Human performance in predicting enhancement quality of gliomas using gadolinium-free MRI sequences.
GBCA
MRI
VASARI
enhancement
gadolinium‐based contrast agent
glioma
Journal
Journal of neuroimaging : official journal of the American Society of Neuroimaging
ISSN: 1552-6569
Titre abrégé: J Neuroimaging
Pays: United States
ID NLM: 9102705
Informations de publication
Date de publication:
19 Sep 2024
19 Sep 2024
Historique:
revised:
13
08
2024
received:
17
06
2024
accepted:
14
08
2024
medline:
20
9
2024
pubmed:
20
9
2024
entrez:
20
9
2024
Statut:
aheadofprint
Résumé
To develop and test a decision tree for predicting contrast enhancement quality and shape using precontrast magnetic resonance imaging (MRI) sequences in a large adult-type diffuse glioma cohort. Preoperative MRI scans (development/optimization/test sets: n = 31/38/303, male = 17/22/189, mean age = 52/59/56.7 years, high-grade glioma = 22/33/249) were retrospectively evaluated, including pre- and postcontrast T1-weighted, T2-weighted, fluid-attenuated inversion recovery, and diffusion-weighted imaging sequences. Enhancement prediction decision tree (EPDT) was developed using development and optimization sets, incorporating four imaging features: necrosis, diffusion restriction, T2 inhomogeneity, and nonenhancing tumor margins. EPDT accuracy was assessed on a test set by three raters of variable experience. True enhancement features (gold standard) were evaluated using pre- and postcontrast T1-weighted images. Statistical analysis used confusion matrices, Cohen's/Fleiss' kappa, and Kendall's W. Significance threshold was p < .05. Raters 1, 2, and 3 achieved overall accuracies of .86 (95% confidence interval [CI]: .81-.90), .89 (95% CI: .85-.92), and .92 (95% CI: .89-.95), respectively, in predicting enhancement quality (marked, mild, or no enhancement). Regarding shape, defined as the thickness of enhancing margin (solid, rim, or no enhancement), accuracies were .84 (95% CI: .79-.88), .88 (95% CI: .84-.92), and .89 (95% CI: .85-.92). Intrarater intergroup agreement comparing predicted and true enhancement features consistently reached substantial levels (≥.68 [95% CI: .61-.75]). Interrater comparison showed at least moderate agreement (group: ≥.42 [95% CI: .36-.48], pairwise: ≥.61 [95% CI: .50-.72]). Among the imaging features in the EPDT, necrosis assessment displayed the highest intra- and interrater consistency (≥.80 [95% CI: .73-.88]). The proposed EPDT has high accuracy in predicting enhancement patterns of gliomas irrespective of rater experience.
Sections du résumé
BACKGROUND AND PURPOSE
OBJECTIVE
To develop and test a decision tree for predicting contrast enhancement quality and shape using precontrast magnetic resonance imaging (MRI) sequences in a large adult-type diffuse glioma cohort.
METHODS
METHODS
Preoperative MRI scans (development/optimization/test sets: n = 31/38/303, male = 17/22/189, mean age = 52/59/56.7 years, high-grade glioma = 22/33/249) were retrospectively evaluated, including pre- and postcontrast T1-weighted, T2-weighted, fluid-attenuated inversion recovery, and diffusion-weighted imaging sequences. Enhancement prediction decision tree (EPDT) was developed using development and optimization sets, incorporating four imaging features: necrosis, diffusion restriction, T2 inhomogeneity, and nonenhancing tumor margins. EPDT accuracy was assessed on a test set by three raters of variable experience. True enhancement features (gold standard) were evaluated using pre- and postcontrast T1-weighted images. Statistical analysis used confusion matrices, Cohen's/Fleiss' kappa, and Kendall's W. Significance threshold was p < .05.
