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A Random Forest-based Classifier for MYCN Status Prediction in Neuroblastoma using CT Images.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
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    Summary

    This study used radiomic features from CT scans to predict MYCN amplification in neuroblastoma (NB). The model achieved an AUC of 0.85, aiding early diagnosis and treatment planning for this common childhood cancer.

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    Area of Science:

    • Oncology
    • Radiology
    • Medical Imaging
    • Genomics

    Background:

    • Neuroblastoma (NB) is the most common pediatric extracranial solid tumor.
    • MYCN gene amplification in NB correlates with poor prognosis and occurs in 16% of cases.
    • Current diagnostic and staging methods include CT and MRI, with imaging features offering prognostic value.

    Purpose of the Study:

    • To develop and validate a radiogenomic model for predicting MYCN amplification in neuroblastoma patients using CT-derived radiomic features.
    • To assess the potential of automated radiomic analysis in identifying genotype-based information from medical images.

    Main Methods:

    • Utilized a random forest classification model.
    • Extracted radiomic features from CT slices of 46 neuroblastoma patients.
    • Classified patients based on the presence or absence of MYCN amplification.

    Main Results:

    • The random forest model achieved an Area Under the Curve (AUC) of 0.85 ± 0.13 for predicting MYCN amplification.
    • Radiomic features extracted from CT scans demonstrated significant potential in classifying MYCN amplification status.

    Conclusions:

    • Radiogenomic approaches, specifically using radiomic features from CT scans, can effectively predict MYCN amplification in neuroblastoma.
    • This automated method offers a faster, earlier, and repeatable analysis, potentially aiding clinicians in diagnosis and treatment planning.