmin values between grade I and II/III tumors (0.69 vs 0.63 × 10− 3 mm2s− 1, P = .539). The estimated cell count varied from 486 to 2091 (mean value, 1158.20 ± 333.74; median value, 1108). There were no significant differences in cell count between grade I and grade II/III tumors (1163.93 vs 1123.86 cells, P = .77). The mean level of the proliferation index was 4.78 ± 5.08%, the range was 1% to 18%, and the median value was 2%. The proliferation index was statistically significant higher in grade II/III meningiomas in comparison to grade I tumors (15.43% vs 3.00%, P = .001). Ki-67 was negatively associated with ADCmean (r = − 0.61, P < .001) and NADC (r = − 0.60, P < .001). No significant correlations between cell count and ADCmean (r = − 0.20, P = .164) or NADC (r = − 0.25, P = .079) were found.ADCmin correlated statistically significant with cell count (r = − 0.44, P = .002) but not with Ki-67 (r = − 0.22, P = .129). Furthermore, the association between ADCmin and cell count was stronger in grade II/III tumors (r = − 0.79, P = .036) versus grade I meningiomas (r = − 0.41, P = .008). An ADCmean value of less than 0.85 × 10− 3 mm2s− 1 was determined as the threshold in differentiating between grade I and grade II/III meningiomas (sensitivity 72.9%, specificity 73.1%, accuracy 73.0%). The positive and negative predictive values were 33.3% and 96.8%, respectively. The same threshold ADCmean value was used in differentiating between tumors with Ki-67 level ≥ 5% and meningiomas with low proliferation index (Ki-67 < 5%). This threshold yielded a sensitivity of 70.6%, a specificity of 81.2%, and an accuracy of 77.6%. The positive and negative predictive values were 66.6% and 83.9%, respectively. CONCLUSIONS: Grade II/III tumors had lower ADCme...

Correlations between intravoxel incoherent motion (IVIM) parameters and histological findings in rectal cancer: preliminary results

“…It has been shown that ADC correlated well with proliferation index KI 67 in different lesions [5, 7, 16]. Furthermore, as reported previously, ADC also correlated inversely with cell count and nuclear areas [4, 6, 17].…”

“…These relationships were documented in sarcomas, meningiomas, and several epithelial tumors [7, 16, 31]. In rectal cancer, only few studies investigated correlations between DWI and histopathological findings [23, 24, 32, 33].…”

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

“…ADC maps and postcontrast T 1 W images were included in the present study mainly for two reasons: 1) because most of the previous studies trying to determine meningioma grading from MR images were focused on these two imaging sequences; 2) because the combination between semantic and radiomic features on postcontrast T 1 W images has recently shown promising results with an AUC of 0.86 . The possibility to determine the grading of human meningiomas using diffusion‐tensor imaging has been explored by several authors, with differing results. In particular, a study by Surov et al including 389 meningioma‐affected patients reported an ADC value below 0.85 × 10 ‐3 mm 2 s ‐1 having a 73% accuracy (72.9% sensitivity and 73.1% specificity) in the distinction between benign and atypical/malignant meningiomas.…”

“…The possibility to determine the grading of human meningiomas using diffusion‐tensor imaging has been explored by several authors, with differing results. In particular, a study by Surov et al including 389 meningioma‐affected patients reported an ADC value below 0.85 × 10 ‐3 mm 2 s ‐1 having a 73% accuracy (72.9% sensitivity and 73.1% specificity) in the distinction between benign and atypical/malignant meningiomas. By contrast, Santelli et al and Sanverdi et al found no statistically significant differences between the ADC calculated on benign and on atypical/malignant lesions in 177 patients.…”

Metastatic meningioma presenting as a malignant soft tissue tumour

“…Multiple deposits were also observed in other extracranial sites in previous reports of soft tissue metastasis from malignant meningioma [3, 4]. The tumour in the frontal lobe showed several radiological findings that have been described as potential markers of atypical or malignant meningiomas including indistinct or irregular tumour margins, marked peritumoral oedema, inhomogeneous enhancement post-contrast, intrinsic cyst-like areas, disruption of the arachnoid at the brain-tumour interface and adjacent bone destruction [18–24]. …”