RESULTS
RESULTS
Raters 1, 2, and 3 achieved overall accuracies of .86 (95% confidence interval [CI]: .81-.90), .89 (95% CI: .85-.92), and .92 (95% CI: .89-.95), respectively, in predicting enhancement quality (marked, mild, or no enhancement). Regarding shape, defined as the thickness of enhancing margin (solid, rim, or no enhancement), accuracies were .84 (95% CI: .79-.88), .88 (95% CI: .84-.92), and .89 (95% CI: .85-.92). Intrarater intergroup agreement comparing predicted and true enhancement features consistently reached substantial levels (≥.68 [95% CI: .61-.75]). Interrater comparison showed at least moderate agreement (group: ≥.42 [95% CI: .36-.48], pairwise: ≥.61 [95% CI: .50-.72]). Among the imaging features in the EPDT, necrosis assessment displayed the highest intra- and interrater consistency (≥.80 [95% CI: .73-.88]).
CONCLUSION
CONCLUSIONS
The proposed EPDT has high accuracy in predicting enhancement patterns of gliomas irrespective of rater experience.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Hanarth Foundation
Organisme : National Institute for Health and Care Research Biomedical Research Center at University College London Hospitals
Organisme : European Society of Neuroradiology Research Fellowship Grant
Informations de copyright
© 2024 The Author(s). Journal of Neuroimaging published by Wiley Periodicals LLC on behalf of American Society of Neuroimaging.
Références
Ellingson BM, Bendszus M, Boxerman J, et al. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol. 2015;17:1188–1198.
Weller M, van den Bent M, Preusser M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2021;18:170–186.
Wen PY, van den Bent M, Youssef G, et al. RANO 2.0: update to the response assessment in neuro‐oncology criteria for high‐ and low‐grade gliomas in adults. J Clin Oncol. 2023;41:5187–5199.
Wang Y, Wang K, Wang J, et al. Identifying the association between contrast enhancement pattern, surgical resection, and prognosis in anaplastic glioma patients. Neuroradiology. 2016;58:367–374.
Castet F, Alanya E, Vidal N, et al. Contrast‐enhancement in supratentorial low‐grade gliomas: a classic prognostic factor in the molecular age. J Neurooncol. 2019;143:515–523.
Verburg N, de Witt Hamer PC. State‐of‐the‐art imaging for glioma surgery. Neurosurg Rev. 2021;44:1331–1343.
Certo F, Altieri R, Maione M, et al. FLAIRectomy in supramarginal resection of glioblastoma correlates with clinical outcome and survival analysis: a prospective, single institution, case series. Oper Neurosurg. 2021;20:151–163.
Haddad AF, Young JS, Morshed RA, et al. FLAIRectomy: resecting beyond the contrast margin for glioblastoma. Brain Sci. 2022;12:544.
Fraum TJ, Ludwig DR, Bashir MR, et al. Gadolinium‐based contrast agents: a comprehensive risk assessment. J Magn Reson Imaging. 2017;46:338–353.
Blomqvist L, Nordberg GF, Nurchi VM, et al. Gadolinium in medical imaging‐usefulness, toxic reactions and possible countermeasures‐a review. Biomolecules. 2022;12:742.
Blumfield E, Moore MM, Drake MK, et al. Survey of gadolinium‐based contrast agent utilization among the members of the society for pediatric radiology: a quality and safety committee report. Pediatr Radiol. 2017;47:665–673.
Proença F, Guerreiro C, Sá G, et al. Neuroimaging safety during pregnancy and lactation: a review. Neuroradiology. 2021;63:837–845.
Crowson MG, Rocke DJ, Hoang JK, et al. Cost‐effectiveness analysis of a non‐contrast screening MRI protocol for vestibular schwannoma in patients with asymmetric sensorineural hearing loss. Neuroradiology. 2017;59:727–736.
Raizer JJ, Fitzner KA, Jacobs DI, et al. Economics of malignant gliomas: a critical review. J Oncol Pract. 2015;11:e59–65.
