Quantitative texture analysis in the prediction of IDH status in low-grade gliomas

Jakola, Asgeir Store; Zhang, Yihua; Skjulsvik, Anne Jarstein; Solheim, Ole; Bø, Hans Kristian; Berntsen, Erik Magnus; Reinertsen, Ingerid; Gulati, Sasha; Förander, Petter; Brismar, Torkel B.

doi:10.1016/j.clineuro.2017.12.007

Cited by 52 publications

(33 citation statements)

References 31 publications

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“…While multiple prior research studies have investigated the use of a texture analysis applied to MR images the underlying influence of MRI scan parameters on texture analysis features are not entirely understood. Furthermore, despite the increasing use of texture analysis in the field of radiology, a fundamental understanding of the histopathologic and biologic correlation between tissue and texture analysis features remains in its infancy.…”

Section: Discussionmentioning

confidence: 99%

“…The use of a texture analysis applied to imaging studies including CT and MRI have been previously performed for the evaluation of multiple nonneoplastic disorders including the evaluation for mesial temporal sclerosis on MRI, evaluation of intervertebral disc disease on MRI, evaluation of hepatic fibrosis on both CT and MRI, evaluation of subchondral bone on MRI . Prior oncologic studies have also employed texture analyses to evaluate specific tumor features including the assessment of HPV status of oropharyngeal squamous cell carcinomas, prognosis of head and neck neoplasms, classification of gastric and colorectal tumors on CT, genomic mapping and predictive marker identification of gliomas on MRI, the identification of potentially prognostic predictors in lung cancer, evaluation of genitourinary neoplasms on both CT and MRI, and for the radiomic classifications of breast carcinoma subtypes …”

Section: Introductionmentioning

confidence: 99%

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Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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ObjectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.MethodsPublicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).ResultsSignificant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).ConclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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“…50 Bahrami et al 51 reported greater FLAIR tissue heterogeneity and lower edge contrast in IDH wild-type compared with IDH mutants. Jakola et al 53 also reported greater accuracy for predicting IDH mutation using 3D-FLAIR. IDH-mutant 1p/19q-codeleted gliomas also have shown similar results compared with an 1p/19q-intact group and an unmethylated group.…”

Section: Glioma Radiogenomicsmentioning

confidence: 95%

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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“…Some studies have used texture analysis (24) and deep learning (25) (26) to correlate the genetic marker with radiological images, but these methods can't be applied universally. The institutions hoping to use these methods need to validate them in their own dataset, leading us back to our problem of inaccessibility to the stereotactic biopsy, genetic facilities etc.…”

Section: Discussionmentioning

confidence: 99%

New Radiological Classification of Glioma and validation with the survival analysis

Kamble

Agrawal

Koundal

et al. 2020

Preprint

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Radiology based classification of glioma independent of histological or genetic markers predicting survival of patients is an unmet need. Until now radiology is chasing these markers rather than focussing directly on the clinical outcome. Our study is first of its kind to come up with the independent new radiological classification of gliomas encompassing both low-and high-grade gliomas under single classification system. TCGA-LGG and REMBRANDT public domain dataset of glioma were analyzed as training and testing dataset respectively. Based on MRI images, gliomas were classified into six types in detailed classification & three types in simplified classification system. Survival analysis using Kaplan Meier and Cox regression was done. Secondary objective was to evaluate the sensitivity and specificity of novel signs with existing histological and genetic markers. The study predicted survival in both training and testing dataset independent of genetic or histological information. Novel signs, "Ball on Christmas tree" sign(highly specific), Type-4 lineage sign(highly sensitive) identifies IDH-wild and high-grade gliomas (grade-III and IV) while Type-2 lineage sign showed good specificity in identifying 1p19q non co-deleted IDH-mutated, ATRX del/mutated, Grade-II gliomas. There is a substantial interobserver agreement for the classification and novel signs. New radiological classification of glioma predicts the survival of patients independent of genetic or histological information. This can act as a scaffolding to formulate and streamline the treatment guidelines for glioma patients. This classification has potential of improving the quality of care of glioma patients by predicting the survival without the need of invasive biopsy.

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Quantitative texture analysis in the prediction of IDH status in low-grade gliomas

Abstract: In this preliminary study using Haralick texture parameters based on preoperative clinical FLAIR sequence, the homogeneity parameter could separate IDH mutated LGG from IDH wild type LGG. Combined with tumor volume, these diagnostic properties seem promising.

Cited by 52 publications

References 31 publications

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Texture Analysis in Cerebral Gliomas: A Review of the Literature

New Radiological Classification of Glioma and validation with the survival analysis

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