Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

Koçak, Burak; Durmaz, Emine Şebnem; Ateş, Ece; Sel, İpek; Gunes, Saime Turgut; Kaya, Ozlem Korkmaz; Zeynalova, Amalya; Kılıçkesmez, Özgür

doi:10.1007/s00330-019-06492-2

Cited by 53 publications

(34 citation statements)

References 29 publications

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“…However, such imaging characteristics may be limited in clinical application due to the unbalanced sensitivity and specificity, and highthroughput quantitative features are intensively needed to better illustrate the radiological divergences and further predict the 1p/19q status non-invasively. Previous radiomics studies using conventional MRI or advanced MRI sequences to predict 1p/19q status reached AUCs ranging from 0.68 to 0.96 (if reported, without distinguishing the training and validation dataset) (18)(19)(20)(21)(22)(23)(24)(25)(26), and our study displayed a competent result, with AUCs around 0.90 for the whole population and further elevated in IDH-mutated tumors, suggesting the capability of our signature for non-invasive 1p/19q detection. In addition, the 3D signature also displayed a balanced sensitivity and specificity, which compensated for the disequilibrium of visual characteristics.…”

Section: Discussionsupporting

confidence: 54%

“…studies of glioma have investigated the association between selected radiomics features and WHO grading, molecular characteristics, clinical manifestations, and patient prognosis (16)(17)(18). A few studies have involved the non-invasive prediction of 1p/19q status through a radiomics approach but display only moderate prediction value (18)(19)(20)(21)(22)(23)(24)(25)(26), and further investigation is needed to establish a reliable radiomics signature. In addition, previous studies were conducted using MR images acquired with diverse spacing (ranging from 1 to 5-6 mm for contrastenhanced T1 [CE-T1]-weighted images), and whether such differences would influence the performance of the prediction model remains to be explored.…”

Section: Introductionmentioning

confidence: 99%

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Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

et al. 2020

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Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed. Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950-1.000. Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.

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Section: Discussionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

et al. 2020

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“…The articles were screened by title and abstract, and 18 remained. Full texts were reviewed, and 14 articles [22][23][24][25][26][27][28][29][30][31][32][33][34][35] fit the review question and inclusion criteria. The publication dates of the 14 included studies 22 -35 ranged from 2017 to 2020.…”

Section: Resultsmentioning

confidence: 99%

“…Two studies did not use ML. 23,24 Most studies assessed WHO grade II and III LGG, [22][23][24][25][26][27][28][29][30][32][33][34] apart from one that assessed only WHO grade II LGG. 31 Table 1 demonstrates the derived aims and key findings of studies that examined the IDH status of LGG, while Table 2 summarizes studies examining the 1p19q status of IDHmut LGG.…”

Section: Resultsmentioning

confidence: 99%

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Bhandari¹,

Liong

Koppen³

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND: Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status. PURPOSE: Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology. DATA SOURCES: Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines. STUDY SELECTION: Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features. DATA ANALYSIS: For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score. DATA SYNTHESIS: The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve ¼ 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve ¼ 0.96, 90% sensitivity, 89% specificity). LIMITATIONS: A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines. CONCLUSIONS: Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake. ABBREVIATIONS: AI ¼ artificial intelligence; AUC ¼ area under the curve; CNN ¼ convolutional neural network; IDH ¼ isocitrate dehydrogenase; IDH-mut ¼ IDH-mutant; LGG ¼ lower-grade gliomas; ML ¼ machine learning; PRISMA-DTA ¼ Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy; QUADAS-2 ¼ Quality Assessment of Diagnostic Accuracy Studies 2; RQS ¼ radiomics quality score; SVM ¼ support vector machine; VASARI ¼ Visually Accessible Rembrandt Images; WHO ¼ World Health Organization

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“…Subsequently, these features could be combined with the results of molecular detection methods to establish prediction models to achieve accurate prediction before surgery [58,59]. Based on a radiomics technique, many predictive models for different molecular biomarkers have been constructed and demonstrated high accuracy [60][61][62][63][64][65]. To perform a customized surgical treatment and achieve an optimal EOR, we developed a preoperative biomarker-predicting system based on radiomics and machine learning technology.…”

Section: Detection and Prediction Of Molecular Biomarkersmentioning

confidence: 99%

Role of molecular biomarkers in glioma resection: a systematic review

Wang

et al. 2020

Chin Neurosurg Jl

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New discoveries based on genetic and epigenetic evidence have significantly expanded the understanding of diffuse gliomas. Molecular biomarkers detected in diffuse gliomas are not only potential targets for radiotherapy, chemotherapy, and immunotherapy, but are also able to guide surgical treatment. Previous studies have suggested that the optimal extent of resection of diffuse gliomas varies according to the expression of specific molecular biomarkers. However, the specific guiding role of these biomarkers in the resection of diffuse gliomas has not been systemically analyzed. This review summarizes several critical molecular biomarkers of tumorigenesis and progression in diffuse gliomas and discusses different strategies of tumor resection in the context of varying genetic expression. With ongoing study and advances in technology, molecular biomarkers will play a more important role in glioma resection and maximize the survival benefit from surgery for diffuse gliomas.

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Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

Cited by 53 publications

References 29 publications

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Role of molecular biomarkers in glioma resection: a systematic review

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