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Radiogenomics prediction for MYCN amplification in Neuroblastoma: a hypothesis generating study
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  • Angela Di Giannatale,
  • Pierluigi Di Paolo,
  • Davide Curione,
  • Jacopo Lenkowicz,
  • Antonio Napolitano,
  • Aurelio Secinaro,
  • Paolo Toma,
  • Franco Locatelli,
  • Aurora Castellano,
  • Luca Boldrini
Angela Di Giannatale
Bambino Gesu Pediatric Hospital
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Pierluigi Di Paolo
Bambino Gesu Pediatric Hospital
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Davide Curione
Bambino Gesu Pediatric Hospital
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Jacopo Lenkowicz
Policlinico Universitario Agostino Gemelli
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Antonio Napolitano
Bambino Gesu Pediatric Hospital
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Aurelio Secinaro
Bambino Gesu Pediatric Hospital
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Paolo Toma
Bambino Gesu Pediatric Hospital
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Franco Locatelli
Bambino Gesu Pediatric Hospital
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Aurora Castellano
Bambino Gesu Pediatric Hospital
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Luca Boldrini
Policlinico Universitario Agostino Gemelli
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Abstract

Background: MYCN amplification represents a powerful prognostic factor in neuroblastoma (NB) and may occasionally account for intratumoral heterogeneity. Radiomics is an emerging field of advanced image analysis that aims to extract a large number of quantitative features from standard radiological images, providing valuable clinical information Procedure: In this retrospective study, we aimed to create a radiogenomics model by correlating computed tomography (CT) radiomics analysis with MYCN status and overall survival (OS). NB lesions were segmented on pre-therapy CT scans and radiomics features subsequently extracted using a dedicated library. Dimensionality reduction/features selection approaches were then used for features procession and logistic regression models have been developed for the considered outcome. Results: Seventy-eight patients were included in this study, 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and 2 features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p=0.0082) and zone size non-uniformity (p=0.038). Five-times repeated 3-fold cross-validation logistic regression models yielded an Area Under the Curve (AUC) value of 0.879 on the training and 0.865 on the testing set for MYCN. No statistical significant difference has been observed comparing radiomics predicted and actual OS data. Conclusions: CT based radiomics is able to predict MYCN amplification status and OS in NB, paving the way to the in depth analysis of imaging based biomarkers that could enhance outcomes prediction.

Peer review status:ACCEPTED

22 Jan 2021Submission Checks Completed
22 Jan 2021Assigned to Editor
22 Jan 2021Submitted to Pediatric Blood & Cancer
23 Jan 2021Reviewer(s) Assigned
14 Feb 2021Review(s) Completed, Editorial Evaluation Pending
15 Feb 2021Editorial Decision: Revise Major
13 Apr 2021Submission Checks Completed
13 Apr 2021Assigned to Editor
13 Apr 20211st Revision Received
23 Apr 2021Review(s) Completed, Editorial Evaluation Pending
23 Apr 2021Editorial Decision: Accept