Imaging pitfalls in paediatric posterior fossa neoplastic and non-neoplastic lesions

Culleton, Sinéad; McKenna, Barbara J.; Dixon, Luke; Taranath, Ajay; Öztekin, Özgür; Prasad, Chandrajit; Siddiqui, Adeel; Mankad, Kshitij

doi:10.1016/j.crad.2020.12.011

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…Differential diagnosis of brainstem lesions in children is challenging as pediatric brainstem tumors are among the most common lesions [ 9 ]. DIPG or DMG, one the most common brainstem tumors, shows typical radiographic features on MRI of infiltrating the majority of the pons, hypo/iso-intensity on T1-weighted imaging, hyperintensity on T2-weighted imaging, and, frequently, ventral involvement of the pons with encasement of the basilar artery.…”

Section: Discussionmentioning

confidence: 99%

Spontaneous Regression of Glioma–Mimicking Brainstem Lesion in a Child: A Case Report

Kang,

Park,

Hyun

et al. 2024

Brain Tumor Res Treat

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Differential diagnosis of focal brainstem lesions detected on MRI is challenging, especially in young children. Formerly, brainstem gliomas were classified mainly based on MRI features and location. However, since 2016, the World Health Organization’s brainstem lesion classification requires tissue biopsy to reveal molecular characteristics. Although modern techniques of stereotactic or navigation-guided biopsy ensure accurate biopsy of the lesion with safety, biopsy of brainstem lesions is still generally not performed. Here, we report a focal brainstem lesion mimicking brainstem glioma in a 9-year-old girl. Initial MRI, MR spectroscopy, and 11 C-methionine positron emission tomography (PET) features suggested low-grade glioma or diffuse intrinsic pontine glioma. However, repeated MR spectroscopy, perfusion MRI, and 18 fluorodeoxyglucose PET findings suggested that it was more likely a non-tumorous lesion. As the patient presented not with a neurological manifestation but with precocious puberty, the attending oncologist chose to observe with regular follow-up MRI. The pontine lesion with high signal intensity on T2-weighted MRI regressed from the 6-month follow-up and became invisible on the 1.5-year follow-up MRI. We reviewed brainstem glioma–mimicking lesions in the literature and discussed the key points of differential diagnosis.

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Section: Discussionmentioning

confidence: 99%

Spontaneous Regression of Glioma–Mimicking Brainstem Lesion in a Child: A Case Report

Kang,

Park,

Hyun

et al. 2024

Brain Tumor Res Treat

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“…Additionally, the image quality of iUS2 was affected by surgery, resulting in generally poorer images than those acquired before tumor debulking. As described by multiple studies [12,19,24,25], acoustic enhancing artifacts (AEA), contusion, and edema are hyperechoic and could be mistaken for residual tumor, which could have led to suboptimal tumor segmentations in the iUS2 images. Clinically, this does not necessarily lead to additional tumor resection, but rather a direction of the surgeon's attention to the suspected area.…”

Section: Discussionmentioning

confidence: 99%

Navigated intraoperative ultrasound in pediatric brain tumors

Klein Gunnewiek,

van Baarsen,

Graus

et al. 2024

Childs Nerv Syst

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Purpose The aim of this study was to evaluate the diagnostic value and accuracy of navigated intraoperative ultrasound (iUS) in pediatric oncological neurosurgery as compared to intraoperative magnetic resonance imaging (iMRI). Methods A total of 24 pediatric patients undergoing tumor debulking surgery with iUS, iMRI, and neuronavigation were included in this study. Prospective acquisition of iUS images was done at two time points during the surgical procedure: (1) before resection for tumor visualization and (2) after resection for residual tumor assessment. Dice similarity coefficients (DSC), Hausdorff distances 95th percentiles (HD95) and volume differences, sensitivity, and specificity were calculated for iUS segmentations as compared to iMRI. Results A high correlation (R = 0.99) was found for volume estimation as measured on iUS and iMRI before resection. A good spatial accuracy was demonstrated with a median DSC of 0.72 (IQR 0.14) and a median HD95 percentile of 4.98 mm (IQR 2.22 mm). The assessment after resection demonstrated a sensitivity of 100% and a specificity of 84.6% for residual tumor detection with navigated iUS. A moderate accuracy was observed with a median DSC of 0.58 (IQR 0.27) and a median HD95 of 5.84 mm (IQR 4.04 mm) for residual tumor volumes. Conclusion We found that iUS measurements of tumor volume before resection correlate well with those obtained from preoperative MRI. The accuracy of residual tumor detection was reliable as compared to iMRI, indicating the suitability of iUS for directing the surgeon’s attention to areas suspect for residual tumor. Therefore, iUS is considered as a valuable addition to the neurosurgical armamentarium. Trial registration number and date PMCLAB2023.476, February 12th 2024.

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“…While conventional magnetic resonance imaging (MRI) can be used to evaluate tumor location and impact on surrounding structures, it is of limited diagnostic value. Radiological differentiation between different PFTs is difficult and can be further complicated by tumor mimics such as demyelinating disorders and Alexander disease [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review

Yearley

Blitz

Patel

et al. 2022

Cancers

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Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. Methods: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. Results: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. Conclusions: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake.

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Imaging pitfalls in paediatric posterior fossa neoplastic and non-neoplastic lesions

Cited by 4 publications

References 42 publications

Spontaneous Regression of Glioma–Mimicking Brainstem Lesion in a Child: A Case Report

Spontaneous Regression of Glioma–Mimicking Brainstem Lesion in a Child: A Case Report

Navigated intraoperative ultrasound in pediatric brain tumors

Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review

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