Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme

Shrot, Shai; Salhov, Moshe; Dvorski, Nir; Konen, Eli; Averbuch, Amir; Hoffmann, Chen

doi:10.1007/s00234-019-02195-z

Cited by 46 publications

(34 citation statements)

References 32 publications

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“…3 Multiple prior studies aimed to distinguish between glioblastoma and PCNSL by utilising advanced magnetic resonance imaging (MRI) (arterial spin labelling, diffusion tensor imaging (DTI) and perfusion imaging). [3][4][5][6][7] However, these advanced sequences require additional expertise and expense, are timeconsuming and as such are not performed routinely. Thus, conventional MRI sequences are still the mainstay in brain tumour imaging in routine clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

et al. 2021

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Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

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Section: Introductionmentioning

confidence: 99%

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

et al. 2021

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“…54 Similarly, several previous machine learning studies with a similar study purpose used MRI datasets, including intra-axial brain tumors (e.g., glioblastomas and metastases) other than extra-axial meningiomas. [55][56][57] In this regard, however, the real radiological challenge in the differential diagnosis of meningioma would be its distinction from dura-based tumors, such as dural metastases and solitary fibrous tumors/hemangiopericytomas. 58,59 Meningioma grading is another attractive topic.…”

Section: Texture Analysis In Other Brain Tumorsmentioning

confidence: 99%

Texture Analysis in Brain Tumor MR Imaging

Kunimatsu

Yasaka

Akai

et al. 2022

MRMS

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Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram-or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.

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“…A total of 11 studies were identified. The majority of the studies utilized at least contrast-enhanced T1 (CE T1) images for feature extraction [8][9][10][11][12][13][14][15][16], but many also used additional standard sequences such as T1, T2, contrast-enhanced T2, T2 gradient echo, ADC, and FLAIR. Sartoretti et al used amide proton-transfer weighted (APTw) imaging and found a sensitivity of 81.3% and a specificity of 81.1% to distinguish between primary brain tumors and metastases [17].…”

Section: Differentiation Between Brain Metastases and Other Entitiesmentioning

confidence: 99%

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Kalasauskas

Kosterhon

Keric

et al. 2022

Cancers

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The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

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Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme

Cited by 46 publications

References 32 publications

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

Texture Analysis in Brain Tumor MR Imaging

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

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