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

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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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Availability of data and material

Anonymized data is available upon request. Please contact the corresponding author.

Code availability

Available upon request. Please contact the corresponding author.

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Author information

Authors and Affiliations

  1. Department of Diagnostic & Interventional Imaging, KK Womens’ and Childrens’ Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore

    Eelin Tan, Poh Hwa Tan & Phua Hwee Tang

  2. Department of Pathology and Laboratory Medicine, KK Womens’ and Childrens’ Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore

    Khurshid Merchant

  3. Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore

    Bhanu Prakash KN & Arvind CS

  4. Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore, 117597, Singapore

    Joseph J. Zhao

  5. Center for Quantitative Medicine, Duke-NUS Graduate Medical School, 8 College Rd, Singapore, 169857, Singapore

    Seyed Ehsan Saffari

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Contributions

Conception and design of study: ET, PH. Acquisition of data: ET, PH, KM, PO. Analysis and/or interpretation of data: ET, JZ, SE, BP, AR. Drafting the manuscript: ET, BP. Revising the manuscript: ET, BP, KM, JZ, PH. All authors have reviewed and approved the final article.

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Correspondence to Eelin Tan.

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This study is approved by SingHealth centralized institutional review board (reference number: 2015/2608).

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Tan, E., Merchant, K., KN, B.P. et al. CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma. Childs Nerv Syst 38, 1487–1495 (2022). https://doi.org/10.1007/s00381-022-05534-3

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  • DOI: https://doi.org/10.1007/s00381-022-05534-3

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