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23.04.2022 | Original Article

CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma

verfasst von: Eelin Tan, Khurshid Merchant, Bhanu Prakash KN, Arvind CS, Joseph J. Zhao, Seyed Ehsan Saffari, Poh Hwa Tan, Phua Hwee Tang

Erschienen in: Child's Nervous System | Ausgabe 8/2022

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Abstract

Purpose

MYCN onco-gene amplification in neuroblastoma confers patients to the high-risk disease category for which prognosis is poor and more aggressive multimodal treatment is indicated. This retrospective study leverages machine learning techniques to develop a computed tomography (CT)–based model incorporating semantic and non-semantic features for non-invasive prediction of MYCN amplification status in pediatric neuroblastoma.

Methods

From 2009 to 2020, 54 pediatric patients treated for neuroblastoma at a specialized children’s hospital with pre-treatment contrast-enhanced CT and MYCN status were identified (training cohort, n = 44; testing cohort, n = 10). Six morphologic features and 107 quantitative gray-level texture radiomics features extracted from manually drawn volume-of-interest were analyzed. Following feature selection and class balancing, the final predictive model was developed with eXtreme Gradient Boosting (XGBoost) algorithm. Accumulated local effects (ALE) plots were used to explore main effects of the predictive features. Tumor texture maps were also generated for visualization of radiomics features.

Results

One morphologic and 2 radiomics features were selected for model building. The XGBoost model from the training cohort yielded an area under the receiver operating characteristics curve (AUC-ROC) of 0.930 (95% CI, 0.85–1.00), optimized F1-score of 0.878, and Matthews correlation coefficient (MCC) of 0.773. Evaluation on the testing cohort returned AUC-ROC of 0.880 (95% CI, 0.64–1.00), optimized F1-score of 0.933, and MCC of 0.764. ALE plots and texture maps showed higher “GreyLevelNonUniformity” values, lower “Strength” values, and higher number of image-defined risk factors contribute to higher predicted probability of MYCN amplification.

Conclusion

The machine learning model reliably classified MYCN amplification in pediatric neuroblastoma and shows potential as a surrogate imaging biomarker.
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Metadaten
Titel
CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma
verfasst von
Eelin Tan
Khurshid Merchant
Bhanu Prakash KN
Arvind CS
Joseph J. Zhao
Seyed Ehsan Saffari
Poh Hwa Tan
Phua Hwee Tang
Publikationsdatum
23.04.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Child's Nervous System / Ausgabe 8/2022
Print ISSN: 0256-7040
Elektronische ISSN: 1433-0350
DOI
https://doi.org/10.1007/s00381-022-05534-3

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