Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis

Surov, Alexey; Ginat, Daniel Thomas; Sanverdi, Eser; Lim, C. C. Tchoyoson; Hakyemez, Bahattin; Yogi, Akira; Cabada, T.; Wienke, Andreas

doi:10.1016/j.wneu.2015.10.049

Cited by 68 publications

(58 citation statements)

References 23 publications

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“…These results are in accordance with previous studies . In several recent studies, significant negative correlations were also observed between ADC and the expression of Ki‐67 in PCa ( r = –0.459), breast cancer ( r = –0.825), meningioma ( r = –0.63), rectal cancer ( r = –0.318) and bladder cancer ( r = –0.57) . In this study, Ki‐67 SI was correlated inversely with ADC, and ADC showed a significant difference between low and high expression levels of Ki‐67 in PCa, which is similar to the results of Mesko et al…”

Section: Discussionsupporting

confidence: 92%

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki‐67, HIF‐1α and VEGF

Yang

Jing

et al. 2018

NMR in Biomedicine

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Prostate cancer (PCa) is the second most common cancer in men. The Gleason score (GS) and biomarkers play important roles in the diagnosis and treatment of patients with PCa. The purpose of this study was to investigate the relationship between the apparent diffusion coefficient (ADC) and the molecular markers Ki-67, hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) in PCa. Thirty-nine patients with 39 lesions, who had been diagnosed with PCa, were enrolled in this study. All patients underwent diffusion-weighted magnetic resonance imaging (DW-MRI) (b = 800 s/mm ). The expression of Ki-67, HIF-1α and VEGF was assessed by immunohistochemistry. Statistical analysis was applied to analyze the association between ADC and prostate-specific antigen (PSA), GS and the expression of Ki-67, HIF-1α and VEGF. The group differences in ADC among different grades of Ki-67, HIF-1α and VEGF were also analyzed. The mean ± standard deviation of ADC was (0.76 ± 0.27) × 10 mm /s. ADC correlated negatively with PSA and GS (p < 0.05). The Ki-67 staining index (SI), HIF-1α expression and VEGF expression in PCa were correlated inversely with ADC, controlling for age (r = -0.332, p < 0.05; r = -0.662, p < 0.0005; and r = -0.714, p < 0.0005, respectively). ADC showed a significant difference among different grades of Ki-67 (F = 9.164, p = 0.005), HIF-1α (F = 40.333, p < 0.0005) and VEGF (F = 22.048, p < 0.0005). In conclusion, ADC was correlated with PSA, GS, and Ki-67, HIF-1α and VEGF expression in patients with PCa. ADC may be used to evaluate tumor proliferation, hypoxia and angiogenesis in PCa.

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Section: Discussionsupporting

confidence: 92%

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki‐67, HIF‐1α and VEGF

Yang

Jing

et al. 2018

NMR in Biomedicine

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“…There was a significant difference between the ROC curve of the IncV3-ADC model and all the remaining models (P < 0.0001), whereas no statistically significant differences between IncV3-PCT 1 W and Alex-ADC (P = 0.733), between Alex-ADC and Alex-PCT 1 W(P = 0.134), and between Alex-PCT 1 Discussion 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. 21 The possibility to determine the grading of human meningiomas using diffusion-tensor imaging has been explored by several authors, [22][23][24][25][26] with differing results. In particular, a study by Surov et al 22 The use of histogram analysis of diffusion-tensor MRI metrics, including tensor shape measurements, showed promising results in the determination of the grading of human meningiomas.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Banzato

Causin

Puppa

et al. 2019

Magnetic Resonance Imaging

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Background Grading of meningiomas is important in the choice of the most effective treatment for each patient. Purpose To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. Study Type Retrospective. Population In all, 117 meningioma‐affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. Field Strength/Sequence 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2). Assessment WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception‐V3 and AlexNet DCNNs was tested on ADC maps and PCT1W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance. Statistical Test Leave‐one‐out cross‐validation. Results The application of the Inception‐V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88–0.98). Remarkably, only 1/38 WHO Grade II–III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59–0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II–III cases. The discriminating accuracy of both DCNNs on postcontrast T1W images was low, with Inception‐V3 displaying an AUC of 0.68 (95% CI, 0.59–0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45–0.64). Data Conclusion DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1W images. Level of evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152–1159.

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“…▸ appearances, and the lack of capsule-like enhancement at the tumor margins [18]. Diffusion-weighted images most likely show restricted diffusion [5,19,20]. On dynamic susceptibility contrast perfusion MR images, the relative cerebral blood volume increases in the area of peritumoral brain edema with a delay in contrast arrival [21].…”

Section: Atypical and Anaplastic Meningiomasmentioning

confidence: 99%

Variants of meningiomas: a review of imaging findings and clinical features

Kunimatsu

Kamiya

et al. 2016

Jpn J Radiol

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Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis

Cited by 68 publications

References 23 publications

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki‐67, HIF‐1α and VEGF

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki‐67, HIF‐1α and VEGF

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

Variants of meningiomas: a review of imaging findings and clinical features

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