Pretreatment texture analysis of routine MR images and shape analysis of the diffusion tensor for prediction of volumetric response after radiosurgery for meningioma

Speckter, Herwin; Bido, Jose; Hernandez, Giancarlo; Rivera, Diones; Suazo, Luis; Valenzuela, Santiago; Miches, Isidro; Oviedo, Jairo; Gonzalez, Cesar; Stoeter, P.

doi:10.3171/2018.7.gks181327

Cited by 19 publications

(11 citation statements)

References 22 publications

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“…Tumor Segmentation T2WI and CE T1WI were known to be associated with histopathology and tumor grades in meningiomas (8,25), and the two sequences (slice thickness/spacing, 5 mm/5 mm) were consistently acquired in all subjects. Thus, they were selected for radiomics analysis in this study.…”

Section: Imaging Acquisitionmentioning

confidence: 99%

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas

Zhang

Chen

et al. 2021

Front. Neurol.

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Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas.Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient.Results: Gross total resection (Simpson grades I–III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003).Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.

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Section: Imaging Acquisitionmentioning

confidence: 99%

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas

Zhang

Chen

et al. 2021

Front. Neurol.

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“…In particular, supervised learning, based on labeling of data by an expert, is mainly employed for classification and regression tasks. Among the promises of ML for clinical practice, there are automatic detection and characterization of lesions and the possibility to predict response to therapy and risk of recurrence [11][12][13]. Regarding neuroradiology, it has shown good results in different applications, especially in the field of neuro-oncology [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

et al. 2021

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Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

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“…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 , 61 , 62 , …”

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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Pretreatment texture analysis of routine MR images and shape analysis of the diffusion tensor for prediction of volumetric response after radiosurgery for meningioma

Cited by 19 publications

References 22 publications

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

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

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