Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI

Hu, Jianping; Zhao, Yijing; Li, Mengcheng; Liu, Jianyi; Wang, Feng; Weng, Qiang; Wang, Xingfu; Cao, Dairong

doi:10.1016/j.ejrad.2020.109251

Cited by 42 publications

(38 citation statements)

References 35 publications

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“…In contrast with previous studies, older age, male gender, comorbidities, bone erosion, absence of calcification, and tumor volume were not independently associated with high-grade meningioma in our study [7,[16][17][18]. These conflicting results are likely due to variations in study design and population between studies.…”

Section: Clinical and Radiological Features Associated With High-grad...contrasting

confidence: 99%

“…Although several recent systematic reviews and meta-analyses have explored possible predictors of the WHO meningioma grade, high-quality evidence is still scarce [2][3][4][5]. Recent advances in novel imaging techniques such as machine learning-based radiomics analysis can improve the accuracy of radiological prediction of meningioma grade [6,7]. However, most imaging markers remain insufficient for routine clinical use because of poor tumor grade prediction accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

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Preoperative Prediction of Intracranial Meningioma Grade Using Conventional CT and MRI

Amano¹,

Nakamizo²,

Murata³

et al. 2022

Cureus

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Preoperative diagnosis of tumor grade can assist in treatment-related decision-making for patients with intracranial meningioma. This study aimed to distinguish between high-grade and low-grade meningiomas using conventional CT and MRI. MethodologyWe retrospectively analyzed 173 consecutive patients with intracranial meningioma (149 low-grade and 24 high-grade tumors) who were treated surgically at the National Hospital Organization Kyushu Medical Center from 2008 to 2020. Clinical and radiological features, including tumor doubling time (Td) and relative growth rate (RGR), were compared between low-grade and high-grade meningiomas. ResultsMultivariate logistic regression analysis showed that symptomatic tumor (p=0.001), non-skull base location (p=0.006), irregular tumor shape (p=0.043), tumor heterogeneity (p=0.025), and peritumoral brain edema (p=0.003) were independent predictors of high-grade meningioma. In 53 patients who underwent surgery because of tumor progression, progression to symptoms (p=0.027), intratumoral heterogeneity (p<0.001), peritumoral brain edema (p=0.001), larger tumor volume (p=0.005), shorter Td (p<0.001), and higher RGR (P<0.001) were significantly associated with high-grade meningioma. Receiver operating characteristics (ROC) curve analysis showed that the optimal Td and annual RGR cut-off values to distinguish high-grade from low-grade meningioma were 460.5 days and 73.2%, respectively (100% sensitivity and 78.6% specificity). ConclusionBased on our findings, conventional CT and MRI are useful methods to predict meningioma grades before surgery. High-grade lesions are associated with non-skull base location, irregular tumor shape, intratumoral heterogeneity, and peritumoral brain edema. High-grade meningioma should be suspected in tumors that exhibit Td <460.5 days or annual RGR >73.2% or those that develop intratumoral heterogeneity or surrounding brain edema on surveillance imaging.

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Section: Clinical and Radiological Features Associated With High-grad...contrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preoperative Prediction of Intracranial Meningioma Grade Using Conventional CT and MRI

Amano¹,

Nakamizo²,

Murata³

et al. 2022

Cureus

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“…Therefore, we conducted DeLong test to all pairs, though the number of possible pairs was significantly larger than most previous studies in which only a few models were considered [ 42 – 49 ]. Similar to those studies in which the chosen models of optimal AUC values were not found significantly different to other models in DeLong test [ 43 – 45 , 49 ], we could still choose and apply the reported model combinations (DD: JMI and BAGC; LNM: MRMR and XGBC) to achieve satisfying performance. However, they should not be treated as the only best models, since they were not significantly different to other combinations, especially not to other combinations of similar AUC values in DeLong test.…”

Section: Discussionmentioning

confidence: 55%

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Tang

Yang

et al. 2021

BMC Cancer

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Background Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Methods A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. Results For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75–1.00), ACC = 0.85 (95% CI 0.69–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.88 (95% CI 0.64–0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94–1.00), ACC = 0.90 (95% CI 0.77–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.94 (95% CI 0.72–0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. Conclusion MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.

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“…A complete list of the excluded articles and the respective reasons for exclusion is provided in Supplementary Table S1 . Thirty three articles were excluded due to predicting only pathological features, e.g., grade (n = 16), or differentiating between tumor entities (n = 8) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Thirty four articles were excluded due to predicting only clinical parameters, e.g., tumor consistency (n = 7), response/treatment outcome (n = 12) or brain/bone invasion (n = 4) [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ...…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI

Cited by 42 publications

References 35 publications

Preoperative Prediction of Intracranial Meningioma Grade Using Conventional CT and MRI

Preoperative Prediction of Intracranial Meningioma Grade Using Conventional CT and MRI

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

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