Differentiating between adult intracranial medulloblastoma and ependymoma using MRI

Deng, Juan; Xue, Caiqiang; Liu, Xianwang; Li, Shenglin; Zhang, Junlin

doi:10.1016/j.crad.2022.11.016

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…An extensive exploration of tumor classifications has been conducted using MRI in the literature. Nevertheless, accurately distinguishing between these tumor types remains an active area of research [ 20 , 39 , 40 , 41 , 42 ]. The differentiation between MB and EP is of the utmost importance, considering the distinct treatment planning required for each, underscoring the significance of their accurate diagnosis in numerous cases.…”

Section: Introductionmentioning

confidence: 99%

Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?

Tanyel

Nadarajan

Duc

et al. 2023

Cancers

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Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.

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