Diagnostic accuracy and added value of qualitative radiological review of 1H-magnetic resonance spectroscopy in evaluation of childhood brain tumors

Manias, Karen; Gill, Sharon A.; MacPherson, Lesley; Oates, Adam; Pinkey, Benjamin; Davies, Paul; Zarinabad, Niloufar; Davies, Nigel; Babourina-Brooks, Ben; Wilson, Martin; Peet, Andrew

doi:10.1093/nop/npz010

Cited by 7 publications

(16 citation statements)

References 21 publications

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“…Simply viewing MRS processed by the MR scanner software increased the radiologists' diagnostic accuracy and certainty of the correct diagnosis when added to conventional imaging. This reflects other paediatric studies in which adding MRS to conventional MRI significantly improved radiologists' diagnostic accuracy of brain tumours 23,24 and indicates that MRS can be a useful adjunct to MRI without sophisticated decision support software. The DSS gave both a comparison of the MRS with mean MRS from the three types of tumour and the results of a pattern recognition-based classifier.…”

Section: Discussionsupporting

confidence: 76%

“…Studies adding visual interpretation of MRS profiles to conventional MRI have been found to significantly improve radiologists' diagnostic accuracy of paediatric brain tumours. [23][24][25] Interpretation of MRS in addition to MRI, by a radiologist, was found to provide clinical benefit in selective cases. There is also some evidence that qualitative interpretation of MRS improves the accuracy of noninvasive diagnosis in the clinical environment.…”

Section: Introductionmentioning

confidence: 99%

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Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

et al. 2021

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H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system.

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Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

et al. 2021

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“…The commonly used PRESS localisation suffers from chemical shift artefacts, which become more apparent at higher field strength, and a recent consensus document has advised moving to a semiLASER localisation sequence [43]. MRS data is best analysed quantitatively using software methods which can fit the spectra to obtain metabolite concentrations, but it has also been shown that visual interpretation aids diagnostic accuracy when added to conventional MRI [44]. Spectroscopic imaging may be more appropriate than single-voxel spectroscopy for large diffuse tumours and may aid the identification of most aggressive regions, but implementation of the technique requires experience and is not part of the routine protocol [45].…”

Section: Multi-modal Advanced Mrimentioning

confidence: 99%

European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours

et al. 2021

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Introduction Standardisation of imaging acquisition is essential in facilitating multicentre studies related to childhood CNS tumours. It is important to ensure that the imaging protocol can be adopted by centres with varying imaging capabilities without compromising image quality. Materials and method An imaging protocol has been developed by the Brain Tumour Imaging Working Group of the European Society for Paediatric Oncology (SIOPE) based on consensus among its members, which consists of neuroradiologists, imaging scientists and paediatric neuro-oncologists. This protocol has been developed to facilitate SIOPE led studies and regularly reviewed by the imaging working group. Results The protocol consists of essential MRI sequences with imaging parameters for 1.5 and 3 Tesla MRI scanners and a set of optional sequences that can be used in appropriate clinical settings. The protocol also provides guidelines for early post-operative imaging and surveillance imaging. The complementary use of multimodal advanced MRI including diffusion tensor imaging (DTI), MR spectroscopy and perfusion imaging is encouraged, and optional guidance is provided in this publication. Conclusion The SIOPE brain tumour imaging protocol will enable consistent imaging across multiple centres involved in paediatric CNS tumour studies.

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“…As a point of reference, earlier studies have documented human accuracy of pediatric brain tumor classification as approximately between 70% and 80% by pediatric neuroradiologists with Certificates of Added Qualification. 35,36…”

Section: Discussionmentioning

confidence: 99%

Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging

Zhou

Hu²,

Tang

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging. MATERIALS AND METHODS:This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (n ¼ 111), ependymoma (n ¼ 70), and pilocytic astrocytoma (n ¼ 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method x 2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P , .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review. ABBREVIATIONS: AUC ¼ area under the curve; AutoML ¼ automatic machine learning; CHSQ ¼ x 2 score; EP ¼ ependymoma; MB ¼ medulloblastoma; ML ¼ machine learning; PA ¼ pilocytic astrocytoma; TPOT ¼ Tree-Based Pipeline Optimization Tool A mong childhood malignancies, pediatric brain tumors are the second most common and the leading cause of death from solid tumors. 1,2 Posterior fossa tumors make up a disproportionate portion of brain tumors in the pediatric population, accounting for 54%-70% of tumors compared with ,20% in the adult population. 3 The most common subtypes of posterior fossa tumors among children are medulloblastoma (MB), pilocytic astrocytoma (PA), and ependymoma (EP). 4,5 Discrimination of these 3 malignancies is important due to the differing natural

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Diagnostic accuracy and added value of qualitative radiological review of 1H-magnetic resonance spectroscopy in evaluation of childhood brain tumors

Cited by 7 publications

References 21 publications

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours

Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging

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