TY - JOUR
T1 - Radiogenomics prediction for MYCN amplification in neuroblastoma
T2 - A hypothesis generating study
AU - Di Giannatale, Angela
AU - Di Paolo, Pier Luigi
AU - Curione, Davide
AU - Lenkowicz, Jacopo
AU - Napolitano, Antonio
AU - Secinaro, Aurelio
AU - Tomà, Paolo
AU - Locatelli, Franco
AU - Castellano, Aurora
AU - Boldrini, Luca
N1 - Funding Information:
Angela Di Giannatale is supported by a grant from the Ministero della Salute (GR‐2016‐02364088). The authors acknowledge the support of the Radiomics Working Group of the Italian Alliance Against Cancer ( https://www.alleanzacontroilcancro.it/ ).
Publisher Copyright:
© 2021 Wiley Periodicals LLC
PY - 2021/9
Y1 - 2021/9
N2 - 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. NB lesions were segmented on pretherapy 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, as training dataset, of which 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and two features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p =.0082) and zone size non-uniformity (p =.038). Five-times repeated three-fold cross-validation logistic regression models yielded an area under the curve (AUC) value of 0.879 on the training set. The model was then applied to an independent validation cohort of 21 patients, of which five presented MYCN amplification. The validation of the model yielded a 0.813 AUC value, with 0.85 accuracy on previously unseen data. Conclusions: CT-based radiomics is able to predict MYCN amplification status in NB, paving the way to the in-depth analysis of imaging based biomarkers that could enhance outcomes prediction.
AB - 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. NB lesions were segmented on pretherapy 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, as training dataset, of which 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and two features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p =.0082) and zone size non-uniformity (p =.038). Five-times repeated three-fold cross-validation logistic regression models yielded an area under the curve (AUC) value of 0.879 on the training set. The model was then applied to an independent validation cohort of 21 patients, of which five presented MYCN amplification. The validation of the model yielded a 0.813 AUC value, with 0.85 accuracy on previously unseen data. Conclusions: CT-based radiomics is able to predict MYCN amplification status in NB, paving the way to the in-depth analysis of imaging based biomarkers that could enhance outcomes prediction.
KW - MYCN
KW - neuroblastoma
KW - radiogenomics
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U2 - 10.1002/pbc.29110
DO - 10.1002/pbc.29110
M3 - Article
C2 - 34003574
AN - SCOPUS:85105916809
VL - 68
JO - Pediatric Blood and Cancer
JF - Pediatric Blood and Cancer
SN - 1545-5009
IS - 9
M1 - e29110
ER -