Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning

Alcaide‐Leon, Paula; Dufort, Paul; Geraldo, Ana Filipa; Alshafai, Laila; Maralani, Pejman; Spears, Julian; Bharatha, Aditya

doi:10.3174/ajnr.a5173

Cited by 60 publications

(72 citation statements)

References 30 publications

(33 reference statements)

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“…Suh et al 59 reported 90% accuracy of radiomics-based machine learning algorithms compared with visual analysis by 3 readers in differentiating PCNSLs from glioblastomas. Similarly, Alcaide-Leon et al 63 showed superiority of the SVM classifier over human evaluation. Overall, better results were found using CE-MR imaging and machine-classification models.…”

Section: Miscellaneous Applicationsmentioning

confidence: 91%

“…59 Recent MRTA studies, however, have shown promising results in differentiating glioblastomas from PCNSLs and metastases (On-line Table 4). [59][60][61][62][63]66 Kunimatsu et al 60,61 differentiated glioblastomas from PCNSLs with 75% accuracy by selecting the top 4 best-performing texture features from CE-MR images. Xiao et al 62 found skewness and kurtosis to be the best first-order features on CE-MR imaging in a similar population.…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

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Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

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Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging. ABBREVIATIONS:AUC ϭ area under the curve; CE ϭ contrast-enhanced; GLCM ϭ gray-level co-occurrence matrix; GLRLM ϭ gray-level run-length matrix; HGG ϭ high-grade glioma; IDH ϭ isocitrate dehydrogenase; IDM ϭ inverse difference moment; LGG ϭ low-grade glioma; MRTA ϭ MR imaging texture analysis; PCNSLϭ primary central nervous system lymphoma; PCA ϭ principal component analysis; SVM ϭ support vector machine; TA ϭ texture analysis Indicates open access to non-subscribers at www.ajnr.org Indicates article with supplemental on-line tables.

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Section: Miscellaneous Applicationsmentioning

confidence: 91%

Section: Miscellaneous Applicationsmentioning

confidence: 99%

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

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“…37 A total of 4 studies prospectively assessed the ability of radiologists to distinguish between GBM and PCNSL, providing a comparison group for ML. 1,12,33,37 In all 4 of these studies, ML algorithms performed as well as or better than radiologists ( Table 3).…”

Section: Systematic Reviewmentioning

confidence: 88%

“…1). 1,5,12,19,33,[36][37][38] Overall, 6 (75%) of the studies exclusively compared GBM to PCNSL, and 2 of the studies included other tumors as well ( Table 2). All studies only investigated de novo tumors that were imaged using T1-/T2-weighted MRI, FLAIR, and/or diffusion-weighted MRI prior to steroid administration, biopsy, or any other treatment.…”

Section: Systematic Reviewmentioning

confidence: 99%

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis

Nguyen¹,

Blears²,

Ross³

et al. 2018

Neurosurgical Focus

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OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.METHODSThe authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).RESULTSEight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.CONCLUSIONSFew studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.

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“…Radiomics is an emerging field that can extract quantitative parameters from medical images to provide non-visual information calculated with mathematical formulas (6). Previous studies suggested that the combination of radiomics and machine-learning algorithms showed promising potential in differential diagnosis, pre-surgical grading, and prognosis prediction of intracranial tumors (7)(8)(9)(10). However, it has never been applied in the grade prediction of oligodendrogliomas.…”

Section: Introductionmentioning

confidence: 99%

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

et al. 2019

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Objectives: To investigate the ability of radiomics features from MRI in differentiating anaplastic oligodendroglioma (AO) from atypical low-grade oligodendroglioma using machine-learning algorithms. Methods: A total number of 101 qualified patients (50 participants with AO and 51 with atypical low-grade oligodendroglioma) were enrolled in this retrospective, single-center study. Forty radiomics features of tumor images derived from six matrices were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images. Three selection methods were performed to select the optimal features for classifiers, including distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Then three machine-learning classifiers were adopted to generate discriminative models, including linear discriminant analysis, support vector machine, and random forest (RF). Receiver operating characteristic analysis was conducted to evaluate the discriminative performance of each model. Results: Nine predictive models were established based on radiomics features from T1C images and FLAIR images. All of the classifiers represented feasible ability in differentiation, with AUC more than 0.840 when combined with suitable selection method. For models based on T1C images, the combination of LASSO and RF classifier represented the highest AUC of 0.904 in the validation group. For models based on FLAIR images, the combination of GBDT and RF classifier showed the highest AUC of 0.861 in the validation group. Conclusion: Radiomics-based machine-learning approach could potentially serve as a feasible method in distinguishing AO from atypical low-grade oligodendroglioma.

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Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning

Abstract: Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.

Cited by 60 publications

References 30 publications

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

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