A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

Abstract The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen’s Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms

Assessing preoperative risk of STR in skull meningiomas using MR radiomics and machine learning

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Protoporphyrin IX (PpIX) Fluorescence during Meningioma Surgery: Correlations with Histological Findings and Expression of Heme Pathway Molecules

Background: The usefulness of 5-ALA-mediated fluorescence-guided resection (FGR) in meningiomas is controversial, and information on the molecular background of fluorescence is sparse. Methods: Specimens obtained during 44 FGRs of intracranial meningiomas were analyzed for the presence of tumor tissue and fluorescence. Protein/mRNA expression of key transmembrane transporters/enzymes involved in PpIX metabolism (ABCB6, ABCG2, FECH, CPOX) were investigated using immunohistochemistry/qPCR. Results: Intraoperative fluorescence was observed in 70 of 111 specimens (63%). No correlation was found between fluorescence and the WHO grade (p = 0.403). FGR enabled the identification of neoplastic tissue (sensitivity 84%, specificity 67%, positive and negative predictive value of 86% and 63%, respectively, AUC: 0.75, p p p = 0.351). Protein expression of ABCB6, ABCG2, FECH and CPOX was found in meningioma tissue and was correlated with fluorescence (p < 0.05, each), whereas this was not confirmed for mRNA expression. Aberrant expression was observed in the CNS. Conclusion: FGR enables the intraoperative identification of meningioma tissue with limitations concerning dura invasion and due to ectopic expression in the CNS. ABCB6, ABCG2, FECH and CPOX are expressed in meningioma tissue and are related to fluorescence

Diagnostic Accuracy and Reliability of Noncontrast Computed Tomography Markers for Acute Hematoma Expansion among Radiologists

Background: Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience. Methods: Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen&rsquo;s kappa (&kappa;) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater &kappa; statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters. Results: Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen&rsquo;s kappa from 0.70 (95% CI 0.65&ndash;0.81) to 0.96 (95% CI 0.94&ndash;0.98). The interrater Cohen&rsquo;s kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52&ndash;0.65) to 94 (95% CI 92&ndash;0.97). The intrarater gold-standard Cohen&rsquo;s kappa was almost perfect and ranged from 0.79 (95% CI 0.78&ndash;0.90) to 0.98 (95% CI 0.78&ndash;0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57&ndash;0.66) to 0.93 (95%CI 0.89&ndash;0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p-values &lt; 0.05. Conclusion: The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction

Radiomics for pseudoprogression prediction in high grade gliomas:added value of MR contrast agent

OBJECTIVE: Our aim is to define the capabilities of radiomics in predicting pseudoprogression from pre-treatment MR images in patients diagnosed with high-grade gliomas using T1 non-contrast-enhanced and contrast-enhanced images. MATERIAL & METHODS: In this retrospective IRB-approved study, image segmentation of high-grade gliomas was semi-automatically performed using 3D Slicer. Non-contrast-enhanced T1-weighted images and contrast-enhanced T1-weighted images were used prior to surgical therapy or radio-chemotherapy. Imaging data was split into a training sample and an independent test sample at random. We extracted 107 radiomic features by use of PyRadiomics. Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM). RESULTS: Our cohort included 124 patients (female: n = 53), diagnosed with progressive (n = 61) and pseudoprogressive disease (n = 63) of primary high-grade gliomas. Based on non-contrast-enhanced T1-weighted images of the independent test sample, the mean area under the curve (AUC), mean sensitivity, mean specificity and mean accuracy of our model were 0.651 [0.576, 0.761], 0.616 [0.417, 0.833], 0.578 [0.417, 0.750] and 0.597 [0.500, 0.708] to predict the development of pseudoprogression. In comparison, the independent test data of contrast-enhanced T1-weighted images yielded significantly higher values of AUC = 0.819 [0.760, 0.872], sensitivity = 0.817 [0.750, 0.833], specificity = 0.723 [0.583, 0.833] and accuracy = 0.770 [0.687, 0.833]. CONCLUSION: Our findings show that it is possible to predict pseudoprogression of high-grade gliomas with a Radiomics model using contrast-enhanced T1-weighted images with comparatively good discriminatory power. The use of a contrast agent results in a clear added value

Pseudoprogression prediction in high grade primary CNS tumors by use of radiomics

Our aim is to define the capabilities of radiomics and machine learning in predicting pseudoprogression development from pre-treatment MR images in a patient cohort diagnosed with high grade gliomas. In this retrospective analysis, we analysed 131 patients with high grade gliomas. Segmentation of the contrast enhancing parts of the tumor before administration of radio-chemotherapy was semi-automatically performed using the 3D Slicer open-source software platform (version 4.10) on T1 post contrast MR images. Imaging data was split into training data, test data and an independent validation sample at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest (ROI). Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM). 131 patients were included, of which 64 patients had a histopathologically proven progressive disease and 67 were diagnosed with mixed or pure pseudoprogression after initial treatment. Our Radiomics approach is able to predict the occurrence of pseudoprogression with an AUC, mean sensitivity, mean specificity and mean accuracy of 91.49% [86.27%, 95.89%], 79.92% [73.08%, 87.55%], 88.61% [85.19%, 94.44%] and 84.35% [80.19%, 90.57%] in the full development group, 78.51% [75.27%, 82.46%], 66.26% [57.95%, 73.02%], 78.31% [70.48%, 84.19%] and 72.40% [68.06%, 76.85%] in the testing group and finally 72.87% [70.18%, 76.28%], 71.75% [62.29%, 75.00%], 80.00% [69.23%, 84.62%] and 76.04% [69.90%, 80.00%] in the independent validation sample, respectively. Our results indicate that radiomics is a promising tool to predict pseudo-progression, thus potentially allowing to reduce the use of biopsies and invasive histopathology