Ogbole GI, Adeyomoye AO, Badu‐Peprah A, et al. Survey of magnetic resonance imaging availability in West Africa. Pan Afr Med J. 2018;30:240.
Anazodo UC, Ng JJ, Ehiogu B, et al. A framework for advancing sustainable magnetic resonance imaging access in Africa. NMR Biomed. 2023;36:e4846.
Pasquini L, Napolitano A, Pignatelli M, et al. Synthetic post‐contrast imaging through artificial intelligence: clinical applications of virtual and augmented contrast media. Pharmaceutics. 2022;14:2378.
The Cancer Genome Atlas (TCGA) Phenotype Research Group. VASARI research project. In: Cancer Imaging Archive. Available from: https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Accessed 19 Dec 2023.
Sim J, Wright CC. The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Ther. 2005;85:257–268.
Vanbelle S. A new interpretation of the weighted kappa coefficients. Psychometrika. 2016;81:399–410.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–174.
Vanbelle S. Comparing dependent kappa coefficients obtained on multilevel data. Biom J. 2017;59:1016–1034.
He H, Guo E, Meng W, et al. Predicting cerebral glioma enhancement pattern using a machine learning‐based magnetic resonance imaging radiomics model. Nan Fang Yi Ke Da Xue Xue Bao. 2024;44:194–200.
Calabrese E, Rudie JD, Rauschecker AM, et al. Feasibility of simulated postcontrast MRI of glioblastomas and lower‐grade gliomas by using three‐dimensional fully convolutional neural networks. Radiol Artif Intell. 2021;3:e200276.
Kleesiek J, Morshuis JN, Isensee F, et al. Can virtual contrast enhancement in brain MRI replace gadolinium?: a feasibility study. Invest Radiol. 2019;54:653–660.
Wang Y, Wu W, Yang Y, et al. Deep learning‐based 3D MRI contrast‐enhanced synthesis from a 2D noncontrast T2Flair sequence. Med Phys. 2022;49:4478–4493.
Parillo M, Mallio CA, Pileri M, et al. Interrater reliability of brain tumor reporting and data system (BT‐RADS) in the follow up of adult primary brain tumors: a single institution experience in Italy. Quant Imaging Med Surg. 2023;13:7423–7431.
Nam YK, Park JE, Park SY, et al. Reproducible imaging‐based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system. Eur Radiol. 2021;31:7374–7385.
Bellini D, Panvini N, Rengo M, et al. Diagnostic accuracy and interobserver variability of CO‐RADS in patients with suspected coronavirus disease‐2019: a multireader validation study. Eur Radiol. 2021;31:1932–1940.
Park M, Lee S‐K, Chang JH, et al. Elderly patients with newly diagnosed glioblastoma: can preoperative imaging descriptors improve the predictive power of a survival model? J Neurooncol. 2017;134:423–431.
Su C‐Q, Lu S‐S, Han Q‐Y, et al. Intergrating conventional MRI, texture analysis of dynamic contrast‐enhanced MRI, and susceptibility weighted imaging for glioma grading. Acta Radiol. 2019;60:777–787.
Zhou H, Vallières M, Bai HX, et al. MRI features predict survival and molecular markers in diffuse lower‐grade gliomas. Neuro Oncol. 2017;19:862–870.
Park YW, Han K, Ahn SS, et al. Prediction of IDH1‐mutation and 1p/19q‐codeletion status using preoperative MR imaging phenotypes in lower grade gliomas. AJNR Am J Neuroradiol. 2018;39:37–42.
Hyare H, Rice L, Thust S, et al. Modelling MR and clinical features in grade II/III astrocytomas to predict IDH mutation status. Eur J Radiol. 2019;114:120–127.
Park CJ, Han K, Shin H, et al. MR image phenotypes may add prognostic value to clinical features in IDH wild‐type lower‐grade gliomas. Eur Radiol. 2020;30:3035–3045.